Publications by authors named "Linghong Zhou"

74 Publications

A prior image constraint robust principal component analysis reconstruction method for sparse segmental multi-energy computed tomography.

Quant Imaging Med Surg 2021 Sep;11(9):4097-4114

School of Biomedical Engineering, Southern Medical University, Guangzhou, China.

Background: Multi-energy computed tomography (MECT) is a promising technique in medical imaging, especially for quantitative imaging. However, high technical requirements and system costs barrier its step into clinical practice.

Methods: We propose a novel sparse segmental MECT (SSMECT) scheme and corresponding reconstruction method, which is a cost-efficient way to realize MECT on a conventional single-source CT. For the data acquisition, the X-ray source is controlled to maintain an energy within a segmental arc, and then switch alternately to another energy level. This scan only needs to switch tube voltage a few times to acquire multi-energy data, but leads to sparse-view and limited-angle issues in image reconstruction. To solve this problem, we propose a prior image constraint robust principal component analysis (PIC-RPCA) reconstruction method, which introduces structural similarity and spectral correlation into the reconstruction.

Results: A numerical simulation and a real phantom experiment were conducted to demonstrate the efficacy and robustness of the scan scheme and reconstruction method. The results showed that our proposed reconstruction method could have achieved better multi-energy images than other competing methods both quantitatively and qualitatively.

Conclusions: Our proposed SSMECT scan with PIC-RPCA reconstruction method could lower kVp switching frequency while achieving satisfactory reconstruction accuracy and image quality.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.21037/qims-20-844DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8339662PMC
September 2021

Association of the collagen score with anastomotic leakage in rectal cancer patients after neoadjuvant chemoradiotherapy.

Surgery 2021 Jun 8. Epub 2021 Jun 8.

Department of General Surgery, Nanfang Hospital, Southern Medical University & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Guangzhou, China. Electronic address:

Background: Collagen changes in the extracellular matrix caused by neoadjuvant chemoradiotherapy are a potential mechanism of anastomotic leakage. We aimed to construct a fully quantitative collagen score to describe collagen structure changes in the extracellular matrix and then develop and validate a prediction model to identify patients who are at a high risk of postoperative anastomotic leakage.

Methods: This is a retrospective study in which 372 patients were enrolled, and their baseline clinicopathological characteristics were collected. Anastomotic distal and proximal "doughnut" specimens underwent second harmonic generation imaging, and collagen features were extracted. A LASSO regression was used to select significant predictors, and the collagen score was constructed. A prediction model based on collagen score was developed and internally and externally validated.

Results: The primary cohort included 214 consecutive patients, and the anastomotic leakage rate was 8.9%. The validation cohort comprised 158 consecutive patients, and the anastomotic leakage rate was 10.1%. The collagen score was significantly related to anastomotic leakage in both cohorts (P < .001). Multivariate analysis revealed that tumor location, preoperative albumin, and collagen score were independent predictors of anastomotic leakage. These 3 predictors were incorporated into the prediction model, and a nomogram was established. The model showed good discrimination in the primary (area under the curve: 0.954) and validation (area under the curve: 0.928) cohorts. Decision curve analysis demonstrated that the nomogram was clinically useful.

Conclusion: The collagen score is associated with anastomotic leakage, and the collagen nomogram based on the collagen score is useful for individualized prediction of anastomotic leakage in rectal cancer patients with neoadjuvant chemoradiotherapy after surgery.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.surg.2021.05.023DOI Listing
June 2021

A feasibility study on deep learning-based individualized 3D dose distribution prediction.

Med Phys 2021 Aug 11;48(8):4438-4447. Epub 2021 Jul 11.

Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA.

Purpose: Radiation therapy treatment planning is a trial-and-error, often time-consuming process. An approximately optimal dose distribution corresponding to a specific patient's anatomy can be predicted by using pre-trained deep learning (DL) models. However, dose distributions are often optimized based not only on patient-specific anatomy but also on physicians' preferred trade-offs between planning target volume (PTV) coverage and organ at risk (OAR) sparing or among different OARs. Therefore, it is desirable to allow physicians to fine-tune the dose distribution predicted based on patient anatomy. In this work, we developed a DL model to predict the individualized 3D dose distributions by using not only the patient's anatomy but also the desired PTV/OAR trade-offs, as represented by a dose volume histogram (DVH), as inputs.

Methods: In this work, we developed a modified U-Net network to predict the 3D dose distribution by using patient PTV/OAR masks and the desired DVH as inputs. The desired DVH, fine-tuned by physicians from the initially predicted DVH, is first projected onto the Pareto surface, then converted into a vector, and then concatenated with feature maps encoded from the PTV/OAR masks. The network output for training is the dose distribution corresponding to the Pareto optimal DVH. The training/validation datasets contain 77 prostate cancer patients, and the testing dataset has 20 patients.

Results: The trained model can predict a 3D dose distribution that is approximately Pareto optimal while having the DVH closest to the input desired DVH. We calculated the difference between the predicted dose distribution and the optimized dose distribution that has a DVH closest to the desired one for the PTV and for all OARs as a quantitative evaluation. The largest absolute error in mean dose was about 3.6% of the prescription dose, and the largest absolute error in the maximum dose was about 2.0% of the prescription dose.

