Publications by authors named "Kishore Rajendran"

30 Publications

  • Page 1 of 1

Simultaneous dual-contrast imaging using energy-integrating-detector multi-energy CT: An in vivo feasibility study.

Med Phys 2022 Jan 11. Epub 2022 Jan 11.

Department of Radiology, Mayo Clinic, Rochester, MN, 55905, US.

Purpose: To demonstrate the feasibility of simultaneous dual-contrast imaging in a large animal using a newly developed dual-source energy-integrating-detector (EID) based multi-energy computed tomography (MECT) system.

Methods: Two imaging tasks that may have potential clinical applications were investigated: head/neck (HN) CT angiography (CTA)/CT venography (CTV) with iodine and gadolinium, and small bowel imaging with iodine and bismuth in domestic swine. Dual-source x-ray beam configurations of 70 kV+Au120/Sn120 kV and 70 kV+Au140/Sn140 kV were used for the HN-CTA/CTV and small bowel imaging studies, respectively. A test bolus scan was performed for each study. The ROIs in the carotid artery and jugular vein for HN-CTA/CTV imaging and abdominal aorta for small bowel imaging were used to determine the time-attenuation curves, based on which the timing for contrast injection and the CT scan was determined. In the HN-CTA/CTV study, a MECT scan was performed at the time point corresponding to the optimal arterial enhancement by iodine and the optimal venous enhancement by gadolinium. In the small bowel imaging study, A MECT scan was performed at the optimal time point to simultaneously capture the mesenteric arterial enhancement of iodine and the enteric enhancement of bismuth. Image-based material decomposition was performed to decompose different materials for each study. To quantitatively characterize contrast material separation and misclassification, two ROIs on left common carotid artery and left internal jugular vein in HN-CTA/CTV imaging and three ROIs on superior mesenteric artery, ileal lumen, and collapsed ileum (ileal wall) in small bowel imaging were placed to measure the mean concentration values and the standard deviations.

Results: In the HN-CTA/CTV study, common carotid arteries containing iodine and internal/external jugular veins containing gadolinium were clearly delineated from each other. Fine vessels such as cephalic veins and branches of external jugular veins were noticeable but clear visualization was hindered by image noise in gadolinium-specific (CTV) images, as reviewed by a neuro radiologist. In the small bowel imaging study, the mesenteric arteries and collapsed bowel wall containing iodine and the small bowel loops containing bismuth were clearly distinctive from each other in the iodine- and bismuth-specific images after material decomposition, as reviewed by an abdominal radiologist. Quantitative analyses showed that the misclassifications between the two contrast materials were less than 1.7 mg/mL and 0.1 mg/mL for CTA/CTV and small bowel imaging studies, respectively.

Conclusions: Feasibility of simultaneous CTA/CTV imaging in head and neck with iodine and gadolinium and simultaneous imaging of arterial and enteric phases of small bowel with iodine and bismuth, using a dual-source EID-MECT system, was demonstrated in a swine study. Compared to iodine and gadolinium in CTA/CTV, better delineation and classification of iodine and bismuth in small bowel imaging were achieved mainly due to wider separation between the corresponding two K-edge energies. This article is protected by copyright. All rights reserved.
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http://dx.doi.org/10.1002/mp.15448DOI Listing
January 2022

First Clinical Photon-counting Detector CT System: Technical Evaluation.

Radiology 2021 Dec 14:212579. Epub 2021 Dec 14.

From the Department of Radiology, Mayo Clinic, Rochester, MN 55905 USA (K.R., A.F., F.B., F.E.D., L.Y., P.R., J.G.F., S.L., C.H.M.), Siemens Healthineers, Forchheim, Germany (M.P., A.H., B.S., T.G.F.) and Siemens Medical Solutions, Malvern, PA, USA (E.R.S.).

Background The first clinical CT system to use photon-counting-detector (PCD) technology has become available for patient care. Purpose To assess the technical performance of the PCD-CT system using phantoms and representative participant exams. Materials and Methods Institutional review board approval and written informed consent from four participants were obtained. Technical performance of a dual-source PCD-CT system was measured for standard and high resolution (HR) collimations. Noise power spectrum (NPS), modulation transfer function (MTF), section sensitivity profile (SSP), iodine CT number accuracy in virtual monoenergetic images (VMI), and iodine concentration accuracy were measured. Four participants were enrolled (between May 2021 and August 2021) in this prospective study and scanned using similar or lower radiation doses compared to same-day exams performed using energy-integrating-detector (EID) CT. Results All standard technical performance measures met accreditation requirements. Relative to filtered-back-projection reconstructions, images from iterative reconstruction had lower noise magnitude but preserved NPS shape and peak-frequency. Maximum in-plane spatial resolutions of 125 and 208 microns were measured for PCD-HR and PCD-standard scans, respectively. Minimum values for SSP full-width-half-maximum measurements were 0.34-mm (0.2 mm nominal section thickness) and 0.64 mm (0.4-mm nominal section thickness) for PCD-HR and PCD-standard scans, respectively. In a PCD-CT 120-kV standard scan of a 40-cm phantom, VMI iodine CT numbers had a mean percent error of 5.7% and iodine concentration had root-mean-squared-error of 0.5 mg/cc, comparable to previously reported values for EID-CT. VMI, iodine map, and virtual non-contrast images were created for a coronary CT angiogram acquired with 66-ms temporal resolution. Participant PCD-CT images showed up to 47% lower noise and/or improved spatial resolution compared to EID-CT. Conclusions Technical performance of a new clinical photon-counting-detector CT is improved relative to current state-of-the-art CT system. The dual-source photon-counting-detector geometry facilitated 66-ms-temporal-resolution multi-energy cardiac imaging. Study-participant images illustrated the impact of the improved technical performance.
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http://dx.doi.org/10.1148/radiol.212579DOI Listing
December 2021

Photon Counting CT: Clinical Applications and Future Developments.

IEEE Trans Radiat Plasma Med Sci 2021 Jul 28;5(4):441-452. Epub 2020 Aug 28.

Department of Radiology at the Mayo Clinic, Rochester MN 55905 USA.

The use of a photon counting detector in CT (PCD CT) is currently the subject of intense investigation and development. In this review article, we will describe potential clinical applications of this technology with a particular focus on the experience of our own institution with a prototype PCD CT scanner. PCDs have three primary advantages over conventional, energy integrating detectors (EIDs): they provide spectral information without need for a dedicated dual energy protocol; they are immune to electronic noise; and they can be made very high resolution without significant compromises to quantum efficiency. These advantages translate into several clinical applications. Metal artifacts, beam hardening artifacts, and noise streaks from photon starvation can be better mitigated using PCD CT. Certain incidental findings can be better characterized using the spectral information from PCD CT. High-contrast, high-resolution structures such as the temporal bone can be better visualized using PCD CT and at greatly reduced dose. We also discuss new possibilities on the horizon, including new contrast agents, and how anticipated improvements in PCD CT will translate to performance in these applications.
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http://dx.doi.org/10.1109/trpms.2020.3020212DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8409241PMC
July 2021

Dual-Contrast Biphasic Liver Imaging With Iodine and Gadolinium Using Photon-Counting Detector Computed Tomography: An Exploratory Animal Study.

Invest Radiol 2022 Feb;57(2):122-129

From the Department of Radiology, Mayo Clinic, Rochester, MN.

Purpose: The aims of this study were to develop a single-scan dual-contrast protocol for biphasic liver imaging with 2 intravenous contrast agents (iodine and gadolinium) and to evaluate its effectiveness in an exploratory swine study using a photon-counting detector computed tomography (PCD-CT) system.

Materials And Methods: A dual-contrast CT protocol was developed for PCD-CT to simultaneously acquire 2 phases of liver contrast enhancement, with the late arterial phase enhanced by 1 contrast agent (iodine-based) and the portal venous phase enhanced by the other (gadolinium-based). A gadolinium contrast bolus (gadobutrol: 64 mL, 8 mL/s) and an iodine contrast bolus (iohexol: 40 mL, 5 mL/s) were intravenously injected in the femoral vein of a healthy domestic swine, with the second injection initiated after 17 seconds from the beginning of the first injection; PCD-CT image acquisition was performed 12 seconds after the beginning of the iodine contrast injection. A convolutional neural network (CNN)-based denoising technique was applied to PCD-CT images to overcome the inherent noise magnification issue in iodine/gadolinium decomposition task. Iodine and gadolinium material maps were generated using a 3-material decomposition method in image space. A set of contrast samples (mixed iodine and gadolinium) was attached to the swine belly; quantitative accuracy of material decomposition in these inserts between measured and true concentrations was calculated using root mean square error. An abdominal radiologist qualitatively evaluated the delineation of arterial and venous vasculatures in the swine liver using iodine and gadolinium maps obtained using the dual-contrast PCD-CT protocol.

