Publications by authors named "Taly Gilat Schmidt"

38 Publications

Automated image processing pipeline for adaptive optics scanning light ophthalmoscopy.

Biomed Opt Express 2021 Jun 7;12(6):3142-3168. Epub 2021 May 7.

Cell Biology, Neurobiology, and Anatomy, Medical College of Wisconsin, Milwaukee, WI 53226, USA.

To mitigate the substantial post-processing burden associated with adaptive optics scanning light ophthalmoscopy (AOSLO), we have developed an open-source, automated AOSLO image processing pipeline with both "live" and "full" modes. The live mode provides feedback during acquisition, while the full mode is intended to automatically integrate the copious disparate modules currently used in generating analyzable montages. The mean (±SD) lag between initiation and montage placement for the live pipeline was 54.6 ± 32.7s. The full pipeline reduced overall human operator time by 54.9 ± 28.4%, with no significant difference in resultant cone density metrics. The reduced overhead decreases both the technical burden and operating cost of AOSLO imaging, increasing overall clinical accessibility.
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http://dx.doi.org/10.1364/BOE.418079DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8221964PMC
June 2021

Enhancing Reproductive Organ Segmentation in Pediatric CT via Adversarial Learning.

Proc SPIE Int Soc Opt Eng 2021 Feb 15;11596. Epub 2021 Feb 15.

Department of Electrical and Computer Engineering, Marquette University, Milwaukee, USA.

Accurately segmenting organs in abdominal computed tomography (CT) scans is crucial for clinical applications such as pre-operative planning and dose estimation. With the recent advent of deep learning algorithms, many robust frameworks have been proposed for organ segmentation in abdominal CT images. However, many of these frameworks require large amounts of training data in order to achieve high segmentation accuracy. Pediatric abdominal CT images containing reproductive organs are particularly hard to obtain since these organs are extremely sensitive to ionizing radiation. Hence, it is extremely challenging to train automatic segmentation algorithms on organs such as the uterus and the prostate. To address these issues, we propose a novel segmentation network with a built-in auxiliary classifier generative adversarial network (ACGAN) that conditionally generates additional features during training. The proposed CFG-SegNet (conditional feature generation segmentation network) is trained on a single loss function which combines adversarial loss, reconstruction loss, auxiliary classifier loss and segmentation loss. 2.5D segmentation experiments are performed on a custom data set containing 24 female CT volumes containing the uterus and 40 male CT volumes containing the prostate. CFG-SegNet achieves an average segmentation accuracy of 0.929 DSC (Dice Similarity Coefficient) on the prostate and 0.724 DSC on the uterus with 4-fold cross validation. The results show that our network is high-performing and has the potential to precisely segment difficult organs with few available training images.
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http://dx.doi.org/10.1117/12.2582127DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8122493PMC
February 2021

Experimental study of photon-counting CT neural network material decomposition under conditions of pulse pileup.

J Med Imaging (Bellingham) 2021 Jan 9;8(1):013502. Epub 2021 Jan 9.

Marquette University and Medical College of Wisconsin, Department of Biomedical Engineering, Milwaukee, United States.

We investigated the performance of a neural network (NN) material decomposition method under varying pileup conditions. Experiments were performed at tube current settings that provided count rates incident on the detector through air equal to 9%, 14%, 27%, 40%, and 54% of the maximum detector count rate. An NN was trained for each count-rate level using transmission measurements through known thicknesses of basis materials (PMMA and aluminum). The NN trained for each count-rate level was applied to x-ray transmission measurements through test materials and to CT data of a rod phantom. Material decomposition error was evaluated as the distance in basis material space between the estimated thicknesses and ground truth. There was no clear trend between count-rate level and material decomposition error for all test materials except neoprene. As an example result, Teflon error was 0.33 cm at the 9% count-rate level and 0.12 cm at the 54% count-rate level for the x-ray transmission experiments. Decomposition error increased with count-rate level for the neoprene test case, with 0.65-cm error at 9% count-rate level and 1.14-cm error at the 54% count-rate level. In the CT study, material decomposition error decreased with increasing incident count rate. For example, the material decomposition error for Teflon was 0.089, 0.066, 0.054 at count-rate levels of 14%, 27%, and 40%, respectively. Results demonstrate over a range of incident count-rate levels that an NN trained at a specific count-rate level can learn the relationship between photon-counting spectral measurements and basis material thicknesses.
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http://dx.doi.org/10.1117/1.JMI.8.1.013502DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7797008PMC
January 2021

Rapid assessment of breast tumor margins using deep ultraviolet fluorescence scanning microscopy.

J Biomed Opt 2020 11;25(12)

Marquette University and Medical College of Wisconsin, Department of Biomedical Engineering, Milwauk, United States.

Significance: Re-excision rates for women with invasive breast cancer undergoing breast conserving surgery (or lumpectomy) have decreased in the past decade but remain substantial. This is mainly due to the inability to assess the entire surface of an excised lumpectomy specimen efficiently and accurately during surgery.

Aim: The goal of this study was to develop a deep-ultraviolet scanning fluorescence microscope (DUV-FSM) that can be used to accurately and rapidly detect cancer cells on the surface of excised breast tissue.

Approach: A DUV-FSM was used to image the surfaces of 47 (31 malignant and 16 normal/benign) fresh breast tissue samples stained in propidium iodide and eosin Y solutions. A set of fluorescence images were obtained from each sample using low magnification (4  ×  ) and fully automated scanning. The images were stitched to form a color image. Three nonmedical evaluators were trained to interpret and assess the fluorescence images. Nuclear-cytoplasm ratio (N/C) was calculated and used for tissue classification.

Results: DUV-FSM images a breast sample with subcellular resolution at a speed of 1.0  min  /  cm2. Fluorescence images show excellent visual contrast in color, tissue texture, cell density, and shape between invasive carcinomas and their normal counterparts. Visual interpretation of fluorescence images by nonmedical evaluators was able to distinguish invasive carcinoma from normal samples with high sensitivity (97.62%) and specificity (92.86%). Using N/C alone was able to differentiate patch-level invasive carcinoma from normal breast tissues with reasonable sensitivity (81.5%) and specificity (78.5%).

Conclusions: DUV-FSM achieved a good balance between imaging speed and spatial resolution with excellent contrast, which allows either visual or quantitative detection of invasive cancer cells on the surfaces of a breast surgical specimen.
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http://dx.doi.org/10.1117/1.JBO.25.12.126501DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7688317PMC
November 2020

Assessing the Influence of OCT-A Device and Scan Size on Retinal Vascular Metrics.

Transl Vis Sci Technol 2020 10 7;9(11). Epub 2020 Oct 7.

Department of Cell Biology, Neurobiology, and Anatomy, Medical College of Wisconsin, Milwaukee, WI, USA.

Purpose: The purpose of this study was to investigate the effect of device and scan size on quantitative optical coherence tomography angiography (OCT-A) metrics.

Methods: The 3 × 3 mm scans from Optovue AngioVue and Zeiss AngioPlex systems were included for 18 eyes of 18 subjects without ocular pathology. The foveal avascular zone (FAZ) was segmented manually by two observers, from which estimates of FAZ area (using both the nominal image scale and the axial length corrected image scale) and acircularity were derived. Three scan sizes (3 mm, 6 mm HD, and 8 mm) from the AngioVue system were included for 15 eyes of 15 subjects without ocular pathology. For each subject, larger image sizes were resized to the same resolution as 3 × 3 mm scans, aligned, then cropped to a common area. FAZ area, FAZ acircularity, average and total parafoveal intercapillary area, vessel density, and vessel end points were computed.