Conclusions: In this feasibility study, we have developed a 3D U-Net model with the patient's anatomy and the desired DVH curves as inputs to predict an individualized 3D dose distribution that is approximately Pareto optimal while having the DVH closest to the desired one. The predicted dose distributions can be used as references for dosimetrists and physicians to rapidly develop a clinically acceptable treatment plan.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/mp.15025DOI Listing
August 2021

Multi-path synergic fusion deep neural network framework for breast mass classification using digital breast tomosynthesis.

Phys Med Biol 2020 12 4;65(23):235045. Epub 2020 Dec 4.

Radiotherapy Center, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, Guangdong 510095 People's Republic of China. Contributed equally.

Objective: To develop and evaluate a multi-path synergic fusion (MSF) deep neural network model for breast mass classification using digital breast tomosynthesis (DBT).

Methods: We retrospectively collected 441 patients who had undergone DBT in which the regions of interest (ROIs) covering the malignant/benign breast mass were extracted for model training and validation. In the proposed MSF framework, three multifaceted representations of the breast mass (gross mass, overview, and mass background) are extracted from the ROIs and independently processed by a multi-scale multi-level features enforced DenseNet (MMFED). The three MMFED sub-models are finally fused at the decision level to generate the final prediction. The advantages of the MMFED over the original DenseNet, as well as different fusion strategies embedded in MSF, were comprehensively compared.

Results: The MMFED was observed to be superior to the original DenseNet, and multiple channel fusions in the MSF outperformed the single-channel MMFED and double-channel fusion with the best classification scores of area under the receiver operating characteristic (ROC) curve (87.03%), Accuracy (81.29%), Sensitivity (74.57%), and Specificity (84.53%) via the weighted fusion method embedded in MSF. The decision level fusion-based MSF was significantly better (in terms of the ROC curve) than the feature concatenation-based fusion (p< 0.05), the single MMFED using a fused three-channel image (p< 0.04), and the multiple MMFED end-to-end training (p< 0.004).

Conclusions: Integrating multifaceted representations of the breast mass tends to increase benign/malignant mass classification performance and the proposed methodology was verified to be a promising tool to assist in clinical breast cancer screening.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1088/1361-6560/abaeb7DOI Listing
December 2020

Scatter correction based on adaptive photon path-based Monte Carlo simulation method in Multi-GPU platform.

Comput Methods Programs Biomed 2020 Oct 11;194:105487. Epub 2020 May 11.

School of Biomedical Engineering, Southern Medical University, Guangzhou, China, 510515. Electronic address:

Monte Carlo (MC)-based simulation is the most precise method in scatter correction for Cone-beam CT (CBCT). Nonetheless, the existing MC methods cannot be fully applied in clinical due to its low efficiency. The traditional MC simulations perform calculations via a particle-by-particle scheme, which leads to high computation costs because abundant photons do not reach the X-ray detector in transport. The conventional approaches cannot control where the particle ends. Hence, it unavoidably waste lots of time in transporting numerous photons that have no contribution to the signal at the detector, yielding a low computational efficiency. To solve the problem, an innovative GPU-based Metropolis MC (gMMC) method was proposed. Compared with the traditional ones, the Metropolis based algorithm utilizes a path-by-path sampling method. The method can automatically control each particle path and eventually accelerate the convergence. In this paper, we firstly take planning CT image as prior information because of its precise CT value, and utilize gMMC to estimate scatter signal. Then the scatter signals are removed from the raw CBCT projections. Afterwards, FDK reconstruction is performed to obtain the corrected image,some accelerating strategies including reducing photon history number, pixels sampling, projection angles sampling and reconstructed image down-sampling achieve adaptive fast CBCT image reconstruction. For having high computational efficiency, we implemented the whole workflow on a 4-GPU workstation. In order to verify the feasibility of the the method, the experiment of several cases are conducted including simulation, phantom, and real patient cases. Results indicate that the image contrast becomes better, the scatter artifacts are eliminated. The maximum error (e), the minimum error (e), the 95th percentile error (e), average error (¯e) are reduced from 264, 56, 14 and 21 HU to 28, 10, 3 and 7 HU in full-fan case, and from 387, 5, 19 and 95 HU to 39, 2, 2 and 6 HU in the half-fan case. In terms of computation time, the MC simulation time of all cases is within 2.5 seconds, and the total time is within 15 seconds.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.cmpb.2020.105487DOI Listing
October 2020

The Canadian Dermatology Association's Top Five Choosing Wisely Canada Recommendations.

J Cutan Med Surg 2020 Sep/Oct;24(5):461-467. Epub 2020 May 20.

2129 Division of Dermatology, Department of Medicine, University of Calgary, AB, Canada.

Introduction: In this article, we present the Canadian Dermatology Association's (CDA) Choosing Wisely Canada (CWC) list of top "Five Things Physicians and Patients Should Question in Dermatology" and the evidence in support of each recommendation.

Methods: Using a nominal technique, the CDA Working Group and Task Force generated an initial list based on literature review and expert consultation. After several rounds of list refinement via a modified Delphi process, a final list of recommendations was generated. These were approved by the CDA Board of Directors, presented at the CDA 93rd Annual Conference in 2018, and published by CWC in 2019.