Results: The iodine and gadolinium samples attached to the swine were quantified with root mean square error values of 0.75 mg/mL for iodine and 0.45 mg/mL for gadolinium from the contrast material maps derived from the denoised PCD-CT images. Hepatic arteries containing iodine and veins containing gadolinium in the swine liver could be clearly visualized. Compared with the original images, better distinctions between 2 liver phases were achieved using CNN denoising, with approximately 60% to 80% noise reduction in contrast material maps acquired with the denoised PCD-CT images compared with the original images.

Conclusions: Simultaneous biphasic liver imaging in a single multienergy PCD-CT acquisition using a dual-contrast (iodine and gadolinium) injection protocol and CNN denoising was demonstrated in a swine study, where the enhanced hepatic arteries (containing iodine) and the enhanced hepatic veins (containing gadolinium) could be clearly visualized and delineated in the swine liver.
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http://dx.doi.org/10.1097/RLI.0000000000000815DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8732294PMC
February 2022

Full field-of-view, high-resolution, photon-counting detector CT: technical assessment and initial patient experience.

Phys Med Biol 2021 Oct 27;66(20). Epub 2021 Oct 27.

Department of Radiology, Mayo Clinic, Rochester, MN, United States of America.

We report a comprehensive evaluation of a full field-of-view (FOV) photon-counting detector (PCD) computed tomography (CT) system using phantoms, and qualitatively assess image quality in patient examples. A whole-body PCD-CT system with 50 cm FOV, 5.76 cm z-detector coverage and two acquisition modes (standard: 144 × 0.4 mm collimation and ultra-high resolution (UHR): 120 × 0.2 mm collimation) was used in this study. Phantoms were scanned to assess image uniformity, CT number accuracy, noise power spectrum, spatial resolution, material decomposition and virtual monoenergetic imaging (VMI) performance. Four patients were scanned on the PCD-CT system with matched or lower radiation dose than their prior clinical CT scans performed using energy-integrating detector (EID) CT, and the potential clinical impact of PCD-CT was qualitatively evaluated. Phantom results showed water CT numbers within ±5 HU, and image uniformity measured between peripheral and central regions-of-interests to be within ±5 HU. For the UHR mode using a dedicated sharp kernel, the cut-off spatial frequency was 40 line-pairs cm, which corresponds to a 125m limiting in-plane spatial resolution. The full-width-at-half-maximum for the section sensitivity profile was 0.33 mm for the smallest slice thickness (0.2 mm) using the UHR mode. Material decomposition in a multi-energy CT phantom showed accurate material classification, with a root-mean-squared-error of 0.3 mg ccfor iodine concentrations (2-15 mg cc) and 14.2 mg ccfor hydroxyapatite concentrations (200 and 400 mg cc). The average percent error for CT numbers corresponding to the iodine concentrations in VMI (40-70 keV) was 2.75%. Patient PCD-CT images demonstrated better delineation of anatomy for chest and temporal bone exams performed with the UHR mode, which allowed the use of very sharp kernels not possible with EID-CT. VMI and virtual non-contrast images generated from a patient head CT angiography exam using the standard acquisition mode demonstrated the multi-energy capability of the PCD-CT system.
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http://dx.doi.org/10.1088/1361-6560/ac155eDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8551012PMC
October 2021

Deep-learning-based direct synthesis of low-energy virtual monoenergetic images with multi-energy CT.

J Med Imaging (Bellingham) 2021 Sep 19;8(5):052104. Epub 2021 Apr 19.

Mayo Clinic, Department of Radiology, Rochester, Minnesota, United States.

We developed a deep learning method to reduce noise and beam-hardening artifact in virtual monoenergetic image (VMI) at low x-ray energy levels. An encoder-decoder type convolutional neural network was implemented with customized inception modules and in-house-designed training loss (denoted as Incept-net), to directly estimate VMI from multi-energy CT images. Images of an abdomen-sized water phantom with varying insert materials were acquired from a research photon-counting-detector CT. The Incept-net was trained with image patches ( ) extracted from the phantom data, as well as synthesized, random-shaped numerical insert materials. The whole CT images ( ) with the remaining real insert materials that were unseen in network training were used for testing. Seven contrast-enhanced abdominal CT exams were used for preliminary evaluation of Incept-net generalizability over anatomical background. Mean absolute percentage error (MAPE) was used to evaluate CT number accuracy. Compared to commercial VMI software, Incept-net largely suppressed beam-hardening artifact and reduced noise (53%) in phantom study. Incept-net presented comparable CT number accuracy at higher-density ( -value [0.0625, 0.999]) and improved it at lower-density inserts ( ) with overall MAPE: Incept-net [2.9%, 4.6%]; commercial-VMI [6.7%, 10.9%]. In patient images, Incept-net suppressed beam-hardening artifact and reduced noise (up to 50%, ). In this preliminary study, Incept-net presented the potential to improve low-energy VMI quality.
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http://dx.doi.org/10.1117/1.JMI.8.5.052104DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8054272PMC
September 2021

Spectral CT imaging of human osteoarthritic cartilage via quantitative assessment of glycosaminoglycan content using multiple contrast agents.

APL Bioeng 2021 Jun 1;5(2):026101. Epub 2021 Apr 1.

Department of Radiology, University of Otago Christchurch, Christchurch 8011, New Zealand.

Detection of early osteoarthritis to stabilize or reverse the damage to articular cartilage would improve patient function, reduce disability, and limit the need for joint replacement. In this study, we investigated nondestructive photon-processing spectral computed tomography (CT) for the quantitative measurement of the glycosaminoglycan (GAG) content compared to destructive histological and biochemical assay techniques in normal and osteoarthritic tissues. Cartilage-bone cores from healthy bovine stifles were incubated in 50% ioxaglate (Hexabrix) or 100% gadobenate dimeglumine (MultiHance). A photon-processing spectral CT (MARS) scanner with a CdTe-Medipix3RX detector imaged samples. Calibration phantoms of ioxaglate and gadobenate dimeglumine were used to determine iodine and gadolinium concentrations from photon-processing spectral CT images to correlate with the GAG content measured using a dimethylmethylene blue assay. The zonal distribution of GAG was compared between photon-processing spectral CT images and histological sections. Furthermore, discrimination and quantification of GAG in osteoarthritic human tibial plateau tissue using the same contrast agents were demonstrated. Contrast agent concentrations were inversely related to the GAG content. The GAG concentration increased from 25 g/ml (85 mg/ml iodine or 43 mg/ml gadolinium) in the superficial layer to 75 g/ml (65 mg/ml iodine or 37 mg/ml gadolinium) in the deep layer of healthy bovine cartilage. Deep zone articular cartilage could be distinguished from subchondral bone by utilizing the material decomposition technique. Photon-processing spectral CT images correlated with histological sections in healthy and osteoarthritic tissues. Post-imaging material decomposition was able to quantify the GAG content and distribution throughout healthy and osteoarthritic cartilage using Hexabrix and MultiHance while differentiating the underlying subchondral bone.
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http://dx.doi.org/10.1063/5.0035312DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8018795PMC
June 2021

Improved coronary calcification quantification using photon-counting-detector CT: an ex vivo study in cadaveric specimens.

Eur Radiol 2021 Sep 13;31(9):6621-6630. Epub 2021 Mar 13.

Department of Radiology, Mayo Clinic, Rochester, MN, USA.

Objectives: To compare the accuracy of coronary calcium quantification of cadaveric specimens imaged from a photon-counting detector (PCD)-CT and an energy-integrating detector (EID)-CT.