Results: Between the devices used here, there were no significant differences in FAZ acircularity ( = 0.88) or FAZ area using scaled ( = 0.11) or unscaled images ( = 0.069). Although there was no significant difference in FAZ area across scan sizes ( = 0.30), vessel morphometry metrics were all significantly influenced by scan size.

Conclusions: The scan devices and sizes used here do not affect FAZ area measures derived from manual segmentations. In contrast, vessel morphometry metrics are affected by scan size. As individual differences in axial length induce differences in absolute scan size, extreme care should be taken when interpreting metrics of vessel morphometry, both between and within OCT-A devices.

Translational Relevance: A better characterization of the confounds surrounding OCT-A retinal vasculature metrics can lead to improved application of these metrics as biomarkers for retinal and systemic diseases.
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http://dx.doi.org/10.1167/tvst.9.11.7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7545061PMC
October 2020

Deterministic linear Boltzmann transport equation solver for patient-specific CT dose estimation: Comparison against a Monte Carlo benchmark for realistic scanner configurations and patient models.

Med Phys 2020 Dec 20;47(12):6470-6483. Epub 2020 Oct 20.

Department of Biomedical Engineering, Medical College of Wisconsin and Marquette University, Milwaukee, WI, 53201, USA.

Purpose: Epidemiological evidence suggests an increased risk of cancer related to computed tomography (CT) scans, with children exposed to greater risk. The purpose of this work is to test the reliability of a linear Boltzmann transport equation (LBTE) solver for rapid and patient-specific CT dose estimation. This includes building a flexible LBTE framework for modeling modern clinical CT scanners and to validate the resulting dose maps across a range of realistic scanner configurations and patient models.

Methods: In this study, computational tools were developed for modeling CT scanners, including a bowtie filter, overrange collimation, and tube current modulation. The LBTE solver requires discretization in the spatial, angular, and spectral dimensions, which may affect the accuracy of scanner modeling. To investigate these effects, this study evaluated the LBTE dose accuracy for different discretization parameters, scanner configurations, and patient models (male, female, adults, pediatric). The method used to validate the LBTE dose maps was the Monte Carlo code Geant4, which provided ground truth dose maps. LBTE simulations were implemented on a GeForce GTX 1080 graphic unit, while Geant4 was implemented on a distributed cluster of CPUs.

Results: The agreement between Geant4 and the LBTE solver quantifies the accuracy of the LBTE, which was similar across the different protocols and phantoms. The results suggest that 18 views per rotation provides sufficient accuracy, as no significant improvement in the accuracy was observed by increasing the number of projection views. Considering this discretization, the LBTE solver average simulation time was approximately 30 s. However, in the LBTE solver the phantom model was implemented with a lower voxel resolution with respect to Geant4, as it is limited by the memory of the GPU. Despite this discretization, the results showed a good agreement between the LBTE and Geant4, with root mean square error of the dose in organs of approximately 3.5% for most of the studied configurations.

Conclusions: The LBTE solver is proposed as an alternative to Monte Carlo for patient-specific organ dose estimation. This study demonstrated accurate organ dose estimates for the rapid LBTE solver when considering realistic aspects of CT scanners and a range of phantom models. Future plans will combine the LBTE framework with deep learning autosegmentation algorithms to provide near real-time patient-specific organ dose estimation.
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http://dx.doi.org/10.1002/mp.14494DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7837758PMC
December 2020

Experimental investigation of neural network estimator and transfer learning techniques for K-edge spectral CT imaging.

Med Phys 2020 Feb 6;47(2):541-551. Epub 2020 Jan 6.

Department of Biomedical Engineering, Marquette University, Medical College of Wisconsin, Milwaukee, WI, 53233, USA.

Purpose: Spectral computed tomography (CT) material decomposition algorithms require accurate physics-based models or empirically derived models. This study investigates a machine learning algorithm and transfer learning techniques for Spectral CT imaging of K-edge contrast agents using simulated and experimental measurements.

Methods: A feed forward multilayer perceptron was implemented and trained on data acquired using a step wedge phantom containing acrylic, aluminum, and gadolinium materials. The neural network estimator was evaluated by scanning a rod phantom with varying dilutions of gadolinium oxide nanoparticles and by scanning a rat leg specimen with injected nanoparticles on a bench-top photon-counting computed tomography system. The algorithm decomposed each spectral projection measurement into path lengths of acrylic and aluminum and mass lengths of gadolinium. Each basis material sinogram was reconstructed into basis material images using filtered backprojection. Machine learning techniques of data standardization, transfer learning from aggregated pixel data, and transfer learning from simulations were investigated to improve image quality. The algorithm was compared to a previously published empirical method based on a linear approximation and calibration error look-up tables.

Results: The combined transfer learning techniques did not improve quantification in the rod phantom and provided only a small qualitative improvement in ring artifacts. Transfer learning from aggregated pixel data and from simulations improved the qualitative image quality of the rat specimen, for which the calibration data were limited. Transfer learning from aggregated pixel data and simulations estimated 3.26, 6.26, and 12.45 mg/mL Gd concentrations compared to true 2.72, 5.44, and 10.88 mg/mL concentrations in the rod phantom. Additionally, the neural networks were able to separate the soft tissue, bone, and gadolinium nanoparticles of the ex vivo rat leg specimen into the different basis images.

Conclusions: The results demonstrate that empirical K-edge imaging from calibration measurements using machine learning and transfer learning is possible without explicit models of material attenuations, incident spectra, or the detector response.
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http://dx.doi.org/10.1002/mp.13946DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7747865PMC
February 2020

Estimation of changing gross tumor volume from longitudinal CTs during radiation therapy delivery based on a texture analysis with classifier algorithms: a proof-of-concept study.

Quant Imaging Med Surg 2019 Jul;9(7):1189-1200

Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, USA.

Background: Adaptive radiation therapy (ART) is moving into the clinic rapidly. Capability of delineating the tumor change as a result of treatment response during treatment delivery is essential for ART. During image-guided radiation therapy (IGRT), a CT or cone-beam CT is taken at the time of daily setup and the tumor is not visible by eye in regions of soft tissue due to low contrast. The scope of this paper is to develop a method using a classifier trained on non-contrast CT textures, to estimate the gross tumor volume (GTV) of the day (GTVd) from daily (longitudinal) CTs acquired during the course of IGRT when the tumor is not visible.

Methods: CT textures from daily diagnostic-quality CTs routinely acquired during IGRT using an in-room CT were analyzed. Pretreatment GTV was delineated from pre-RT diagnostic images and populated to the first daily CT. Maps of first-order textures (mean, SD, entropy, skewness and kurtosis) and short-range second-order textures were created from the first daily CT. The classifier was trained to sort voxels into GTV and surrounding tissue on subsequent daily CTs over the course of RT. Optimum combinations of textures was defined by repeating the training process with all possible texture combinations. The trained classifier was used to identify voxels belonging to the GTVd, based on the CT of the day. Posttreatment GTV delineated from the post-RT follow-up images was populated to the last daily CT and used to validate the last GTVd delineated by the classifier. To demonstrate the concept, the method was described using three representative treatment sites, e.g., lung, breast and pancreatic tumors.