Results: The top five recommendations are as follows: (1) Don't routinely prescribe antibiotics for bilateral lower leg redness and swelling; (2) Don't routinely prescribe topical combination corticosteroid/antifungal products; (3) Don't routinely use topical antibiotics on a surgical wound; (4) Don't prescribe systemic antifungals without mycological confirmation of dermatophyte infection; and (5) Don't use oral antibiotics for acne vulgaris for more than 3 months without assessing efficacy.

Discussion: This list of recommendations aims to encourage both physicians and patients to reevaluate ineffective, yet common, practices in treating dermatologic conditions. These recommendations represent actionable changes in practice, and therefore have considerable potential to enhance value-based care in dermatology.

Conclusions: This list was developed to identify tangible changes in practice within dermatology that may reduce inefficiencies, prevent potential patient harm, and improve care. Future advocacy work may include updates, feedback obtainment, and patient care handouts, to continue to promote value-based healthcare and best practices.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1177/1203475420928904DOI Listing
May 2020

Comparison of Different Combinations of Irradiation Mode and Jaw Width in Helical Tomotherapy for Nasopharyngeal Carcinoma.

Front Oncol 2020 23;10:598. Epub 2020 Apr 23.

Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China.

To aid in the selection of a suitable combination of irradiation mode and jaw width in helical tomotherapy (HT) for the treatment of nasopharyngeal carcinoma (NPC). Twenty patients with NPC who underwent radiotherapy were retrospectively selected. Four plans using a jaw width of 2.5 or 5-cm in dynamic jaw (DJ) or fix jaw (FJ) modes for irradiation were designed (2.5DJ, 2.5FJ, 5.0DJ, and 5.0FJ). The dose parameters of planning target volume (PTV) and organs at risk (OARs) of the plans were compared and analyzed, as well as the beam on time (BOT) and monitor unit (MU). The plans in each group were ranked by scoring the doses received by the OARs and the superity was assessed in combination with the planned BOT and MU. The prescribed dose coverage of PTV met the clinical requirements for all plans in the four groups. The groups using a 2.5-cm jaw width or a DJ mode provided better protection to most OARs, particularly for those at the longitudinal edges of the PTV ( < 0.05). The 2.5DJ group had the best ranking for OAR-dose, followed by the 2.5FJ and 5.0DJ groups with a same score. The BOT and MU of the groups using a 5.0-cm jaw width reduced nearly 45% comparing to those of the 2.5-cm jaw groups. 2.5DJ has the best dose distribution, while 5.0DJ has satisfactory dose distribution and less BOT and MU that related to the leakage dose. Both 2.5DJ or 5DJ were recommended for HT treatment plan for NPC based on the center workload.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3389/fonc.2020.00598DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7190867PMC
April 2020

Treating Keloids With Intralesional 5-Fluorouracil and Triamcinolone Acetonide: Aren't We There Yet?

J Cutan Med Surg 2020 Mar/Apr;24(2):205-206

153006 Department of Dermatology and Skin Science, University of British Columbia, Vancouver, BC, Canada.

View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1177/1203475419897577DOI Listing
February 2021

Dosiomics improves prediction of locoregional recurrence for intensity modulated radiotherapy treated head and neck cancer cases.

Oral Oncol 2020 05 6;104:104625. Epub 2020 Mar 6.

Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China. Electronic address:

Objectives: To investigate whether dosiomics can benefit to IMRT treated patient's locoregional recurrences (LR) prediction through a comparative study on prediction performance inspection between radiomics methods and that integrating dosiomics in head and neck cancer cases.

Materials And Methods: A cohort of 237 patients with head and neck cancer from four different institutions was obtained from The Cancer Imaging Archive and utilized to train and validate the radiomics-only prognostic model and integrate the dosiomics prognostic model. For radiomics, the radiomics features were initially extracted from images, including CTs and PETs, and selected on the basis of their concordance index (CI) values, then condensed via principle component analysis. Lastly, multivariate Cox proportional hazards regression models were constructed with class-imbalance adjustment as the LR prediction models by inputting those condensed features. For dosiomics integration model establishment, the initial features were similar, but with additional 3-dimensional dose distribution from radiation treatment plans. The CI and the Kaplan-Meier curves with log-rank analysis were used to assess and compare these models.

Results: Observed from the independent validation dataset, the CI of the model for dosiomics integration (0.66) was significantly different from that for radiomics (0.59) (Wilcoxon test, p=5.9×10). The integrated model successfully classified the patients into high- and low-risk groups (log-rank test, p=2.5×10), whereas the radiomics model was not able to provide such classification (log-rank test, p=0.37).

Conclusion: Dosiomics can benefit in predicting the LR in IMRT-treated patients and should not be neglected for related investigations.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.oraloncology.2020.104625DOI Listing
May 2020

Multi-sequence MR image-based synthetic CT generation using a generative adversarial network for head and neck MRI-only radiotherapy.

Med Phys 2020 Apr 26;47(4):1880-1894. Epub 2020 Feb 26.

Department of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, Guangdong, China.

Purpose: The purpose of this study is to investigate the effect of different magnetic resonance (MR) sequences on the accuracy of deep learning-based synthetic computed tomography (sCT) generation in the complex head and neck region.