Methods: Excised coronary specimens were scanned on a PCD-CT scanner, using both the PCD and EID subsystems. The scanning and reconstruction parameters for EID-CT and PCD-CT were matched: 120 kV, 9.3-9.4 mGy CTDI, and a quantitative kernel (D50). PCD-CT images were also reconstructed using a sharper kernel (D60). Scanning the same specimens using micro-CT served as a reference standard for calcified volumes. Calcifications were segmented with a half-maximum thresholding technique. Segmented calcified volume differences were analyzed using the Friedman test and post hoc pairwise Wilcoxon signed rank test with the Bonferroni correction. Image noise measurements were compared between EID-CT and PCD-CT with a repeated-measures ANOVA test and post hoc pairwise comparison with the Bonferroni correction. A p < 0.05 was considered statistically significant.

Results: The volume measurements in 12/13 calcifications followed a similar trend: EID-D50 > PCD-D50 > PCD-D60 > micro-CT. The median calcified volumes in EID-D50, PCD-D50, PCD-D60, and micro-CT were 22.1 (IQR 10.2-64.8), 21.0 (IQR 9.0-56.5), 18.2 (IQR 8.3-49.3), and 14.6 (IQR 5.1-42.4) mm, respectively (p < 0.05 for all pairwise comparisons). The average image noise in EID-D50, PCD-D50, and PCD-D60 was 60.4 (± 3.5), 56.0 (± 4.2), and 113.6 (± 8.5) HU, respectively (p < 0.01 for all pairwise comparisons).

Conclusion: The PCT-CT system quantified coronary calcifications more accurately than EID-CT, and a sharp PCD-CT kernel further improved the accuracy. The PCD-CT images exhibited lower noise than the EID-CT images.

Key Points: • High spatial resolution offered by PCD-CT reduces partial volume averaging and consequently leads to better morphological depiction of coronary calcifications. • Improved quantitative accuracy for coronary calcification volumes could be achieved using high-resolution PCD-CT compared to conventional EID-CT. • PCD-CT images exhibit lower image noise than conventional EID-CT at matched radiation dose and reconstruction kernel.
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http://dx.doi.org/10.1007/s00330-021-07780-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8380662PMC
September 2021

Noise reduction in CT image using prior knowledge aware iterative denoising.

Phys Med Biol 2020 Nov 19;65(22). Epub 2020 Nov 19.

Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, United States of America.

The clinical demand for low image noise often limits the slice thickness used in many CT applications. However, a thick-slice image is more susceptible to longitudinal partial volume effects, which can blur key anatomic structures and pathologies of interest. In this work, we develop a prior knowledge aware iterative denoising (PKAID) framework that utilizes spatial data redundancy in the slice increment direction to generate low-noise, thin-slice images, and demonstrate its application in non-contrast head CT exams. The proposed technique takes advantage of the low noise of thicker images and exploits the structural similarity between the thick- and thin-slice images to reduce noise in the thin-slice image. Phantom data and patient cases (= 3) of head CT were used to assess performance of this method. Images were reconstructed at clinically utilized slice thickness (5 mm) and thinner slice thickness (2 mm). PKAID was used to reduce image noise in 2 mm images using the 5 mm images as low-noise prior. Noise amplitude, noise power spectra (NPS), modulation transfer function (MTF), and slice sensitivity profiles (SSPs) of images before/after denoising were analyzed. The NPS and MTF analysis showed that PKAID preserved noise texture and resolution of the original thin-slice image, while reducing noise to the level of thick-slice image. The SSP analysis showed that the slice thickness of the original thin-slice image was retained. Patient examples demonstrated that PKAID-processed, thin-slice images better delineated brain structures and key pathologies such as subdural hematoma compared to the clinical 5 mm images, while additionally reducing image noise. To test an alternative PKAID utilization for dose reduction, a head exam with 40% dose reduction was simulated using projection-domain noise insertion. The image of 5 mm slice thickness was then denoised using PKAID. The results showed that the PKAID-processed reduced-dose images maintained similar noise and image quality compared to the full-dose images.
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http://dx.doi.org/10.1088/1361-6560/abc231DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8050138PMC
November 2020

Deep-learning-based direct inversion for material decomposition.

Med Phys 2020 Dec 30;47(12):6294-6309. Epub 2020 Oct 30.

Department of Radiology, Mayo Clinic, Rochester, MN, 55901, USA.

Purpose: To develop a convolutional neural network (CNN) that can directly estimate material density distribution from multi-energy computed tomography (CT) images without performing conventional material decomposition.

Methods: The proposed CNN (denoted as Incept-net) followed the general framework of encoder-decoder network, with an assumption that local image information was sufficient for modeling the nonlinear physical process of multi-energy CT. Incept-net was implemented with a customized loss function, including an in-house-designed image-gradient-correlation (IGC) regularizer to improve edge preservation. The network consisted of two types of customized multibranch modules exploiting multiscale feature representation to improve the robustness over local image noise and artifacts. Inserts with various densities of different materials [hydroxyapatite (HA), iodine, a blood-iodine mixture, and fat] were scanned using a research photon-counting detector (PCD) CT with two energy thresholds and multiple radiation dose levels. The network was trained using phantom image patches only, and tested with different-configurations of full field-of-view phantom and in vivo porcine images. Furthermore, the nominal mass densities of insert materials were used as the labels in CNN training, which potentially provided an implicit mass conservation constraint. The Incept-net performance was evaluated in terms of image noise, detail preservation, and quantitative accuracy. Its performance was also compared to common material decomposition algorithms including least-square-based material decomposition (LS-MD), total-variation regularized material decomposition (TV-MD), and U-net-based method.

Results: Incept-net improved accuracy of the predicted mass density of basis materials compared with the U-net, TV-MD, and LS-MD: the mean absolute error (MAE) of iodine was 0.66, 1.0, 1.33, and 1.57 mgI/cc for Incept-net, U-net, TV-MD, and LS-MD, respectively, across all iodine-present inserts (2.0-24.0 mgI/cc). With the LS-MD as the baseline, Incept-net and U-net achieved comparable noise reduction (both around 95%), both higher than TV-MD (85%). The proposed IGC regularizer effectively helped both Incept-net and U-net to reduce image artifact. Incept-net closely conserved the total mass densities (i.e., mass conservation constraint) in porcine images, which heuristically validated the quantitative accuracy of its outputs in anatomical background. In general, Incept-net performance was less dependent on radiation dose levels than the two conventional methods; with approximately 40% less parameters, the Incept-net achieved relatively improved performance than the comparator U-net, indicating that performance gain by Incept-net was not achieved by simply increasing network learning capacity.

Conclusion: Incept-net demonstrated superior qualitative image appearance, quantitative accuracy, and lower noise than the conventional methods and less sensitive to dose change. Incept-net generalized and performed well with unseen image structures and different material mass densities. This study provided preliminary evidence that the proposed CNN may be used to improve the material decomposition quality in multi-energy CT.
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http://dx.doi.org/10.1002/mp.14523DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7796910PMC
December 2020

Simultaneous Dual-Contrast Imaging of Small Bowel With Iodine and Bismuth Using Photon-Counting-Detector Computed Tomography: A Feasibility Animal Study.

Invest Radiol 2020 10;55(10):688-694

From the Department of Radiology, Mayo Clinic, Rochester, MN.

Objectives: Dual-energy and multienergy computed tomography (DECT/MECT) has the potential to simultaneously visualize two contrast agents in the small bowel: arterial enhancement of iodine in the bowel wall and enteric enhancement of bismuth in the bowel lumen. The purpose of this study was to explore its feasibility in a swine study using a research whole-body photon-counting-detector (PCD) computed tomography (CT) system.

Materials And Methods: A phantom study was initially performed to evaluate the quantification accuracy of iodine and bismuth separation from a single PCD-CT scan, which also served as the calibration reference for material decomposition of in vivo swine PCD-CT data. In the animal study, a test bolus scan was first performed to determine the time-attenuation curve for the arterial enhancement, based on which the timing of the PCD-CT dual-contrast scan was determined. A 600 mL homogeneous bismuth-saline solution (180 mL Pepto-Bismol + 420 mL normal saline) was orally administered to the pig using esophageal intubation. Approximately 1 hour after bismuth administration, 40 mL iodine contrast (Omnipaque 350, 5 mL/s) was injected intravenously. A PCD-CT scan was performed 13 seconds after the initiation of the contrast injection to simultaneously capture the arterial enhancement of iodine and the enteric enhancement of bismuth. To provide optimal material separation and quantification, all PCD-CT scans in both phantom and animal studies were operated at 140 kV with 4 energy thresholds of 25, 50, 75, and 90 keV.