Results: Comparing the classifier map generated from a new CT to the initial training CT, the dice coefficient (DC) for GTV in lung is 83% on the eighth treatment and 84% on the last. The DC for the breast GTV is 56% mid-treatment and 65% at the last treatment. In the case of the pancreas with the least in organ tissue contrast, the DC for 4 cases ranges from 21% to 77% for the last treatment compared with the post-RT diagnostic CT. The Housdorff distance (HD) ranged from 2.9 to 5.9 mm with the mean GTV RECIST dimension of 22.75 mm long by 14.7 mm short.

Conclusions: It is feasible to estimate the general region of the GTV of the day from the daily CT acquired during RT, based on CT textures, using a trained voxel classifier algorithm. The obtained GTV may be used as a starting point for an accurate GTV delineation in online adaptive replanning. Further study with larger patient datasets is required to improve the robustness of the algorithms.
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http://dx.doi.org/10.21037/qims.2019.06.24DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6685807PMC
July 2019

The Utility of Frame Averaging for Automated Algorithms in Analyzing Retinal Vascular Biomarkers in AngioVue OCTA.

Transl Vis Sci Technol 2019 Jan 18;8(1):10. Epub 2019 Jan 18.

Department of Cell Biology, Neurobiology, & Anatomy, Medical College of Wisconsin, Milwaukee, WI, USA.

Purpose: This study proposes an optical coherence tomography angiography (OCTA) frame-averaging method and investigates the effects of the number of frames acquired and averaged on metrics quantifying the foveal avascular zone (FAZ), vessel morphology, and parafoveal intercapillary area (PICA).

Methods: Ten OCTA frames were acquired for each of the 19 subjects without known retinal disease using the AngioVue OCTA system. For each subject, acquired frames were ranked by an image quality metric. A subset of frames was then registered and averaged. The effects of the number of frames acquired and averaged on FAZ segmentation and metrics of FAZ geometry, vessel morphology, and PICA were analyzed.

Results: Frame averaging increased the accuracy of the automatically segmented FAZ region; for example, the absolute error in FAZ area decreased from 0.026 mm (1 frame) to 0.005 mm (5 frames). Averaging multiple frames exponentially decreased the estimated number of vessel endpoints and increased the average vessel length with a 32% decrease in number of endpoints and 14% increase in average vessel length when averaging five frames compared with one. Frame averaging also improved the precision of PICA estimates.

Conclusions: Averaging multiple OCTA frames using the Optovue AngioVue system reduced error in FAZ segmentation and improved the robustness of OCTA vessel morphology and perfusion metrics. The study demonstrated limited benefit in acquiring and averaging more than five frames.

Translational Relevance: Averaging multiple OCTA frames improved the robustness of OCTA foveal biomarkers with limited benefit when averaging more than five frames.
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http://dx.doi.org/10.1167/tvst.8.1.10DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6340247PMC
January 2019

A fast, linear Boltzmann transport equation solver for computed tomography dose calculation (Acuros CTD).

Med Phys 2019 Feb 24;46(2):925-933. Epub 2018 Dec 24.

Department of Biomedical Engineering, Marquette University, Milwaukee, WI, 53201, USA.

Purpose: To improve dose reporting of CT scans, patient-specific organ doses are highly desired. However, estimating the dose distribution in a fast and accurate manner remains challenging, despite advances in Monte Carlo methods. In this work, we present an alternative method that deterministically solves the linear Boltzmann transport equation (LBTE), which governs the behavior of x-ray photon transport through an object.

Methods: Our deterministic solver for CT dose (Acuros CTD) is based on the same approach used to estimate scatter in projection images of a CT scan (Acuros CTS). A deterministic method is used to compute photon fluence within the object, which is then converted to deposited energy by multiplying by known, material-specific conversion factors. To benchmark Acuros CTD, we used the AAPM Task Group 195 test for CT dose, which models an axial, fan beam scan (10 mm thick beam) and calculates energy deposited in each organ of an anthropomorphic phantom. We also validated our own Monte Carlo implementation of Geant4 to use as a reference to compare Acuros against for other common geometries like an axial, cone beam scan (160 mm thick beam) and a helical scan (40 mm thick beam with table motion for a pitch of 1).

Results: For the fan beam scan, Acuros CTD accurately estimated organ dose, with a maximum error of 2.7% and RMSE of 1.4% when excluding organs with <0.1% of the total energy deposited. The cone beam and helical scans yielded similar levels of accuracy compared to Geant4. Increasing the number of source positions beyond 18 or decreasing the voxel size below 5 × 5 × 5 mm provided marginal improvement to the accuracy for the cone beam scan but came at the expense of increased run time. Across the different scan geometries, run time of Acuros CTD ranged from 8 to 23 s.

Conclusions: In this digital phantom study, a deterministic LBTE solver was capable of fast and accurate organ dose estimates.
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http://dx.doi.org/10.1002/mp.13305DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6367051PMC
February 2019

CT automated exposure control using a generalized detectability index.

Med Phys 2019 Jan 4;46(1):140-151. Epub 2018 Dec 4.

Department of Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, WI, 53233, USA.

Purpose: Identifying an appropriate tube current setting can be challenging when using iterative reconstruction due to the varying relationship between spatial resolution, contrast, noise, and dose across different algorithms. This study developed and investigated the application of a generalized detectability index ( ) to determine the noise parameter to input to existing automated exposure control (AEC) systems to provide consistent image quality (IQ) across different reconstruction approaches.

Methods: This study proposes a task-based automated exposure control (AEC) method using a generalized detectability index ( ). The proposed method leverages existing AEC methods that are based on a prescribed noise level. The generalized metric is calculated using lookup tables of task-based modulation transfer function (MTF) and noise power spectrum (NPS). To generate the lookup tables, the American College of Radiology CT accreditation phantom was scanned on a multidetector CT scanner (Revolution CT, GE Healthcare) at 120 kV and tube current varied manually from 20 to 240 mAs. Images were reconstructed using a reference reconstruction algorithm and four levels of an in-house iterative reconstruction algorithm with different regularization strengths (IR1-IR4). The task-based MTF and NPS were estimated from the measured images to create lookup tables of scaling factors that convert between and noise standard deviation. The performance of the proposed -AEC method in providing a desired IQ level over a range of iterative reconstruction algorithms was evaluated using the American College of Radiology (ACR) phantom with elliptical shell and using a human reader evaluation on anthropomorphic phantom images.

Results: The study of the ACR phantom with elliptical shell demonstrated reasonable agreement between the predicted by the lookup table and measured in the images, with a mean absolute error of 15% across all dose levels and maximum error of 45% at the lowest dose level with the elliptical shell. For the anthropomorphic phantom study, the mean reader scores for images resulting from the -AEC method were 3.3 (reference image), 3.5 (IR1), 3.6 (IR2), 3.5 (IR3), and 2.2 (IR4). When using the -AEC method, the observers' IQ scores for the reference reconstruction were statistical equivalent to the scores for IR1, IR2, and IR3 iterative reconstructions (P > 0.35). The -AEC method achieved this equivalent IQ at lower dose for the IR scans compared to the reference scans.