Methods: Four sequences of MR images (T1, T2, T1C, and T1DixonC-water) were collected from 45 patients with nasopharyngeal carcinoma. Seven conditional generative adversarial network (cGAN) models were trained with different sequences (single channel) and different combinations (multi-channel) as inputs. To further verify the cGAN performance, we also used a U-net network as a comparison. Mean absolute error, structural similarity index, peak signal-to-noise ratio, dice similarity coefficient, and dose distribution were evaluated between the actual CTs and sCTs generated from different models.

Results: The results show that the cGAN model with multi-channel (i.e., T1 + T2 + T1C + T1DixonC-water) as input to predict sCT achieves higher accuracy than any single MR sequence model. The T1-weighted MR model achieves better results than T2, T1C, and T1DixonC-water models. The comparison between cGAN and U-net shows that the sCTs predicted by cGAN retains additional image details are less blurred and more similar to the actual CT.

Conclusions: Conditional generative adversarial network with multiple MR sequences as model input shows the best accuracy. The T1-weighted MR images provide sufficient image information and are suitable for sCT prediction in clinical scenarios with limited acquisition sequences or limited acquisition time.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/mp.14075DOI Listing
April 2020

Integrating cover crops with chicken grazing to improve soil nitrogen in rice fields and increase economic output.

Sci Total Environ 2020 Apr 27;713:135218. Epub 2019 Nov 27.

The Ecosystems Center, Marine Biological Laboratory, Woods Hole, MA 02543, USA. Electronic address:

Winter fallow is important for renewing and improving soil fertility under double-cropping rice systems, such as those in southern China. Using a regenerative farming technology of integrating grass-chicken farming in a winter fallow field, we investigated soil nitrogen conversion and assessed the agricultural economic benefits of the whole farmland ecosystem. To test the effects of chicken grazing on the fallow system, we established field treatments involving adding chickens to a field planted with the cover crops, including cover milk vetch (Astragalus sinicus) with chicken grazing treatment (MC) and cover ryegrass (Lolium spp.) with chicken grazing (RC); cover crops only, including cover milk vetch (Astragalus sinicus) treatment (M) and cover ryegrass (Lolium spp.) (R); and a bare fallow field treatment (CK). We found that both cover crops (M and R) and cover crops with chicken grazing (MC and RC) increased nitrate, ammonium, dissolved organic nitrogen, and total nitrogen contents, and the increase was higher in MC and RC treatments. We also observed increased straw biomass and grain yield in the all four treatments, with more increases with chicken treatments as compared with CK. On the economic profits, MC increased by 101.72% and RC increased by 104.12% as compared with CK, while R increased by 5.19% and M reduced by 1.86% as compared with CK. The nitrogen transfer rate (the output/input ratio) of MC, RC, M, and R increased by 66.71%, 71.50%, 65.97%, and 59.97%, respectively, while the nitrogen accumulation rate (input-output) of MC, RC, M, and R increased by 480.56%, 612.98%, 356.74%, and 267.65%, respectively. Our study demonstrates that retaining nitrogen and gaining economic profit by integrating cover crops with chicken grazing is potentially more sustainable than adding cover crops alone. We further suggest that using the integrated grass-livestock farming technology can reduce environmental damage caused by commercial fertilizers.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.scitotenv.2019.135218DOI Listing
April 2020

Digital breast tomosynthesis versus digital mammography: integration of image modalities enhances deep learning-based breast mass classification.

Eur Radiol 2020 Feb 5;30(2):778-788. Epub 2019 Nov 5.

School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, Guangdong, China.

Objective: To evaluate the impact of utilizing digital breast tomosynthesis (DBT) or/and full-field digital mammography (FFDM), and different transfer learning strategies on deep convolutional neural network (DCNN)-based mass classification for breast cancer.

Methods: We retrospectively collected 441 patients with both DBT and FFDM on which regions of interest (ROIs) covering the malignant, benign and normal tissues were extracted for DCNN training and validation. Experiments were conducted for tasks in distinguishing malignant/benign/normal: (1) classification capabilities of DBT vs FFDM and the role of transfer learning were validated on 2D-DCNN; (2) different strategies of combining DBT and FFDM and the associated impacts on classification were explored; (3) 2D-DCNN and 3D-DCNN trained from scratch with volumetric DBT were compared.

Results: 2D-DCNN with transfer learning outperformed that without for DBT in distinguishing malignant (ΔAUC = 0.059 ± 0.009, p < 0.001), benign (ΔAUC = 0.095 ± 0.010, p < 0.001) and normal tissue (ΔAUC = 0.042 ± 0.004, p < 0.001) (paired samples t test). 2D-DCNN trained on DBT (with transfer learning) achieved higher accuracy than those on FFDM (malignant: ΔAUC = 0.014 ± 0.014, p = 0.037; benign: ΔAUC = 0.031 ± 0.006, p < 0.001; normal: ΔAUC = 0.017 ± 0.004, p < 0.001) (independent samples t test). The 2D-DCNN employing both DBT and FFDM for training achieved better performances in benign (FFDM: ΔAUC = 0.010 ± 0.008, p < 0.001; DBT: ΔAUC = 0.009 ± 0.005, p < 0.001) and normal (FFDM: ΔAUC = 0.005 ± 0.003, p < 0.001; DBT: ΔAUC = 0.002 ± 0.002, p < 0.001) (related samples Friedman test). The 3D-DCNN and 2D-DCNN trained from scratch with DBT only produced moderate classification.