Results: Using a generic image-based material decomposition method, the iodine and bismuth samples were successfully delineated and quantified in the phantom images with a root-mean-square-error of 1.32 mg/mL in iodine measurement and 0.64 mg/mL in bismuth measurement. In the pig study, the enhancing bowel wall containing iodine and the small bowel loop containing bismuth were not differentiable in the original PCD-CT images. However, they were clearly distinctive from each other in the iodine- and bismuth-specific images after material decomposition, as reviewed by an abdominal radiologist. In addition, quantitative analysis showed that the misclassification between the two contrast materials was less than 1.0 mg/mL.

Conclusions: Our study demonstrated the feasibility of simultaneous imaging of iodine and bismuth in small bowel of swine using PCD-CT.
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http://dx.doi.org/10.1097/RLI.0000000000000687DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7808340PMC
October 2020

Multi-energy CT imaging for large patients using dual-source photon-counting detector CT.

Phys Med Biol 2020 08 31;65(17):17NT01. Epub 2020 Aug 31.

Department of Radiology, Mayo Clinic, Rochester, MN, United States of America.

Multi-energy CT imaging of large patients with conventional dual-energy (DE)-CT using an energy-integrating-detector (EID) is challenging due to photon starvation-induced image artifacts, especially in lower tube potential (80-100 kV) images. Here, we performed phantom experiments to investigate the performance of DECT for morbidly obese patients, using an iodine and water material decomposition task as an example, on an emulated dual-source (DS)-photon-counting-detector (PCD)-CT, and compared its performance with a clinical DS-EID-CT. An abdominal CT phantom with iodine inserts of different concentrations was wrapped with tissue-equivalent gel layers to emulate a large patient (50 cm lateral size). The phantom was scanned on a research whole-body single-source (SS)-PCD-CT (140 kV tube potential), a DS-PCD-CT (100/Sn140 kV; Sn140 indicates 140 kV with Sn filter), and a clinical DS-EID-CT (100/Sn140 kV) with the same radiation dose. Phantom scans were repeated five times on each system. The DS-PCD-CT acquisition was emulated by scanning twice on the SS-PCD-CT using different tube potentials. The multi-energy CT images acquired on each system were then reconstructed, and iodine- and water-specific images were generated using material decomposition. The root-mean-square-error (RMSE) between true and measured iodine concentrations were calculated for each system and compared. The images acquired on the DS-EID-CT showed severe artifacts, including ringing, reduced uniformity, and photon starvation artifacts, especially for low-energy images. These were largely reduced in DS-PCD-CT images. The CT number difference that was measured using regions-of-interest across field-of-view were reduced from 20.3 ± 0.9 (DS-EID-CT) to 2.5 ± 0.4 HU on DS-PCD-CT, showing improved image uniformity using DS-PCD-CT. Iodine RMSE was reduced from 3.42 ± 0.03 mg ml (SS-PCD-CT) and 2.90 ± 0.03 mg ml (DS-EID-CT) to 2.39 ± 0.05 mg ml using DS-PCD-CT. DS-PCD-CT out-performed a clinical DS-EID-CT for iodine and water-based material decomposition on phantom emulating obese patients by reducing image artifacts and improving iodine quantification (RMSE reduced by 20%). With DS-PCD-CT, multi-energy CT can be performed on large patients that cannot be accommodated with current DECT.
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http://dx.doi.org/10.1088/1361-6560/ab99e4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7682256PMC
August 2020

Quantitative Knee Arthrography in a Large Animal Model of Osteoarthritis Using Photon-Counting Detector CT.

Invest Radiol 2020 06;55(6):349-356

From the Departments of Radiology.

Objective: The aim of this study was to grade cartilage damage in a swine model of osteoarthritis using a whole-body photon-counting detector (PCD) CT.

Materials And Methods: A multienergy phantom containing gadolinium (Gd) (2, 4, 8, and 16 mg/mL) and hydroxyapatite (200 and 400 mg/cc) was scanned using a PCD-CT system (48 × 0.25 mm collimation, 80 kV, 800 mAs, D50 reconstruction kernel) to serve as calibration for material decomposition and to assess quantification accuracy. Osteoarthritis was induced in Yucatan miniswine (n = 8) using 1.2 mg monoiodoacetate (MIA) injected into a randomized knee, whereas the contralateral control knee received saline. Twenty-one days later, a contrast bolus (gadoterate meglumine, 4 mL/knee) was intra-articularly administered into both knees. The knees were simultaneously scanned on the PCD-CT system (48 × 0.25 mm collimation, 80 kV, 800 mAs). Multienergy images were reconstructed with a sharp "V71" kernel and a quantitative "D50" kernel. Image denoising was applied to the V71 images before grading cartilage damage, and an iterative material decomposition technique was applied to D50 images to generate the Gd maps. Two radiologists blinded to the knee injection status graded the cartilage integrity based on a modified International Cartilage Repair Society scoring system. Histology was performed on excised cartilage using methylene blue/basic fuchsin. Statistical analysis of grade distribution was performed using an exact test of omnibus symmetry with P < 0.05 considered significant.

Results: Material decomposed images from the multienergy phantom scan showed delineation and quantification of Gd and hydroxyapatite with a root-mean-squared error of 0.3 mg/mL and 18.4 mg/cc, respectively. In the animal cohort, the radiologists reported chondromalacia in the MIA knees with International Cartilage Repair Society scores ranging from grade 1 (cartilage heterogeneity, n = 4 knees) to grade 3 (up to 100% cartilage loss, n = 4 knees). Grade 1 was characterized by cartilage heterogeneity and increased joint space in the patellofemoral compartment, whereas grade 3 was characterized by cartilage erosion and bone-on-bone articulation in the patellofemoral compartment. All control knees were scored as grade 0 (normal cartilage). Significant difference (P = 0.004) was observed in the grade distribution between the MIA and control knees. Gross examination of the excised knees showed cartilage lesions in the grade 3 MIA knees. The Gd maps from material decomposition showed lower contrast levels in the joint space of the MIA knee compared with the contralateral control knee due to joint effusion. Histology revealed chondrocyte loss in the MIA knee cartilage confirming the chondrotoxic effects of MIA on cartilage matrix.

Conclusions: We demonstrated a high-resolution and quantitative PCD-CT arthrography technique for grading cartilage damage in a large animal model of osteoarthritis. Photon-counting detector CT offers simultaneous high-resolution and multienergy imaging capabilities that allowed morphological assessment of cartilage loss and quantification of contrast levels in the joint as a marker of joint disease. Cartilage damage in the MIA knees was graded using PCD-CT images, and the image-based findings were further confirmed using histology and gross examination of the excised knees.
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http://dx.doi.org/10.1097/RLI.0000000000000648DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7212750PMC
June 2020

Dose Reduction for Sinus and Temporal Bone Imaging Using Photon-Counting Detector CT With an Additional Tin Filter.

Invest Radiol 2020 02;55(2):91-100

From the Departments of Radiology.

Objective: The aim of this study was to quantitatively demonstrate radiation dose reduction for sinus and temporal bone examinations using high-resolution photon-counting detector (PCD) computed tomography (CT) with an additional tin (Sn) filter.