Conclusions: A novel AEC method, based on a generalized detectability index, was investigated. The proposed method can be used with some existing AEC systems to derive the tube current profile for iterative reconstruction algorithms. The results provide preliminary evidence that the proposed -AEC can produce similar IQ across different iterative reconstruction approaches at different dose levels.
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http://dx.doi.org/10.1002/mp.13286DOI Listing
January 2019

Estimating the spectrum in computed tomography via Kullback-Leibler divergence constrained optimization.

Med Phys 2019 Jan 13;46(1):81-92. Epub 2018 Dec 13.

Department of Radiology, The University of Chicago, Chicago, IL, 60637, USA.

Purpose: We study the problem of spectrum estimation from transmission data of a known phantom. The goal is to reconstruct an x-ray spectrum that can accurately model the x-ray transmission curves and reflects a realistic shape of the typical energy spectra of the CT system.

Methods: Spectrum estimation is posed as an optimization problem with x-ray spectrum as unknown variables, and a Kullback-Leibler (KL)-divergence constraint is employed to incorporate prior knowledge of the spectrum and enhance numerical stability of the estimation process. The formulated constrained optimization problem is convex and can be solved efficiently by use of the exponentiated-gradient (EG) algorithm. We demonstrate the effectiveness of the proposed approach on the simulated and experimental data. The comparison to the expectation-maximization (EM) method is also discussed.

Results: In simulations, the proposed algorithm is seen to yield x-ray spectra that closely match the ground truth and represent the attenuation process of x-ray photons in materials, both included and not included in the estimation process. In experiments, the calculated transmission curve is in good agreement with the measured transmission curve, and the estimated spectra exhibits physically realistic looking shapes. The results further show the comparable performance between the proposed optimization-based approach and EM.

Conclusions: Our formulation of a constrained optimization provides an interpretable and flexible framework for spectrum estimation. Moreover, a KL-divergence constraint can include a prior spectrum and appears to capture important features of x-ray spectrum, allowing accurate and robust estimation of x-ray spectrum in CT imaging.
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http://dx.doi.org/10.1002/mp.13257DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6461446PMC
January 2019

Technical Note: Enhancing soft tissue contrast and radiation-induced image changes with dual-energy CT for radiation therapy.

Med Phys 2018 Jul 4. Epub 2018 Jul 4.

Department of Radiation Oncology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI, 53226, USA.

Purpose: The purpose of this work is to investigate the use of low-energy monoenergetic decompositions obtained from dual-energy CT (DECT) to enhance image contrast and the detection of radiation-induced changes of CT textures in pancreatic cancer.

Methods: The DECT data acquired for 10 consecutive pancreatic cancer patients during routine nongated CT-guided radiation therapy (RT) using an in-room CT (Definition AS Open, Siemens Healthcare, Malvern, PA) were analyzed. With a sequential DE protocol, the scanner rapidly performs two helical acquisitions, the first at a tube voltage of 80 kVp and the second at a tube voltage of 140 kVp. Virtual monoenergetic images across a range of energies from 40 to 140 keV were reconstructed using an image-based material decomposition. Intravenous (IV) bolus-free contrast enhancement in pancreas patient tumors was measured across a spectrum of monoenergies. For treatment response assessment, the changes in CT histogram features (including mean CT number (MCTN), entropy, kurtosis) in pancreas tumors were measured during treatment. The results from the monoenergetic decompositions were compared to those obtained from the standard 120 kVp CT protocol for the same subjects.

Results: Data of monoenergetic decompositions of the 10 patients confirmed the expected enhancement of soft tissue contrast as the energy is decreased. The changes in the selected CT histogram features in the pancreas during RT delivery were amplified with the low-energy monoenergetic decompositions, as compared to the changes measured from the 120 kVp CTs. For the patients studied, the average reduction in the MCTN in pancreas from the first to the last (the 28th) treatment fraction was 4.09 HU for the standard 120 kVp and 11.15 HU for the 40 keV monoenergetic decomposition.

Conclusions: Low-energy monoenergetic decompositions from DECT substantially increase soft tissue contrast and increase the magnitude of radiation-induced changes in CT histogram textures during RT delivery for pancreatic cancer. Therefore, quantitative DECT may assist the detection of early RT response.
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http://dx.doi.org/10.1002/mp.13083DOI Listing
July 2018

Application of fractal dimension for quantifying noise texture in computed tomography images.

Med Phys 2018 Jun 9. Epub 2018 Jun 9.

Department of Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, WI, 53233, USA.

Purpose: Evaluation of noise texture information in CT images is important for assessing image quality. Noise texture is often quantified by the noise power spectrum (NPS), which requires numerous image realizations to estimate. This study evaluated fractal dimension for quantifying noise texture as a scalar metric that can potentially be estimated using one image realization.

Methods: The American College of Radiology CT accreditation phantom (ACR) was scanned on a clinical scanner (Discovery CT750, GE Healthcare) at 120 kV and 25 and 90 mAs. Images were reconstructed using filtered back projection (FBP/ASIR 0%) with varying reconstruction kernels: Soft, Standard, Detail, Chest, Lung, Bone, and Edge. For each kernel, images were also reconstructed using ASIR 50% and ASIR 100% iterative reconstruction (IR) methods. Fractal dimension was estimated using the differential box-counting algorithm applied to images of the uniform section of ACR phantom. The two-dimensional Noise Power Spectrum (NPS) and one-dimensional-radially averaged NPS were estimated using established techniques. By changing the radiation dose, the effect of noise magnitude on fractal dimension was evaluated. The Spearman correlation between the fractal dimension and the frequency of the NPS peak was calculated. The number of images required to reliably estimate fractal dimension was determined and compared to the number of images required to estimate the NPS-peak frequency. The effect of Region of Interest (ROI) size on fractal dimension estimation was evaluated. Feasibility of estimating fractal dimension in an anthropomorphic phantom and clinical image was also investigated, with the resulting fractal dimension compared to that estimated within the uniform section of the ACR phantom.

Results: Fractal dimension was strongly correlated with the frequency of the peak of the radially averaged NPS curve, having a Spearman rank-order coefficient of 0.98 (P-value < 0.01) for ASIR 0%. The mean fractal dimension at ASIR 0% was 2.49 (Soft), 2.51 (Standard), 2.52 (Detail), 2.57 (Chest), 2.61 (Lung), 2.66 (Bone), and 2.7 (Edge). A reduction in fractal dimension was observed with increasing ASIR levels for all investigated reconstruction kernels. Fractal dimension was found to be independent of noise magnitude. Fractal dimension was successfully estimated from four ROIs of size 64 × 64 pixels or one ROI of 128 × 128 pixels. Fractal dimension was found to be sensitive to non-noise structures in the image, such as ring artifacts and anatomical structure. Fractal dimension estimated within a uniform region of an anthropomorphic phantom and clinical head image matched that estimated within the ACR phantom for filtered back projection reconstruction.

Conclusions: Fractal dimension correlated with the NPS-peak frequency and was independent of noise magnitude, suggesting that the scalar metric of fractal dimension can be used to quantify the change in noise texture across reconstruction approaches. Results demonstrated that fractal dimension can be estimated from four, 64 × 64-pixel ROIs or one 128 × 128 ROI within a head CT image, which may make it amenable for quantifying noise texture within clinical images.
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http://dx.doi.org/10.1002/mp.13040DOI Listing
June 2018

A Spectral CT Method to Directly Estimate Basis Material Maps From Experimental Photon-Counting Data.