Conclusions: Transfer learning facilitates mass classification for both DBT and FFDM, and DBT outperforms FFDM when equipped with transfer learning. Integrating DBT and FFDM in DCNN training enhances mass classification accuracy for breast cancer.

Key Points: • Transfer learning facilitates mass classification for both DBT and FFDM, and the DBT-based DCNN outperforms the FFDM-based DCNN when equipped with transfer learning. • Integrating DBT and FFDM in DCNN training enhances breast mass classification accuracy. • 3D-DCNN/2D-DCNN trained from scratch with volumetric DBT but without transfer learning only produce moderate mass classification result.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/s00330-019-06457-5DOI Listing
February 2020

Postoperative glioma segmentation in CT image using deep feature fusion model guided by multi-sequence MRIs.

Eur Radiol 2020 Feb 24;30(2):823-832. Epub 2019 Oct 24.

School of Biomedical Engineering, Southern Medical University, No. 1838 Guangzhou Northern Avenue, Baiyun District, Guangzhou, 510515, Guangdong, China.

Objectives: Computed tomography (CT) and magnetic resonance imaging (MRI) are the most commonly selected methods for imaging gliomas. Clinically, radiotherapists always delineate the CT glioma region with reference to multi-modal MR image information. On this basis, we develop a deep feature fusion model (DFFM) guided by multi-sequence MRIs for postoperative glioma segmentation in CT images.

Methods: DFFM is a multi-sequence MRI-guided convolutional neural network (CNN) that iteratively learns the deep features from CT images and multi-sequence MR images simultaneously by utilizing a multi-channel CNN architecture, and then combines these two deep features together to produce the segmentation result. The whole network is optimized together via a standard back-propagation. A total of 59 CT and MRI datasets (T1/T2-weighted FLAIR, T1-weighted contrast-enhanced, T2-weighted) of postoperative gliomas as tumor grade II (n = 24), grade III (n = 18), or grade IV (n = 17) were included. Dice coefficient (DSC), precision, and recall were used to measure the overlap between automated segmentation results and manual segmentation. The Wilcoxon signed-rank test was used for statistical analysis.

Results: DFFM showed a significantly (p < 0.01) higher DSC of 0.836 than U-Net trained by single CT images and U-Net trained by stacking the CT and multi-sequence MR images, which yielded 0.713 DSC and 0.818 DSC, respectively. The precision values showed similar behavior as DSC. Moreover, DSC and precision values have no significant statistical difference (p > 0.01) with difference grades.

Conclusions: DFFM enables the accurate automated segmentation of CT postoperative gliomas of profit guided by multi-sequence MR images and may thus improve and facilitate radiotherapy planning.

Key Points: • A fully automated deep learning method was developed to segment postoperative gliomas on CT images guided by multi-sequence MRIs. • CT and multi-sequence MR image integration allows for improvements in deep learning postoperative glioma segmentation method. • This deep feature fusion model produces reliable segmentation results and could be useful in delineating GTV in postoperative glioma radiotherapy planning.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/s00330-019-06441-zDOI Listing
February 2020

Systematic analysis of the impact of imaging noise on dual-energy CT-based proton stopping power ratio estimation.

Med Phys 2019 May 1;46(5):2251-2263. Epub 2019 Apr 1.

Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.

Purpose: Dual-energy CT (DECT) has been shown to have a great potential in reducing the uncertainty in proton stopping power ratio (SPR) estimation, when compared to current standard method - the stoichiometric method based on single-energy CT (SECT). However, a few recent studies indicated that imaging noise may have a substantial impact on the performance of the DECT-based approach, especially at a high noise level. The goal of this study is to quantify the uncertainty in SPR and range estimation caused by noise in the DECT-based approach under various conditions.

Methods: Two widely referred parametric DECT methods were studied: the Hünemohr-Saito (HS) method and the Bourque method. Both methods were calibrated using Gammex tissue substitute inserts scanned on the Siemens Force DECT scanner. An energy pair of 80 and 150 kVp with a tin filter was chosen to maximize the spectral separation. After calibrating the model with the Gammex phantom, CT numbers were synthesized using the density and elemental composition from ICRU 44 human tissues to be used as a reference, in order to evaluate the impact of noise alone while putting aside other sources of uncertainty. Gaussian noise was introduced to the reference CT numbers and its impact was measured with the difference between estimated SPR and its noiseless reference SPR. The uncertainty caused by noise was divided into two independent categories: shift of the mean SPR and variation of SPR. Their overall impact on range uncertainty was evaluated on homogeneous and heterogeneous tissue samples of various water equivalent path lengths (WEPL).

Results: Due to the algorithms being nonlinear and/or having hard thresholds in the CT number to SPR mapping, noise in the CT numbers induced a shift in the mean SPR from its noiseless reference SPR. The degree of the mean shift was dependent on the algorithm and tissue type, but its impact on the SPR uncertainty was mostly small compared to the variation. All mean shifts observed in this study were within 0.5% at a noise level of 2%. The ratio of the influence of variation to mean shift was mostly greater than 1, indicating that variation more likely determined the uncertainty caused by noise. Overall, the range uncertainty (95th percentile) caused by noise was within 1.2% and 1.0% for soft and bone tissues, respectively, at 2% noise with 50 voxels. This value can be considered an upper limit as more voxels and lower noise level rapidly decreased the uncertainty.