Materials And Methods: A multienergy CT phantom, an anthropomorphic head phantom, and a cadaver head were scanned on a research PCD-CT scanner using ultra-high-resolution mode at 100-kV tube potential with an additional tin filter (Sn-100 kV) and volume CT dose index of 10 mGy. They were also scanned on a commercial CT scanner with an energy-integrating detector (EID) following standard clinical protocols. Thirty patients referred to clinically indicated sinus examinations, and two patients referred to temporal bone examinations were scanned on the PCD-CT system after their clinical scans on an EID-CT. For the sinus cohort, PCD-CT scans were performed using Sn-100 kV at 4 dose levels at 10 mGy (n = 9), 8 mGy (n = 7), 7 mGy (n = 7), and 6 mGy (n = 7), and the clinical EID-CT was performed at 120 kV and 13.7 mGy (mean CT volume dose index). For the temporal bone scans, PCD-CT was performed using Sn-100 kV (10.1 mGy), and EID-CT was performed at 120 kV and routine clinical dose (52.6 and 66 mGy). For both PCD-CT and EID-CT, sinus images were reconstructed using H70 kernel at 0.75-mm slice thickness, and temporal bone images were reconstructed using a U70 kernel at 0.6-mm slice thickness. In addition, iterative reconstruction with a dedicated sharp kernel (V80) was used to obtain high-resolution PCD-CT images from a sinus patient scan to demonstrate improved anatomic delineation. Improvements in spatial resolution from the dedicated sharp kernel was quantified using modulation transfer function measured with a wire phantom. A neuroradiologist assessed the H70 sinus images for visualization of critical anatomical structures in low-dose PCD-CT images and routine-dose EID-CT images using a 5-point Likert scale (structural detection obscured and poor diagnostic confidence, score = 1; improved anatomic delineation and diagnostic confidence, score = 5). Image contrast and noise were measured in representative regions of interest and compared between PCD-CT and EID-CT, and the noise difference between the 2 acquisitions was used to estimate the dose reduction in the sinus and temporal bone patient cohorts.

Results: The multienergy phantom experiment showed a noise reduction of 26% in the Sn-100 kV PCD-CT image, corresponding to a total dose reduction of 56% compared with EID-CT (clinical dose) without compromising image contrast. The PCD-CT images from the head phantom and the cadaver scans demonstrated a dose reduction of 67% and 83%, for sinus and temporal bone examinations, respectively, compared with EID-CT. In the sinus cohort, PCD-CT demonstrated a mean dose reduction of 67%. The 10- and 8-mGy sinus patient images from PCD-CT were significantly superior to clinical EID-CT for visualization of critical sinus structures (median score = 5 ± 0.82 and P = 0.01 for lesser palatine foramina, median score = 4 ± 0.68 and P = 0.039 for nasomaxillary sutures, and median score = 4 ± 0.96 and P = 0.01 for anterior ethmoid artery canal). The 6- and 7-mGy sinus patient images did not show any significant difference between PCD-CT and EID-CT. In addition, V80 (sharp kernel, 10% modulation transfer function = 18.6 cm) PCD-CT images from a sinus patient scan increased the conspicuity of nasomaxillary sutures compared with the clinical EID-CT images. The temporal bone patient images demonstrated a dose reduction of up to 85% compared with clinical EID-CT images, whereas visualization of inner ear structures such as the incudomalleolar joint were similar between EID-CT and PCD-CT.

Conclusions: Phantom and cadaver studies demonstrated dose reduction using Sn-100 kV PCD-CT compared with current clinical EID-CT while maintaining the desired image contrast. Dose reduction was further validated in sinus and temporal bone patient studies. The ultra-high resolution capability from PCD-CT allowed improved anatomical delineation for sinus imaging compared with current clinical standard.
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http://dx.doi.org/10.1097/RLI.0000000000000614DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8522262PMC
February 2020

Quantitative accuracy and dose efficiency of dual-contrast imaging using dual-energy CT: a phantom study.

Med Phys 2020 Feb 10;47(2):441-456. Epub 2019 Dec 10.

Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA.

Purpose: To evaluate the quantitative accuracy and dose efficiency of simultaneous imaging of two contrast agents using dual-energy computed tomography (DECT), two imaging tasks each representing one potential clinical application were investigated in a phantom study: biphasic liver imaging with iodine and gadolinium, and small bowel imaging with iodine and bismuth.

Methods: To separate and quantify mixtures of two contrast agents using a single DECT scan, mixed iodine and gadolinium samples were prepared with the contrast enhancement values corresponding to the late arterial (iodine) and the portal-venous (gadolinium) phase for biphasic liver imaging. Mixed iodine and bismuth samples were prepared mimicking the arterial (iodine) and the enteric (bismuth) enhancement for small bowel imaging. For comparison to the reference condition of performing two single-energy CT (SECT) scans, contrast samples were prepared separately to mimic separate scans in the arterial/venous phase and arterial/enteric enhancement. Samples were placed in a 35 cm wide water tank and scanned using a third-generation dual-source DECT scanner with three tube potential pairs: 80/Sn150, 90/Sn150, and 100/Sn150 kV, all with default dose partitioning between two x-ray beams to acquire DECT data. The same scanner operated in a single-energy mode acquired SECT data (120 kV). Total radiation dose (CTDIvol) was matched for the single-scan DECT and the two-scan SECT protocols. The DECT protocol was followed by a generic image-based three-material decomposition method to determine the material-specific images, based on which concentrations of each basis material were quantified and noise levels were measured. To compare with the SECT images directly acquired with the SECT protocol, the concentration values in each contrast-specific image were converted to CT numbers at 120 kV (i.e., virtual SECT (vSECT) images). The noise level and noise power spectra differences between the SECT and vSECT images were compared to evaluate the dose efficiency of the single-scan DECT protocol. The impact of dose partitioning in the DECT protocol on quantitative dual-contrast imaging performance was also studied.

Results: For each imaging task, contrast materials were accurately quantified against the nominal concentrations using the DECT data with strong correlation (R  ≥ 0.98 for both imaging tasks). Compared to the SECT protocol, the DECT protocol was not dose efficient. With the optimal x-ray tube potential pair 80/Sn150 kV, the noise level in vSECT images increased by 401%/488% (arterial/portal-venous) for the biphasic liver imaging task and by 10%/41% (arterial/enteric) for the small bowel imaging task compared to that in SECT images. The corresponding radiation dose increase is 2410%/3357% for the biphasic liver imaging task and 21%/99% for the small bowel imaging task, respectively, to achieve the same noise as that in SECT images. This could be improved by adjusting the dose partitioning in DECT.

Conclusions: DECT can be used to simultaneously separate and quantify two contrast materials. However, compared to a two-scan SECT protocol, much higher radiation dose is needed in a single-scan DECT protocol to achieve the same image noise, especially for tasks involving the dual contrast of iodine and gadolinium.
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http://dx.doi.org/10.1002/mp.13912DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7015798PMC
February 2020

Radiation dose efficiency of multi-energy photon-counting-detector CT for dual-contrast imaging.

Phys Med Biol 2019 12 13;64(24):245003. Epub 2019 Dec 13.

Department of Radiology, Mayo Clinic, 200 1st Street SW, Rochester, MN 55902, United States of America.

Compared to traditional multi-scan single-energy CT (SECT), one potential advantage of single-scan multi-energy CT (MECT) proposed for simultaneous imaging of multiple contrast agents is the radiation dose reduction. This phantom study aims to rigorously evaluate whether the radiation dose can truly be reduced in a single-scan MECT protocol (MECT_1s) in biphasic liver imaging with iodine and gadolinium, and small bowel imaging with iodine and bismuth, compared to traditional two-scan SECT protocols (SECT_2s). For MECT_1s, mixed iodine/gadolinium samples were prepared corresponding to late arterial/portal-venous phase for biphasic liver imaging. Mixed iodine/bismuth samples were prepared representing the arterial/enteric enhancement for small bowel imaging. For SECT_2s, separate contrast samples were prepared to mimic separate scans in arterial/venous phase and arterial/enteric enhancement. Samples were placed in a 35 cm wide water phantom and scanned by a research whole-body photon-counting-detector-CT (PCD-CT) system ('chess' mode). MECT images were acquired with optimized kV/threshold settings for each imaging task, and SECT images were acquired at 120 kV. Total CTDIvol was matched for the two protocols. Image-based three-material decomposition was employed in MECT_1s to determine the basis material concentration values, which were converted to CT numbers at 120 kV (i.e. virtual SECT images) to compare with the SECT images directly acquired with SECT_2s. The noise difference between the SECT and the virtual SECT images was compared to evaluate the dose efficiency of MECT_1s. Compared to SECT_2s, MECT_1s was not dose efficient for both imaging tasks. The amount of noise increase is highly task dependent, with noise increased by 203%/278% and 110%/82% in virtual SECT images for iodine/gadolinium and iodine/bismuth quantifications, respectively, corresponding to dose increase by 819%/1328% and 340%/230% in MECT_1s to achieve the same image noise level. MECT with the current PCD-CT technique requires higher radiation dose than SECT to achieve the same image quality.
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http://dx.doi.org/10.1088/1361-6560/ab55bfDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6980362PMC
December 2019

Feasibility of multi-contrast imaging on dual-source photon counting detector (PCD) CT: An initial phantom study.