IEEE Trans Med Imaging 2017 09 24;36(9):1808-1819. Epub 2017 Apr 24.

The proposed spectral CT method solves the constrained one-step spectral CT reconstruction (cOSSCIR) optimization problem to estimate basis material maps while modeling the nonlinear X-ray detection process and enforcing convex constraints on the basis map images. In order to apply the optimization-based reconstruction approach to experimental data, the presented method empirically estimates the effective energy-window spectra using a calibration procedure. The amplitudes of the estimated spectra were further optimized as part of the reconstruction process to reduce ring artifacts. A validation approach was developed to select constraint parameters. The proposed spectral CT method was evaluated through simulations and experiments with a photon-counting detector. Basis material map images were successfully reconstructed using the presented empirical spectral modeling and cOSSCIR optimization approach. In simulations, the cOSSCIR approach accurately reconstructed the basis map images (<1% error). In experiments, the proposed method estimated the low-density polyethylene region of the basis maps with 0.5% error in the PMMA image and 4% error in the aluminum image. For the Teflon region, the experimental results demonstrated 8% and 31% error in the PMMA and aluminum basis material maps, respectively, compared with -24% and 126% error without estimation of the effective energy window spectra, with residual errors likely due to insufficient modeling of detector effects. The cOSSCIR algorithm estimated the material decomposition angle to within 1.3 degree error, where, for reference, the difference in angle between PMMA and muscle tissue is 2.1 degrees. The joint estimation of spectral-response scaling coefficients and basis material maps was found to reduce ring artifacts in both a phantom and tissue specimen. The presented validation procedure demonstrated feasibility for the automated determination of algorithm constraint parameters.
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http://dx.doi.org/10.1109/TMI.2017.2696338DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5604434PMC
September 2017

Biplane fluoroscopy for hindfoot motion analysis during gait: A model-based evaluation.

Med Eng Phys 2017 05 1;43:118-123. Epub 2017 Mar 1.

Marquette University, 1515 W. Wisconsin Avenue, Milwaukee, WI 53233 USA. Electronic address:

The purpose of this study was to quantify the accuracy and precision of a biplane fluoroscopy system for model-based tracking of in vivo hindfoot motion during over-ground gait. Gait was simulated by manually manipulating a cadaver foot specimen through a biplane fluoroscopy system attached to a walkway. Three 1.6-mm diameter steel beads were implanted into the specimen to provide marker-based tracking measurements for comparison to model-based tracking. A CT scan was acquired to define a gold standard of implanted bead positions and to create 3D models for model-based tracking. Static and dynamic trials manipulating the specimen through the capture volume were performed. Marker-based tracking error was calculated relative to the gold standard implanted bead positions. The bias, precision, and root-mean-squared (RMS) error of model-based tracking was calculated relative to the marker-based measurements. The overall RMS error of the model-based tracking method averaged 0.43 ± 0.22mm and 0.66 ± 0.43° for static and 0.59 ± 0.10mm and 0.71 ± 0.12° for dynamic trials. The model-based tracking approach represents a non-invasive technique for accurately measuring dynamic hindfoot joint motion during in vivo, weight bearing conditions. The model-based tracking method is recommended for application on the basis of the study results.
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http://dx.doi.org/10.1016/j.medengphy.2017.02.009DOI Listing
May 2017

Accuracy of patient-specific organ dose estimates obtained using an automated image segmentation algorithm.

J Med Imaging (Bellingham) 2016 Oct 29;3(4):043502. Epub 2016 Nov 29.

Varian Medical Systems , 3120 Hansen Way, Palo Alto, California 94304, United States.

The overall goal of this work is to develop a rapid, accurate, and automated software tool to estimate patient-specific organ doses from computed tomography (CT) scans using simulations to generate dose maps combined with automated segmentation algorithms. This work quantified the accuracy of organ dose estimates obtained by an automated segmentation algorithm. We hypothesized that the autosegmentation algorithm is sufficiently accurate to provide organ dose estimates, since small errors delineating organ boundaries will have minimal effect when computing mean organ dose. A leave-one-out validation study of the automated algorithm was performed with 20 head-neck CT scans expertly segmented into nine regions. Mean organ doses of the automatically and expertly segmented regions were computed from Monte Carlo-generated dose maps and compared. The automated segmentation algorithm estimated the mean organ dose to be within 10% of the expert segmentation for regions other than the spinal canal, with the median error for each organ region below 2%. In the spinal canal region, the median error was [Formula: see text], with a maximum absolute error of 28% for the single-atlas approach and 11% for the multiatlas approach. The results demonstrate that the automated segmentation algorithm can provide accurate organ dose estimates despite some segmentation errors.
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http://dx.doi.org/10.1117/1.JMI.3.4.043502DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5126039PMC
October 2016

An algorithm for constrained one-step inversion of spectral CT data.

Phys Med Biol 2016 05 15;61(10):3784-818. Epub 2016 Apr 15.

Department of Statistics, The University of Chicago, 5734 S. University Ave., Chicago, IL 60637, USA.

We develop a primal-dual algorithm that allows for one-step inversion of spectral CT transmission photon counts data to a basis map decomposition. The algorithm allows for image constraints to be enforced on the basis maps during the inversion. The derivation of the algorithm makes use of a local upper bounding quadratic approximation to generate descent steps for non-convex spectral CT data discrepancy terms, combined with a new convex-concave optimization algorithm. Convergence of the algorithm is demonstrated on simulated spectral CT data. Simulations with noise and anthropomorphic phantoms show examples of how to employ the constrained one-step algorithm for spectral CT data.
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http://dx.doi.org/10.1088/0031-9155/61/10/3784DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5494281PMC
May 2016

Quantifying cross-scatter contamination in biplane fluoroscopy motion analysis systems.

J Med Imaging (Bellingham) 2015 Oct 23;2(4):043503. Epub 2015 Oct 23.

Marquette University , Department of Biomedical Engineering, 1515 W. Wisconsin Avenue, Milwaukee, Wisconsin 53233, United States.

Biplane fluoroscopy is used for dynamic in vivo three-dimensional motion analysis of various joints of the body. Cross-scatter between the two fluoroscopy systems may limit tracking accuracy. This study measured the magnitude and effects of cross-scatter in biplane fluoroscopic images. Four cylindrical phantoms of 4-, 6-, 8-, and 10-in. diameter were imaged at varying kVp levels to determine the cross-scatter fraction and contrast-to-noise ratio (CNR). Monte Carlo simulations quantified the effect of the gantry angle on the cross-scatter fraction. A cadaver foot with implanted beads was also imaged. The effect of cross-scatter on marker-based tracking accuracy was investigated. Results demonstrated that the cross-scatter fraction varied from 0.15 for the 4-in. cylinder to 0.89 for the 10-in. cylinder when averaged across kVp. The average change in CNR due to cross-scatter ranged from 5% to 36% CNR decreases for the 4- and 10-in. cylinders, respectively. In simulations, the cross-scatter fraction increased with the gantry angle for the 8- and 10-in. cylinders. Cross-scatter significantly increased static-tracking error by 15%, 25%, and 38% for the 6-, 8-, and 10-in. phantoms, respectively, with no significant effect for the foot specimen. The results demonstrated submillimeter marker-based tracking for a range of phantom sizes, despite cross-scatter degradation.
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http://dx.doi.org/10.1117/1.JMI.2.4.043503DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4718465PMC
October 2015

Technical Note: Phantom study to evaluate the dose and image quality effects of a computed tomography organ-based tube current modulation technique.