Conclusions: We have systematically evaluated the impact of noise to the DECT-based SPR estimation and identified under various conditions that the variation caused by noise is the dominant uncertainty-contributing component. We conclude that, based on the noise level and tumor depth, it is important to estimate and include the uncertainty due to noise in estimating the overall range uncertainty before implementing a small margin in the range of 1%.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/mp.13493DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6510613PMC
May 2019

Metropolis Monte Carlo simulation scheme for fast scattered X-ray photon calculation in CT.

Opt Express 2019 Jan;27(2):1262-1275

Monte Carlo (MC) method is commonly considered as the most accurate approach for particle transport simulation because of its capability to precisely model physics interactions and simulation geometry. Conventionally, MC simulation is performed in a particle-by-particle fashion. In certain problems such as computing scattered X-ray photon signal at a detector of CT, the conventional simulation scheme suffers from low efficiency mainly due to the fact that abundant photons are simulated but do not reach the detector. The computational resources spent on those photons are therefore wasted. To solve this problem, this study develops a novel GPU-based Metropolis MC (gMMC) with a novel path-by-path simulation scheme and demonstrates its effectiveness in an example problem of scattered X-ray photon calculation in CT. In contrast to the conventional MC approach, gMMC samples an entire photon path extending from the X-ray source to the detector using Metropolis-Hasting algorithm. The path-by-path simulation scheme ensures contribution of every sampled event to the signal of interest, improving overall efficiency. We benchmark gMMC against an in-house developed GPU-based MC tool, gMCDRR, which performs simulations in the conventional particle-by-particle fashion. gMMC reaches speed up factors of 37~48 times in simple phantom cases and 20-34 times in real patient cases. The results calculated by gMCDRR and gMMC agree well with average differences < 3%.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1364/OE.27.001262DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6410917PMC
January 2019

Deep-learning based surface region selection for deep inspiration breath hold (DIBH) monitoring in left breast cancer radiotherapy.

Phys Med Biol 2018 Dec 12;63(24):245013. Epub 2018 Dec 12.

School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, People's Republic of China. Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America.

Deep inspiration breath hold (DIBH) with surface supervising is a common technique for cardiac dose reduction in left breast cancer radiotherapy. Surface supervision accuracy relies on the characteristics of surface region. In this study, a convolutional neural network (CNN) based automatic region-of-interest (ROI) selection method was proposed to select an optimal surface ROI for DIBH surface monitoring. The curvature entropy and the normal of each vertex on the breast cancer patient surface were calculated and formed as representative maps for ROI selection learning. 900 ROIs were randomly extracted from each patient's surface representative map, and the corresponding rigid ROI registration errors (REs) were calculated. The VGG-16 (a 16-layer network structure developed by Visual Geometry Group(VGG) from University of Oxford) pre-trained on a large natural image database ImageNet were fine-tuned using 27 thousand extracted ROIs and the corresponding REs from thirty patients. The RE prediction accuracy of the trained model was validated on additional ten patients. Satisfactory RE predictive accuracies were achieved with the root mean square error (RMSE)/mean absolute error (MAE) smaller than 1 mm/0.7 mm in translations and 0.45°/0.35° in rotations, respectively. The REs of the model selected ROIs on ten testing cases is close to the minimal predicted RE with mean RE differences  <1 mm and  <0.5° for translation and rotation, respectively. The proposed RE predictive model can be utilized for selecting a quasi-optimal ROI in left breast cancer DIBH radiotherapy (DIBH-RT).
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1088/1361-6560/aaf0d6DOI Listing
December 2018

A recursive ensemble organ segmentation (REOS) framework: application in brain radiotherapy.

Phys Med Biol 2019 01 11;64(2):025015. Epub 2019 Jan 11.

School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, People's Republic of China. Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America. School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, Guangdong 510275, People's Republic of China.

The aim of this work is to develop a novel recursive ensemble OARs segmentation (REOS) framework for accurate organs-at-risk (OARs) automatic segmentation. The REOS recursively segment individual OARs by ensembling images features extracted from an organ localization module and a contour detection module. Both modules are based on a 3D U-Net architecture. The organ localization module is trained for rough segmentation to localize a region of interest (ROI) that encompasses the to-be-delineated OAR, while the contour detection module is trained to segment the OAR within the identified ROI. In this study, the developed REOS framework is applied for brain radiotherapy on segmenting six OARs including the eyes, the brainstem (BS), the optical nerves and the chiasm. Eighty T1-weighted magnetic resonance images (MRI) from 80 brain cancer patients' cases with OARs' gold standard contours were collected for training and testing REOS. On 20 testing cases, the REOS achieve a high segmentation accuracy with Dice similarity coefficient (DSC) mean and standard deviation of 93.9%  ±  1.4%, 94.5%  ±  2.0%, 90.6%  ±  2.7%, on the left and right eyes and the BS, respectively. On small and segmentation-challenging organs, the left and right optical nerves and the chiasm, the REOS achieves DSC of 78.0%  ±  10.5%, 82.2%  ±  5.9% and 71.1%  ±  9.1%. The satisfactory performances demonstrated the effectiveness of the REOS in OARs segmentation.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1088/1361-6560/aaf83cDOI Listing
January 2019

Voxel-based automatic multi-criteria optimization for intensity modulated radiation therapy.