Med Phys 2019 Sep 5;46(9):4105-4115. Epub 2019 Jul 5.

Department of Radiology, Mayo Clinic, Rochester, MN, USA.

Purpose: Photon-counting-detector-computed tomography (PCD-CT) allows separation of multiple, simultaneously imaged contrast agents, such as iodine (I), gadolinium (Gd), and bismuth (Bi). However, PCDs suffer from several technical limitations such as charge sharing, K-edge escape, and pulse pile-up, which compromise spectral separation of multi-energy data and degrade multi-contrast imaging performance. The purpose of this work was to determine the performance of a dual-source (DS) PCD-CT relative to a single-source (SS) PCD-CT for the separation of simultaneously imaged I, Gd, and Bi contrast agents.

Methods: Phantom experiments were performed using a research whole-body PCD-CT and head/abdomen-sized phantoms containing vials of different I, Gd, Bi concentrations. To emulate a DS-PCD-CT, the phantoms were scanned twice on the SS-PCD-CT using different tube potentials for each scan. A tube potential of 80 kV (energy thresholds = 25/50 keV) was used for low-energy tube, while the high-energy tube used Sn140 kV (Sn indicates tin filter) and thresholds of 25/90 keV. The same phantoms were scanned also on the SS-PCD-CT using the chess acquisition mode. In chess mode, the 4 × 4 subpixels within a macro detector pixel are split into two sets based on a chess-board pattern. With each subpixel set having two energy thresholds, chess mode allows four energy-bin data sets, which permits simultaneous multi-contrast imaging. Because of this design, only 50% area of each detector pixel is configured to receive photons of a pre-defined threshold, leading to 50% dose utilization efficiency. To compensate for this dose inefficiency, the radiation dose for this scan was doubled compared to DS-PCD-CT. A 140 kV tube potential and thresholds = 25/50/75/90 keV were used. These settings were determined based on the K-edges of Gd, and Bi, and were found to yield good differentiation of I/Gd/Bi based on phantom experiments and other literature. The energy-bin images obtained from each scan (scan pair) were used to generate I-, Gd-, Bi-specific image via material decomposition. Root-mean-square-error (RMSE) between the known and measured concentrations was calculated for each scenario. A 20-cm water cylinder phantom was scanned on both systems, which was used for evaluating the magnitude of noise, and noise power spectra (NPS) of I/Gd/Bi-specific images.

Results: Phantom results showed that DS-PCD-CT reduced noise in material-specific images for both head and body phantoms compared to SS-PCD-CT. The noise level of SS-PCD was reduced from 2.55 to 0.90 mg/mL (I), 1.97 to 0.78 mg/mL (Gd), and 0.85 to 0.74 mg/mL (Bi) using DS-PCD. NPS analysis showed that the noise texture of images acquired on both systems is similar. For the body phantom, the RMSE for SS-PCD-CT was reduced relative to DS-PCD-CT from 10.52 to 2.76 mg/mL (I), 7.90 to 2.01 mg/mL (Gd), and 1.91 to 1.16 mg/mL (Bi). A similar trend was observed for the head phantom: RMSE reduced from 2.59 (SS-PCD) to 0.72 (DS-PCD) mg/mL (I), 2.02 to 0.58 mg/mL (Gd), and 0.85 to 0.57 mg/mL (Bi).

Conclusion: We demonstrate the feasibility of performing simultaneous imaging of I, Gd, and Bi materials on DS-PCD-CT. Under the condition without cross scattering, DS-PCD reduced the RMSE for quantification of material concentration in relative to a SS-PCD-CT system using chess mode.
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http://dx.doi.org/10.1002/mp.13668DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6857531PMC
September 2019

Photon-counting Detector CT: System Design and Clinical Applications of an Emerging Technology.

Radiographics 2019 May-Jun;39(3):729-743

From the Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905 (S.L., M.B., S.T., K.R., N.G.C., J.G.F., C.H.M.); and Siemens Healthcare, Malvern, Pa (A.F.H.).

Photon-counting detector (PCD) CT is an emerging technology that has shown tremendous progress in the last decade. Various types of PCD CT systems have been developed to investigate the benefits of this technology, which include reduced electronic noise, increased contrast-to-noise ratio with iodinated contrast material and radiation dose efficiency, reduced beam-hardening and metal artifacts, extremely high spatial resolution (33 line pairs per centimeter), simultaneous multienergy data acquisition, and the ability to image with and differentiate among multiple CT contrast agents. PCD technology is described and compared with conventional CT detector technology. With the use of a whole-body research PCD CT system as an example, PCD technology and its use for in vivo high-spatial-resolution multienergy CT imaging is discussed. The potential clinical applications, diagnostic benefits, and challenges associated with this technology are then discussed, and examples with phantom, animal, and patient studies are provided. RSNA, 2019.
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http://dx.doi.org/10.1148/rg.2019180115DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6542627PMC
March 2020

Improving iodine contrast to noise ratio using virtual monoenergetic imaging and prior-knowledge-aware iterative denoising (mono-PKAID).

Phys Med Biol 2019 05 16;64(10):105014. Epub 2019 May 16.

Department of Radiology, Mayo Clinic, Rochester, MN, United States of America.

Multi-energy CT acquires simultaneous multiple x-ray attenuation measurements from different energy spectra which facilitates the computation of virtual monoenergetic images (VMI) at a specific photon energy (keV). Since the contrast between iodine attenuation and the attenuation of surrounding soft tissues increases at lower x-ray energies, VMIs in the range of 40-70 keV can be used to improve iodine visualization. However, at lower energy levels, image noise in VMIs is substantially increased, which counteracts the benefits from the increased iodine contrast, resulting in a decreased iodine contrast-to-noise ratio (CNR). There exists considerable data redundancy between multi-energy CT images created from the same acquisition. Similarly, a substantial spatio-spectral data redundancy exists between multi-energy CT images and the corresponding VMIs. In this work, we develop a denoising framework that exploits this data redundancy to improve iodine CNR in the VMIs. We accomplish this by applying prior-knowledge-aware iterative denoising to low-energy VMIs; we refer to the denoised images as mono-PKAID images. The proposed framework was evaluated using phantom and in vivo data acquired on a research whole-body photon-counting-detector CT, as well as using data from a commercial dual-source dual-energy CT system. The results of phantom experiments show that the proposed framework can preserve image resolution and noise texture compared to the original VMIs, while reducing noise to improve iodine CNR. Quantitative measurements show that the iodine CNR of 50 keV VMI is improved by 1.8-fold using the proposed method, relative to the VMI produced using commercial software (Mono+). With mono-PKAID, VMIs at lower keV take full advantage of higher iodine contrast without substantially increasing image noise. These observations were confirmed using patient data sets, which demonstrated that mono-PKAID reduced image noise, improved CNR in anatomical regions with iodine perfusion by 1.8-fold, and potentially enhanced the visibility of focal liver lesions.
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http://dx.doi.org/10.1088/1361-6560/ab17faDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6598704PMC
May 2019

Impact of prior information on material decomposition in dual- and multienergy computed tomography.

J Med Imaging (Bellingham) 2019 Jan 14;6(1):013503. Epub 2019 Mar 14.

Mayo Clinic, Department of Radiology, Rochester, Minnesota, United States.