Med Phys 2015 Nov;42(11):6572-8

Department of Biomedical Engineering, Marquette University, Milwaukee, Wisconsin 53201.

Purpose: This technical note quantifies the dose and image quality performance of a clinically available organ-dose-based tube current modulation (ODM) technique, using experimental and simulation phantom studies. The investigated ODM implementation reduces the tube current for the anterior source positions, without increasing current for posterior positions, although such an approach was also evaluated for comparison.

Methods: Axial CT scans at 120 kV were performed on head and chest phantoms on an ODM-equipped scanner (Optima CT660, GE Healthcare, Chalfont St. Giles, England). Dosimeters quantified dose to breast, lung, heart, spine, eye lens, and brain regions for ODM and 3D-modulation (SmartmA) settings. Monte Carlo simulations, validated with experimental data, were performed on 28 voxelized head phantoms and 10 chest phantoms to quantify organ dose and noise standard deviation. The dose and noise effects of increasing the posterior tube current were also investigated.

Results: ODM reduced the dose for all experimental dosimeters with respect to SmartmA, with average dose reductions across dosimeters of 31% (breast), 21% (lung), 24% (heart), 6% (spine), 19% (eye lens), and 11% (brain), with similar results for the simulation validation study. In the phantom library study, the average dose reduction across all phantoms was 34% (breast), 20% (lung), 8% (spine), 20% (eye lens), and 8% (brain). ODM increased the noise standard deviation in reconstructed images by 6%-20%, with generally greater noise increases in anterior regions. Increasing the posterior tube current provided similar dose reduction as ODM for breast and eye lens, increased dose to the spine, with noise effects ranging from 2% noise reduction to 16% noise increase. At noise equal to SmartmA, ODM increased the estimated effective dose by 4% and 8% for chest and head scans, respectively. Increasing the posterior tube current further increased the effective dose by 15% (chest) and 18% (head) relative to SmartmA.

Conclusions: ODM reduced dose in all experimental and simulation studies over a range of phantoms, while increasing noise. The results suggest a net dose/noise benefit for breast and eye lens for all studied phantoms, negligible lung dose effects for two phantoms, increased lung dose and/or noise for eight phantoms, and increased dose and/or noise for brain and spine for all studied phantoms compared to the reference protocol.
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http://dx.doi.org/10.1118/1.4933197DOI Listing
November 2015

Experimental comparison of empirical material decomposition methods for spectral CT.

Phys Med Biol 2015 Apr 27;60(8):3175-91. Epub 2015 Mar 27.

Department of Biomedical Engineering, Marquette University, 1250 W Wisconsin Ave, Milwaukee, WI 53233, USA.

Material composition can be estimated from spectral information acquired using photon counting x-ray detectors with pulse height analysis. Non-ideal effects in photon counting x-ray detectors such as charge-sharing, k-escape, and pulse-pileup distort the detected spectrum, which can cause material decomposition errors. This work compared the performance of two empirical decomposition methods: a neural network estimator and a linearized maximum likelihood estimator with correction (A-table method). The two investigated methods differ in how they model the nonlinear relationship between the spectral measurements and material decomposition estimates. The bias and standard deviation of material decomposition estimates were compared for the two methods, using both simulations and experiments with a photon-counting x-ray detector. Both the neural network and A-table methods demonstrated a similar performance for the simulated data. The neural network had lower standard deviation for nearly all thicknesses of the test materials in the collimated (low scatter) and uncollimated (higher scatter) experimental data. In the experimental study of Teflon thicknesses, non-ideal detector effects demonstrated a potential bias of 11-28%, which was reduced to 0.1-11% using the proposed empirical methods. Overall, the results demonstrated preliminary experimental feasibility of empirical material decomposition for spectral CT using photon-counting detectors.
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http://dx.doi.org/10.1088/0031-9155/60/8/3175DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4459606PMC
April 2015

The effects of extending the spectral information acquired by a photon-counting detector for spectral CT.

Phys Med Biol 2015 Feb 23;60(4):1583-600. Epub 2015 Jan 23.

Department of Biomedical Engineering, Marquette University, Milwaukee, WI 53233, USA.

Photon-counting x-ray detectors with pulse-height analysis provide spectral information that may improve material decomposition and contrast-to-noise ratio (CNR) in CT images. The number of energy measurements that can be acquired simultaneously on a detector pixel is equal to the number of comparator channels. Some spectral CT designs have a limited number of comparator channels, due to the complexity of readout electronics. The spectral information could be extended by changing the comparator threshold levels over time, sub pixels, or view angle. However, acquiring more energy measurements than comparator channels increases the noise and/or dose, due to differences in noise correlations across energy measurements and decreased dose utilisation. This study experimentally quantified the effects of acquiring more energy measurements than comparator channels using a bench-top spectral CT system. An analytical and simulation study modeling an ideal detector investigated whether there was a net benefit for material decomposition or optimal energy weighting when acquiring more energy measurements than comparator channels. Experimental results demonstrated that in a two-threshold acquisition, acquiring the high-energy measurement independently from the low-energy measurement increased noise standard deviation in material-decomposition basis images by factors of 1.5-1.7 due to changes in covariance between energy measurements. CNR in energy-weighted images decreased by factors of 0.92-0.71. Noise standard deviation increased by an additional factor of [Formula: see text] due to reduced dose utilisation. The results demonstrated no benefit for two-material decomposition noise or energy-weighted CNR when acquiring more energy measurements than comparator channels. Understanding the noise penalty of acquiring more energy measurements than comparator channels is important for designing spectral detectors and for designing experiments and interpreting data from prototype systems with a limited number of comparator channels.
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http://dx.doi.org/10.1088/0031-9155/60/4/1583DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4339093PMC
February 2015

The effects of gantry tilt on breast dose and image noise in cardiac CT.

Med Phys 2013 Dec;40(12):121905

Department of Biomedical Engineering, Marquette University, Milwaukee, Wisconsin 53233.

Purpose: This study investigated the effects of tilted-gantry acquisition on image noise and glandular breast dose in females during cardiac computed tomography (CT) scans. Reducing the dose to glandular breast tissue is important due to its high radiosensitivity and limited diagnostic significance in cardiac CT scans.

Methods: Tilted-gantry acquisition was investigated through computer simulations and experimental measurements. Upon IRB approval, eight voxelized phantoms were constructed from previously acquired cardiac CT datasets. Monte Carlo simulations quantified the dose deposited in glandular breast tissue over a range of tilt angles. The effects of tilted-gantry acquisition on breast dose were measured on a clinical CT scanner (CT750HD, GE Healthcare) using an anthropomorphic phantom with MOSFET dosimeters in the breast regions. In both simulations and experiments, scans were performed at gantry tilt angles of 0°-30°, in 5° increments. The percent change in breast dose was calculated relative to the nontilted scan for all tilt angles. The percent change in noise standard deviation due to gantry tilt was calculated in all reconstructed simulated and experimental images.