Radiat Oncol 2018 Dec 5;13(1):241. Epub 2018 Dec 5.

Department of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, Guangdong, China.

Background: Automatic multi-criteria optimization is necessary for intensity modulated radiation therapy (IMRT) because of low planning efficiency and large plan quality uncertainty in current clinical practice. Most studies focused on imitating dosimetrists' planning procedures to automate this process and ignored the fact that organ-based objective functions typically used in commercial treatment planning systems (such as dose-volume function) usually lead to sub-optimal plans. To guarantee the optimum results and to automate this process, we incorporate an improved automation strategy and a voxel-based optimization algorithm to generate a novel automatic multi-criteria optimization framework. We then evaluate it in clinical cases.

Methods: This novel automatic multi-criteria optimization framework incorporates a ranked priority-list based automatic constraints adjustment strategy and an in-house developed voxel-based optimization algorithm. Constraints are sequentially adjusted following a pre-defined priority list. Afterward, a voxel-based fluence map optimization (FMO) with an orientation to the newly updated constraints is launched to find a Pareto optimal solution. Loops of constraints adjustment are repeated until each of them could not be relaxed or tightened. The feasibility of the framework is evaluated with 10 automatic generated gynecology (GYN) cancer IMRT cases by comparing the dosimetric performance with the original.

Results: Plan quality improvement is observed for our automatic multi-criteria optimization method. Comparable DVHs are found for the planning target volume (PTV), but with better organs-at-risk (OAR) dose sparing. Among 13 evaluated dosimetric endpoints, 5 of them show significant improvements in automatically generated plans compared with the original plans. Investigation of improvement tendency during optimization exhibits gradual change as the optimization stage proceeds. An initial voxel-based optimization stage and in-low-priority dosimetric criteria tighten can significantly contribute to the optimization procedure.

Conclusions: We have successfully developed an automatic multi-criteria optimization framework that can dramatically reduce the current trial-and-error patterned planning workload while affording an efficient method to assure high plan quality consistency. This optimization framework is expected to greatly facilitate precise radiation therapy because of its advantages of planning efficiency and plan quality improvement.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s13014-018-1179-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6280392PMC
December 2018

[Accuracy of different image registration methods in image-guided adaptive brachytherapy for cervical cancer].

Nan Fang Yi Ke Da Xue Xue Bao 2018 Nov;38(11):1344-1348

School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.

Objective: To compare the accuracy of different methods for image registration in image-guided adaptive brachytherapy (IGABT) for cervical cancer.

Methods: The last treatment planning CT images (CT1) and the first treatment planning CT images (CT2) were acquired from 15 patients with cervical cancer and registered with different match image qualities (retained/removed catheter source in images) and different match regions [target only (S Group)/ interested organ structure (M Group)/body (L Group)] in Velocity3.2 software. The dice similarity coefficient (DSC) between the clinical target volumes (CTV) of the CT1 and CT2 images (CTVCT1 and CTVCT2, respectively) and between the organs-at-risk (OAR) of the two imaging datasets (OARCT1 and OARCT2, respectively) were used to evaluate the image registration accuracy.

Results: The auto-segmentation volume of the catheter source using Velocity software based on the CT threshold was the closest to the actual volume within the CT value range of 1700-1800 HU. In the retained group, the DSC for the OARs of was better than or equal to that of the removed group, and the DSC value of the rectum was significantly improved ( < 0.05). For comparison of different match regions, the high-risk target volume (HRCTV) and the low-risk target volume (IRCTV) had the best precision for registration of the target area, which was significantly greater than that of M group and L group ( < 0.05). The M group had better registration accuracy of the target area and the best accuracy for the OARs. The DSC values of the bladder and rectum were significantly better than those of the other two groups ( < 0.05).

Conclusions: The CT value range of 1700-1800 HU is optimal for automatic image segmentation using Velocity software. Automatic segmentation and shielding the volume of the catheter source can improve the image quality. We recommend the use of interested organ structures regions for image registration in image-guided adaptive brachytherapy for cervical cancer.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.12122/j.issn.1673-4254.2018.11.11DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6744129PMC
November 2018

MULTI-ENERGY CONE-BEAM CT RECONSTRUCTION WITH A SPATIAL SPECTRAL NONLOCAL MEANS ALGORITHM.

SIAM J Imaging Sci 2018 8;11(2):1205-1229. Epub 2018 May 8.

Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75235, USA.

Multi-energy computed tomography (CT) is an emerging medical image modality with a number of potential applications in diagnosis and therapy. However, high system cost and technical barriers obstruct its step into routine clinical practice. In this study, we propose a framework to realize multi-energy cone beam CT (ME-CBCT) on the CBCT system that is widely available and has been routinely used for radiotherapy image guidance. In our method, a kVp switching technique is realized, which acquires x-ray projections with kVp levels cycling through a number of values. For this kVp-switching based ME-CBCT acquisition, x-ray projections of each energy channel are only a subset of all the acquired projections. This leads to an undersampling issue, posing challenges to the reconstruction problem. We propose a spatial spectral non-local means (ssNLM) method to reconstruct ME-CBCT, which employs image correlations along both spatial and spectral directions to suppress noisy and streak artifacts. To address the intensity scale difference at different energy channels, a histogram matching method is incorporated. Our method is different from conventionally used NLM methods in that spectral dimension is included, which helps to effectively remove streak artifacts appearing at different directions in images with different energy channels. Convergence analysis of our algorithm is provided. A comprehensive set of simulation and real experimental studies demonstrate feasibility of our ME-CBCT scheme and the capability of achieving superior image quality compared to conventional filtered backprojection-type (FBP) and NLM reconstruction methods.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1137/17M1123237DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6173488PMC
May 2018

Multienergy element-resolved cone beam CT (MEER-CBCT) realized on a conventional CBCT platform.