Prior information is often included in the basis material decomposition to solve the quantification problem of three-material mixtures in dual-energy computed tomography (DECT). Multienergy computed tomography (MECT) with more than two energy bins can provide a sufficient solution to this problem without invoking additional prior information. However, a question remains as to whether the prior information should still be included in the material decomposition process using MECT to improve the quantification accuracy and control noise amplification. This study aims to evaluate the impact of the prior information on noise and quantification bias in both DECT and MECT. The material decomposition tasks we used in this study are to quantify water/iodine, water/iodine/gadolinium, and water/ iodine/calcium in two- and three-material decompositions, under the assumption that the object to be decomposed consists of the basis materials and their mixtures. We performed phantom simulation and experimental studies using a clinical DECT system and a research photon-counting-detector-based MECT system. Results in the current phantom studies show that the prior information can still improve the noise performance without substantially affecting the basis material quantitative accuracy during the material decomposition process, even when the number of x-ray energy beams/bins is equal or greater than the number of basis materials.
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http://dx.doi.org/10.1117/1.JMI.6.1.013503DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6416528PMC
January 2019

Impact of Effective Detector Pixel and CT Voxel Size on Accurate Estimation of Blood Volume in Opacified Microvasculature.

Acad Radiol 2019 10 7;26(10):1410-1416. Epub 2018 Dec 7.

Department of Radiology, Mayo Clinic, Rochester, MN 55905. Electronic address:

Rationale And Objectives: The purpose of this study was to determine the impact of effective detector-pixel-size and image voxel size on the accurate estimation of microvessel density (ratio of microvascular lumen volume/tissue volume) in an excised porcine myocardium specimen using microcomputed tomography (CT), and the ability of whole-body energy-integrating-detector (EID) CT and photon-counting-detector (PCD) CT to measure microvessel density in the same ex vivo specimen.

Materials And Methods: Porcine myocardial tissue in which the microvessels contained radio-opaque material was scanned using a micro-CT scanner and data were generated with a range of detector pixel sizes and image voxel sizes from 20 to 260 microns, to determine the impact of these parameters on the accuracy of microvessel density estimates. The same specimen was scanned in a whole-body EID CT and PCD CT system and images reconstructed with 600 and 250 micron slice thicknesses, respectively. Fraction of tissue volume that is filled with opacified microvessels was determined by first subtracting the mean background attenuation value from all voxels, and then by summing the remaining attenuation.

Results: Microvessel density data were normalized to the value measured at 20 µm voxel size, which was considered reference truth for this study. For emulated micro-CT voxels up to 260 µm, the microvessel density was underestimated by at most 11%. For whole-body EID CT and PCD CT, microvessel density was underestimated by 9.5% and overestimated by 0.1%, respectively.

Conclusion: Our data indicate that microvessel density can be accurately calculated from the larger detector pixels used in clinical CT scanners by measuring the increase of CT attenuation caused by these opacified microvessels.
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http://dx.doi.org/10.1016/j.acra.2018.11.013DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7682255PMC
October 2019

High-Resolution Chest Computed Tomography Imaging of the Lungs: Impact of 1024 Matrix Reconstruction and Photon-Counting Detector Computed Tomography.

Invest Radiol 2019 03;54(3):129-137

Siemens Healthineers, Malvern, PA.

Objectives: The aim of this study was to evaluate if a high-resolution photon-counting detector computed tomography (PCD-CT) system with a 1024×1024 matrix reconstruction can improve the visualization of fine structures in the lungs compared with conventional high-resolution CT (HRCT).

Materials And Methods: Twenty-two adult patients referred for clinical chest HRCT (mean CTDI vol, 13.58 mGy) underwent additional dose-matched PCD-CT (mean volume CT dose index, 13.37 mGy) after written informed consent. Computed tomography images were reconstructed at a slice thickness of 1.5 mm and an image increment of 1 mm with our routine HRCT reconstruction kernels (B46 and Bv49) at 512 and 1024 matrix sizes for conventional energy-integrating detector (EID) CT scans. For PCD-CT, routine B46 kernel and an additional sharp kernel (Q65, unavailable for EID) images were reconstructed at 1024 matrix size. Two thoracic radiologists compared images from EID and PCD-CT noting the highest level bronchus clearly identified in each lobe of the right lung, and rating bronchial wall conspicuity of third- and fourth-order bronchi. Lung nodules were also compared with the B46/EID/512 images using a 5-point Likert scale. Statistical analysis was performed using a Wilcoxon signed rank test with a P < 0.05 considered significant.

Results: Compared with B46/EID/512, readers detected higher-order bronchi using B46/PCD/1024 and Q65/PCD/1024 images for every lung lobe (P < 0.0015), but in only the right middle lobe for B46/EID/1024 (P = 0.007). Readers were able to better identify bronchial walls of the third- and fourth-order bronchi better using the Q65/PCD/1024 images (mean Likert scores of 1.1 and 1.5), which was significantly higher compared with B46/EID/1024 or B46/PCD/1024 images (mean difference, 0.8; P < 0.0001). The Q65/PCD/1024 images had a mean nodule score of 1 ± 1.3 for reader 1, and -0.1 (0.9) for reader 2, with one reader having improved nodule evaluation scores for both PCD kernels (P < 0.001), and the other reader not identifying any increased advantage over B46/EID/1024 (P = 1.0).

Conclusions: High-resolution lung PCD-CT with 1024 image matrix reconstruction increased radiologists' ability to visualize higher-order bronchi and bronchial walls without compromising nodule evaluation compared with current chest CT, creating an opportunity for radiologists to better evaluate airway pathology.
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http://dx.doi.org/10.1097/RLI.0000000000000524DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6363870PMC
March 2019

Material decomposition with prior knowledge aware iterative denoising (MD-PKAID).

Phys Med Biol 2018 09 21;63(19):195003. Epub 2018 Sep 21.

Department of Radiology, Mayo Clinic, Rochester, MN, United States of America.

Dual- or multi-energy CT, also known as spectral CT, obtains x-ray attenuation measurements at two or more energy spectra, allowing quantification of materials with different compositions. This process is known as material decomposition, which is the basis for a number of spectral CT applications. The conventional image-domain basis material decomposition is based on a least-squares fitting between the underlying material-specific images and the measured source spectral CT images (i.e. energy-bin or energy-threshold CT images), and a non-iterative solution based on matrix inversion can be derived for this process. However, due to its ill-conditioned nature, the material decomposition process is intrinsically susceptible to noise amplification. Hence, material-specific images can be contaminated by the presence of strong noise, which compromises the conspicuity of small objects, and hinders the delineation of anatomical regions of interest and associated pathology. In this work, we describe an image domain material decomposition framework with prior knowledge aware iterative denoising (MD-PKAID). The proposed framework exploits the structural redundancy between the individual material-specific images and the source spectral CT images to retain structural details in denoised material-specific images. It directly treats material decomposition as a regularized optimization problem with spectral CT images measured with different energy spectra as inputs. Phantom, in vivo animal and human data were acquired on a research whole-body photon-counting-detector-based CT system and a dual-source, dual-energy CT system to test the proposed method. The phantom results show that the proposed MD-PKAID can reduce the root-mean-square-error of basis material quantification by 75% compared to the standard material decomposition based on matrix inversion, while preserving structural details and image resolution in the material-specific images. The initial in vivo results demonstrate that the proposed method can improve delineation of small vasculature features in iodine-specific images while reducing image noise.
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http://dx.doi.org/10.1088/1361-6560/aadc90DOI Listing
September 2018

Ultra-High Resolution Photon-Counting Detector CT Reconstruction using Spectral Prior Image Constrained Compressed-Sensing (UHR-SPICCS).

Proc SPIE Int Soc Opt Eng 2018 Mar;10573

Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, USA 55905.

Photon-counting detector based CT (PCD-CT) enables dose efficient high resolution imaging, in addition to providing multi-energy information. This allows better delineation of anatomical structures crucial for several clinical applications ranging from temporal bone imaging to pulmonary nodule visualization. Due to the smaller detector pixel sizes required for high resolution imaging, the PCD-CT images suffer from higher noise levels. The image quality is further degraded in narrow energy bins as a consequence of low photon counts. This limits the potential benefits that high-resolution PCD-CT could offer. Conventional reconstruction techniques such as the filtered back projection (FBP) have poor performance when reconstructing noisy CT projection data. To enable low noise multi-energy reconstructions, we employed a spectral prior image constrained compressed sensing (SPICCS) framework that exploits the spatio-spectral redundancy in the multi-energy acquisitions. We demonstrated noise reduction in narrow energy bins without losing energy-specific attenuation information and spatial resolution. We scanned an anthropomorphic head phantom, and a euthanized pig using our whole-body prototype PCD-CT system in the ultra-high resolution mode at 120. Image reconstructions were performed using SPICCS and compared with conventional FBP. Noise reduction of 18 to 46% was noticed in narrow energy bins corresponding to 25 - 65 and 65 - 12 , while the mean CT number was preserved. Spatial resolution measurement showed similar modulation transfer function (MTF) values between FBP and SPICCS, demonstrating preservation of spatial resolution.
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http://dx.doi.org/10.1117/12.2294628DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6053073PMC
March 2018

150-μm Spatial Resolution Using Photon-Counting Detector Computed Tomography Technology: Technical Performance and First Patient Images.