Results: Tilting the gantry reduced the breast dose in all simulated and experimental phantoms, with generally greater dose reduction at increased gantry tilts. For example, at 30° gantry tilt, the dosimeters located in the superior, middle, and inferior breast regions measured dose reductions of 74%, 61%, and 9%, respectively. The simulations estimated 0%-30% total breast dose reduction across the eight phantoms and range of tilt angles. However, tilted-gantry acquisition also increased the noise standard deviation in the simulated phantoms by 2%-50% due to increased pathlength through the iodine-filled heart. The experimental phantom, which did not contain iodine in the blood, demonstrated decreased breast dose and decreased noise at all gantry tilt angles.

Conclusions: Tilting the gantry reduced the dose to the breast, while also increasing noise standard deviation. Overall, the noise increase outweighed the dose reduction for the eight voxelized phantoms, suggesting that tilted gantry acquisition may not be beneficial for reducing breast dose while maintaining image quality.
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http://dx.doi.org/10.1118/1.4829521DOI Listing
December 2013

The performance of MLEM for dynamic imaging from simulated few-view, multi-pinhole SPECT.

IEEE Trans Nucl Sci 2013 Feb;60(1)

Department of Biomedical Engineering, Case Western University, Cleveland, OH 44105 and formerly with the Department of Biomedical Engineering, Marquette University, Milwaukee, WI, 53201, ( ).

Stationary small-animal SPECT systems are being developed for rapid dynamic imaging from limited angular views. This paper quantified, through simulations, the performance of Maximum Likelihood Expectation Maximization (MLEM) for reconstructing a time-activity curve (TAC) with uptake duration of a few seconds from a stationary, three-camera multi-pinhole SPECT system. The study also quantified the benefits of a heuristic method of initializing the reconstruction with a prior image reconstructed from a conventional number of views, for example from data acquired during the late-study portion of the dynamic TAC. We refer to MLEM reconstruction initialized by a prior-image initial guess (IG) as MLEM . The effect of the prior-image initial guess on the depiction of contrast between two regions of a static phantom was quantified over a range of angular sampling schemes. A TAC was modeled from the experimentally measured uptake of Tc-hexamethylpropyleneamine oxime (HMPAO) in the rat lung. The resulting time series of simulated images was quantitatively analyzed with respect to the accuracy of the estimated exponential washin and washout parameters. In both static and dynamic phantom studies, the prior-image initial guess improved the spatial depiction of the phantom, for example improved definition of the cylinder boundaries and more accurate quantification of relative contrast between cylinders. For example in the dynamic study, there was ~50% error in relative contrast for MLEM reconstructions compared to ~25-30% error for MLEM . In the static phantom study, the benefits of the initial guess decreased as the number of views increased. The prior-image initial guess introduced an additive offset in the reconstructed dynamic images, likely due to biases introduced by the prior image. MLEM initialized with a uniform initial guess yielded images that faithfully reproduced the time dependence of the simulated TAC; there were no statistically significant differences in the mean exponential washin/washout parameters estimated from MLEM reconstructions compared to the true values. Washout parameters estimated from MLEM reconstructions did not differ significantly from the true values, however the estimated washin parameter differed significantly from the true value in some cases. Overall, MLEM reconstruction from few views and a uniform initial guess accurately quantified the time dependance of the TAC while introducing errors in the spatial depiction of the object. Initializing the reconstruction with a late-study initial guess improved spatial accuracy while decreasing temporal accuracy in some cases.
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http://dx.doi.org/10.1109/TNS.2012.2214235DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3835826PMC
February 2013

Reducing radiation dose to the female breast during CT coronary angiography: a simulation study comparing breast shielding, angular tube current modulation, reduced kV, and partial angle protocols using an unknown-location signal-detectability metric.

Med Phys 2013 Aug;40(8):081921

Department of Biomedical Engineering, Marquette University, Milwaukee, Wisconsin 53233, USA.

Purpose: The authors compared the performance of five protocols intended to reduce dose to the breast during computed tomography (CT) coronary angiography scans using a model observer unknown-location signal-detectability metric.

Methods: The authors simulated CT images of an anthropomorphic female thorax phantom for a 120 kV reference protocol and five "dose reduction" protocols intended to reduce dose to the breast: 120 kV partial angle (posteriorly centered), 120 kV tube-current modulated (TCM), 120 kV with shielded breasts, 80 kV, and 80 kV partial angle (posteriorly centered). Two image quality tasks were investigated: the detection and localization of 4-mm, 3.25 mg/ml and 1-mm, 6.0 mg/ml iodine contrast signals randomly located in the heart region. For each protocol, the authors plotted the signal detectability, as quantified by the area under the exponentially transformed free response characteristic curve estimator (ÂFE), as well as noise and contrast-to-noise ratio (CNR) versus breast and lung dose. In addition, the authors quantified each protocol's dose performance as the percent difference in dose relative to the reference protocol achieved while maintaining equivalent ÂFE.

Results: For the 4-mm signal-size task, the 80 kV full scan and 80 kV partial angle protocols decreased dose to the breast (80.5% and 85.3%, respectively) and lung (80.5% and 76.7%, respectively) with ÂFE=0.96, but also resulted in an approximate three-fold increase in image noise. The 120 kV partial protocol reduced dose to the breast (17.6%) at the expense of increased lung dose (25.3%). The TCM algorithm decreased dose to the breast (6.0%) and lung (10.4%). Breast shielding increased breast dose (67.8%) and lung dose (103.4%). The 80 kV and 80 kV partial protocols demonstrated greater dose reductions for the 4-mm task than for the 1-mm task, and the shielded protocol showed a larger increase in dose for the 4-mm task than for the 1-mm task. In general, the CNR curves indicate a similar relative ranking of protocol performance as the corresponding ÂFE curves, however, the CNR metric overestimated the performance of the shielded protocol for both tasks, leading to corresponding underestimates in the relative dose increases compared to those obtained when using the ÂFE metric.

Conclusions: The 80 kV and 80 kV partial angle protocols demonstrated the greatest reduction to breast and lung dose, however, the subsequent increase in image noise may be deemed clinically unacceptable. Tube output for these protocols can be adjusted to achieve a more desirable noise level with lesser breast dose savings. Breast shielding increased breast and lung dose when maintaining equivalent ÂFE. The results demonstrated that comparisons of dose performance depend on both the image quality metric and the specific task, and that CNR may not be a reliable metric of signal detectability.
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http://dx.doi.org/10.1118/1.4816302DOI Listing
August 2013

A database for estimating organ dose for coronary angiography and brain perfusion CT scans for arbitrary spectra and angular tube current modulation.

Med Phys 2012 Sep;39(9):5336-46

Department of Biomedical Engineering, Marquette University, Milwaukee, WI, USA.

Purpose: The purpose of this study was to develop a database for estimating organ dose in a voxelized patient model for coronary angiography and brain perfusion CT acquisitions with any spectra and angular tube current modulation setting. The database enables organ dose estimation for existing and novel acquisition techniques without requiring Monte Carlo simulations.