Invest Radiol 2018 11;53(11):655-662

Siemens Healthcare, Forchheim, Germany.

Objective: The aims of this study were to quantitatively assess two new scan modes on a photon-counting detector computed tomography system, each designed to maximize spatial resolution, and to qualitatively demonstrate potential clinical impact using patient data.

Materials And Methods: This Health Insurance Portability Act-compliant study was approved by our institutional review board. Two high-spatial-resolution scan modes (Sharp and UHR) were evaluated using phantoms to quantify spatial resolution and image noise, and results were compared with the standard mode (Macro). Patients were scanned using a conventional energy-integrating detector scanner and the photon-counting detector scanner using the same radiation dose. In first patient images, anatomic details were qualitatively evaluated to demonstrate potential clinical impact.

Results: Sharp and UHR modes had a 69% and 87% improvement in in-plane spatial resolution, respectively, compared with Macro mode (10% modulation-translation-function values of 16.05, 17.69, and 9.48 lp/cm, respectively). The cutoff spatial frequency of the UHR mode (32.4 lp/cm) corresponded to a limiting spatial resolution of 150 μm. The full-width-at-half-maximum values of the section sensitivity profiles were 0.41, 0.44, and 0.67 mm for the thinnest image thickness for each mode (0.25, 0.25, and 0.5 mm, respectively). At the same in-plane spatial resolution, Sharp and UHR images had up to 15% lower noise than Macro images. Patient images acquired in Sharp mode demonstrated better delineation of fine anatomic structures compared with Macro mode images.

Conclusions: Phantom studies demonstrated superior resolution and noise properties for the Sharp and UHR modes relative to the standard Macro mode and patient images demonstrated the potential benefit of these scan modes for clinical practice.
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http://dx.doi.org/10.1097/RLI.0000000000000488DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6173631PMC
November 2018

Measuring arterial wall perfusion using photon-counting computed tomography (CT): improving CT number accuracy of artery wall using image deconvolution.

J Med Imaging (Bellingham) 2017 Oct 8;4(4):044006. Epub 2017 Dec 8.

Mayo Clinic, Department of Radiology, Rochester, Minnesota, United States.

Changes in arterial wall perfusion mark the onset of atherosclerosis. A characteristic change is the increased spatial density of vasa vasorum (VV), the microvessels in the arterial walls. Measuring this increased VV (IVV) density using contrast-enhanced computed tomography (CT) has had limited success due to blooming effects from contrast media. If the system point-spread function (PSF) is known, then the blooming effect can be modeled as a convolution between the true signal and the PSF. We report the application of image deconvolution to improve the CT number accuracy in the arterial wall of a phantom and in a porcine model of IVV density, both scanned using a whole-body research photon-counting CT scanner. A 3D-printed carotid phantom filled with three concentrations of iodinated contrast material was scanned to assess blooming and its effect on wall CT number accuracy. The results showed a reduction in blooming effects following image deconvolution, and, consequently, a better delineation between lumen and wall was achieved. Results from the animal experiment showed improved CT number difference between the carotid with IVV density and the normal carotid artery after deconvolution, enabling the detection of VV proliferation, which may serve as an early indicator of atherosclerosis.
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http://dx.doi.org/10.1117/1.JMI.4.4.044006DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5722232PMC
October 2017

Detection of increased vasa vasorum in artery walls: Improving CT number accuracy using image deconvolution.

Proc SPIE Int Soc Opt Eng 2017 Feb 9;10132. Epub 2017 Mar 9.

Dept. of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, USA 55905.

Changes in arterial wall perfusion are an indicator of early atherosclerosis. This is characterized by an increased spatial density of vasa vasorum (VV), the micro-vessels that supply oxygen and nutrients to the arterial wall. Detection of increased VV during contrast-enhanced computed tomography (CT) imaging is limited due to contamination from blooming effect from the contrast-enhanced lumen. We report the application of an image deconvolution technique using a measured system point-spread function, on CT data obtained from a photon-counting CT system to reduce blooming and to improve the CT number accuracy of arterial wall, which enhances detection of increased VV. A phantom study was performed to assess the accuracy of the deconvolution technique. A porcine model was created with enhanced VV in one carotid artery; the other carotid artery served as a control. CT images at an energy range of 25-120 keV were reconstructed. CT numbers were measured for multiple locations in the carotid walls and for multiple time points, pre and post contrast injection. The mean CT number in the carotid wall was compared between the left (increased VV) and right (control) carotid arteries. Prior to deconvolution, results showed similar mean CT numbers in the left and right carotid wall due to the contamination from blooming effect, limiting the detection of increased VV in the left carotid artery. After deconvolution, the mean CT number difference between the left and right carotid arteries was substantially increased at all the time points, enabling detection of the increased VV in the artery wall.
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http://dx.doi.org/10.1117/12.2255676DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5391689PMC
February 2017

Quantitative imaging of excised osteoarthritic cartilage using spectral CT.

Eur Radiol 2017 Jan 10;27(1):384-392. Epub 2016 May 10.

Department of Radiology, University of Otago - Christchurch, 2 Riccarton Ave, Christchurch, 8140, New Zealand.

Objectives: To quantify iodine uptake in articular cartilage as a marker of glycosaminoglycan (GAG) content using multi-energy spectral CT.

Methods: We incubated a 25-mm strip of excised osteoarthritic human tibial plateau in 50 % ionic iodine contrast and imaged it using a small-animal spectral scanner with a cadmium telluride photon-processing detector to quantify the iodine through the thickness of the articular cartilage. We imaged both spectroscopic phantoms and osteoarthritic tibial plateau samples. The iodine distribution as an inverse marker of GAG content was presented in the form of 2D and 3D images after applying a basis material decomposition technique to separate iodine in cartilage from bone. We compared this result with a histological section stained for GAG.

Results: The iodine in cartilage could be distinguished from subchondral bone and quantified using multi-energy CT. The articular cartilage showed variation in iodine concentration throughout its thickness which appeared to be inversely related to GAG distribution observed in histological sections.

Conclusions: Multi-energy CT can quantify ionic iodine contrast (as a marker of GAG content) within articular cartilage and distinguish it from bone by exploiting the energy-specific attenuation profiles of the associated materials.

Key Points: • Contrast-enhanced articular cartilage and subchondral bone can be distinguished using multi-energy CT. • Iodine as a marker of glycosaminoglycan content is quantifiable with multi-energy CT. • Multi-energy CT could track alterations in GAG content occurring in osteoarthritis.
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http://dx.doi.org/10.1007/s00330-016-4374-7DOI Listing
January 2017

Study of scan protocol for exposure reduction in hybrid spectral micro-CT.

Scanning 2014 Jul-Aug;36(4):444-55. Epub 2014 Mar 6.

Department of Electrical and Computer Engineering, University of Canterbury, Christchurch, New Zealand.

The hybrid spectral micro-computed tomography (CT) architecture integrates a conventional imaging chain and an interior spectral imaging chain, and has been proven to be an important development in spectral CT. The motivation for this study is to minimize X-ray exposure for hybrid spectral micro-CT using both simulated and experimental scan data while maintaining the spectral fidelity of the reconstruction. Three elements of the hybrid scan protocol are investigated: truncation of the interior spectral chain and the numbers of projections for each of the global and interior imaging chains. The effect of these elements is quantified by analyzing how each affects the reconstructed spectral accuracy. The results demonstrate that there is significant scope for reduction of radiation exposure in the hybrid scan protocol. It appears decreasing the number of conventional projections offers the most potential for exposure reduction, while further reduction is possible by decreasing the interior FOV and number of spectral projections.
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http://dx.doi.org/10.1002/sca.21140DOI Listing
April 2015
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