Methods: The study simulated transport of monoenergetic photons between 5 and 150 keV for 1000 projections over 360° through anthropomorphic voxelized female chest and head (0° and 30° tilt) phantoms and standard head and body CTDI dosimetry cylinders. The simulations resulted in tables of normalized dose deposition for several radiosensitive organs quantifying the organ dose per emitted photon for each incident photon energy and projection angle for coronary angiography and brain perfusion acquisitions. The values in a table can be multiplied by an incident spectrum and number of photons at each projection angle and then summed across all energies and angles to estimate total organ dose. Scanner-specific organ dose may be approximated by normalizing the database-estimated organ dose by the database-estimated CTDI(vol) and multiplying by a physical CTDI(vol) measurement. Two examples are provided demonstrating how to use the tables to estimate relative organ dose. In the first, the change in breast and lung dose during coronary angiography CT scans is calculated for reduced kVp, angular tube current modulation, and partial angle scanning protocols relative to a reference protocol. In the second example, the change in dose to the eye lens is calculated for a brain perfusion CT acquisition in which the gantry is tilted 30° relative to a nontilted scan.

Results: Our database provides tables of normalized dose deposition for several radiosensitive organs irradiated during coronary angiography and brain perfusion CT scans. Validation results indicate total organ doses calculated using our database are within 1% of those calculated using Monte Carlo simulations with the same geometry and scan parameters for all organs except red bone marrow (within 6%), and within 23% of published estimates for different voxelized phantoms. Results from the example of using the database to estimate organ dose for coronary angiography CT acquisitions show 2.1%, 1.1%, and -32% change in breast dose and 2.1%, -0.74%, and 4.7% change in lung dose for reduced kVp, tube current modulated, and partial angle protocols, respectively, relative to the reference protocol. Results show -19.2% difference in dose to eye lens for a tilted scan relative to a nontilted scan. The reported relative changes in organ doses are presented without quantification of image quality and are for the sole purpose of demonstrating the use of the proposed database.

Conclusions: The proposed database and calculation method enable the estimation of organ dose for coronary angiography and brain perfusion CT scans utilizing any spectral shape and angular tube current modulation scheme by taking advantage of the precalculated Monte Carlo simulation results. The database can be used in conjunction with image quality studies to develop optimized acquisition techniques and may be particularly beneficial for optimizing dual kVp acquisitions for which numerous kV, mA, and filtration combinations may be investigated.
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http://dx.doi.org/10.1118/1.4739243DOI Listing
September 2012

Response to "Comment on 'Estimation of organ and effective dose due to Compton backscatter security scans'" [Med. Phys., 39, 3396 (2012)].

Med Phys 2012 Sep;39(9):5785-5787

Department of Biomedical Engineering, Marquette University, Milwaukee, Wisconsin 53201

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http://dx.doi.org/10.1118/1.4747611DOI Listing
September 2012

Estimation of organ and effective dose due to Compton backscatter security scans.

Med Phys 2012 Jun;39(6):3396-403

Department of Biomedical Engineering, Marquette University, P.O. Box 1881, Milwaukee, WI 53201, USA.

Purpose: To estimate organ and effective radiation doses due to backscatter security scanners using Monte Carlo simulations and a voxelized phantom set.

Methods: Voxelized phantoms of male and female adults and children were used with the GEANT4 toolkit to simulate a backscatter security scan. The backscatter system was modeled based on specifications available in the literature. The simulations modeled a 50 kVp spectrum with 1.0 mm-aluminum-equivalent filtration and a previously measured exposure of approximately 4.6 μR at 30 cm from the source. Photons and secondary interactions were tracked from the source until they reached zero kinetic energy or exited from the simulation's boundaries. The energy deposited in the phantoms' respective organs was tallied and used to calculate total organ dose and total effective dose for frontal, rear, and full scans with subjects located 30 and 75 cm from the source.

Results: For a full screen, all phantoms' total effective doses were below the established 0.25 μSv standard, with an estimated maximum total effective dose of 0.07 μSv for full screen of a male child. The estimated maximum organ dose due to a full screen was 1.03 μGy, deposited in the adipose tissue of the male child phantom when located 30 cm from the source. All organ dose estimates had a coefficient of variation of less than 3% for a frontal scan and less than 11% for a rear scan.

Conclusions: Backscatter security scanners deposit dose in organs beyond the skin. The effective dose is below recommended standards set by the Health Physics Society (HPS) and the American National Standards Institute (ANSI) assuming the system provides a maximum exposure of approximately 4.6 μR at 30 cm.
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http://dx.doi.org/10.1118/1.4718680DOI Listing
June 2012

Quantifying the tibiofemoral joint space using x-ray tomosynthesis.

Med Phys 2011 Dec;38(12):6672-82

Department of Biomedical Engineering, Marquette University, Milwaukee, Wisconsin 53201.

Purpose: Digital x-ray tomosynthesis (DTS) has the potential to provide 3D information about the knee joint in a load-bearing posture, which may improve diagnosis and monitoring of knee osteoarthritis compared with projection radiography, the current standard of care. Manually quantifying and visualizing the joint space width (JSW) from 3D tomosynthesis datasets may be challenging. This work developed a semiautomated algorithm for quantifying the 3D tibiofemoral JSW from reconstructed DTS images. The algorithm was validated through anthropomorphic phantom experiments and applied to three clinical datasets.

Methods: A user-selected volume of interest within the reconstructed DTS volume was enhanced with 1D multiscale gradient kernels. The edge-enhanced volumes were divided by polarity into tibial and femoral edge maps and combined across kernel scales. A 2D connected components algorithm was performed to determine candidate tibial and femoral edges. A 2D joint space width map (JSW) was constructed to represent the 3D tibiofemoral joint space. To quantify the algorithm accuracy, an adjustable knee phantom was constructed, and eleven posterior-anterior (PA) and lateral DTS scans were acquired with the medial minimum JSW of the phantom set to 0-5 mm in 0.5 mm increments (VolumeRad™, GE Healthcare, Chalfont St. Giles, United Kingdom). The accuracy of the algorithm was quantified by comparing the minimum JSW in a region of interest in the medial compartment of the JSW map to the measured phantom setting for each trial. In addition, the algorithm was applied to DTS scans of a static knee phantom and the JSW map compared to values estimated from a manually segmented computed tomography (CT) dataset. The algorithm was also applied to three clinical DTS datasets of osteoarthritic patients.

Results: The algorithm segmented the JSW and generated a JSW map for all phantom and clinical datasets. For the adjustable phantom, the estimated minimum JSW values were plotted against the measured values for all trials. A linear fit estimated a slope of 0.887 (R² = 0.962) and a mean error across all trials of 0.34 mm for the PA phantom data. The estimated minimum JSW values for the lateral adjustable phantom acquisitions were found to have low correlation to the measured values (R² = 0.377), with a mean error of 2.13 mm. The error in the lateral adjustable-phantom datasets appeared to be caused by artifacts due to unrealistic features in the phantom bones. JSW maps generated by DTS and CT varied by a mean of 0.6 mm and 0.8 mm across the knee joint, for PA and lateral scans. The tibial and femoral edges were successfully segmented and JSW maps determined for PA and lateral clinical DTS datasets.

Conclusions: A semiautomated method is presented for quantifying the 3D joint space in a 2D JSW map using tomosynthesis images. The proposed algorithm quantified the JSW across the knee joint to sub-millimeter accuracy for PA tomosynthesis acquisitions. Overall, the results suggest that x-ray tomosynthesis may be beneficial for diagnosing and monitoring disease progression or treatment of osteoarthritis by providing quantitative images of JSW in the load-bearing knee.
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http://dx.doi.org/10.1118/1.3662891DOI Listing
December 2011
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