Med Phys 2018 Oct 22;45(10):4461-4470. Epub 2018 Sep 22.

Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.

Purpose: Cone beam CT (CBCT) has been widely used in radiation therapy. However, its main application is still to acquire anatomical information for patient positioning. This study proposes a multienergy element-resolved (MEER) CBCT framework that employs energy-resolved data acquisition on a conventional CBCT platform and then simultaneously reconstructs images of x-ray attenuation coefficients, electron density relative to water (rED), and elemental composition (EC) to support advanced applications.

Methods: The MEER-CBCT framework is realized on a Varian TrueBeam CBCT platform using a kVp-switching scanning scheme. A simultaneous image reconstruction and elemental decomposition model is formulated as an optimization problem. The objective function uses a least square term to enforce fidelity between x-ray attenuation coefficients and projection measurements. Spatial regularization is introduced via sparsity under a tight wavelet-frame transform. Consistency is imposed among rED, EC, and attenuation coefficients and inherently serves as a regularization term along the energy direction. The EC is further constrained by a sparse combination of ECs in a dictionary containing tissues commonly existing in humans. The optimization problem is solved by a novel alternating-direction minimization scheme. The MEER-CBCT framework was tested in a simulation study using an NCAT phantom and an experimental study using a Gammex phantom.

Results: MEER-CBCT framework was successfully realized on a clinical Varian TrueBeam onboard CBCT platform with three energy channels of 80, 100, and 120 kVp. In the simulation study, the attenuation coefficient image achieved a structural similarity index of 0.98, compared to 0.61 for the image reconstructed by the conventional conjugate gradient least square (CGLS) algorithm, primarily because of reduction in artifacts. In the experimental study, the attenuation image obtained a contrast-to-noise ratio ≥60, much higher than that of CGLS results (~16) because of noise reduction. The median errors in rED and EC were 0.5% and 1.4% in the simulation study and 1.4% and 2.3% in the experimental study.

Conclusion: We proposed a novel MEER-CBCT framework realized on a clinical CBCT platform. Simulation and experimental studies demonstrated its capability to simultaneously reconstruct x-ray attenuation coefficient, rED, and EC images accurately.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1002/mp.13169DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6553481PMC
October 2018

Patch Based Grid Artifact Suppressing in Digital Mammography.

Biomed Res Int 2018 12;2018:9727259. Epub 2018 Aug 12.

School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.

The mammography is the first choice of breast cancer screening, which has proven to be the most effective screening method. An antiscatter grid is usually employed to enhance the contrast of image by absorbing unexpected scattered signals. However, the grid pattern casts shadows and grid artifacts, which severely degrade the image quality. To solve the problem, we propose the patch based frequency signal filtering for fast grid artifacts suppressing. As opposed to whole image processing synchronously, the proposed method divides image into a number of blocks for tuning filter simultaneously, which reduces the frequency interference among image blocks and saves computation time by multithread processing. Moreover, for mitigating grid artifacts more precisely, characteristic peak detection is employed in each block automatically, which can accurately identify the location of the antiscatter grid and its motion pattern. Qualitative and quantitative studies were performed on simulation and real machine data to validate the proposed method. The results show great potential for fast suppressing grid artifacts and generating high quality of digital mammography.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1155/2018/9727259DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6109478PMC
November 2018

Physician workforce planning in Ontario must move from short-term reactivity to long-term proactivity.

Can Med Educ J 2018 May 31;9(2):e84-e88. Epub 2018 May 31.

Department of Ophthalmology and Vision Sciences, University of Toronto, Ontario, Canada.

View Article and Find Full Text PDF

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6044305PMC
May 2018

Investigating rectal toxicity associated dosimetric features with deformable accumulated rectal surface dose maps for cervical cancer radiotherapy.

Radiat Oncol 2018 Jul 6;13(1):125. Epub 2018 Jul 6.

School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, Guangdong, China.

Background: Better knowledge of the dose-toxicity relationship is essential for safe dose escalation to improve local control in cervical cancer radiotherapy. The conventional dose-toxicity model is based on the dose volume histogram, which is the parameter lacking spatial dose information. To overcome this limit, we explore a comprehensive rectal dose-toxicity model based on both dose volume histogram and dose map features for accurate radiation toxicity prediction.

Methods: Forty-two cervical cancer patients treated with combined external beam radiotherapy (EBRT) and brachytherapy (BT) were retrospectively studied, including 12 with Grade ≥ 2 rectum toxicity and 30 patients with Grade 0-1 toxicity (non-toxicity patients). The cumulative equivalent 2-Gy rectal surface dose was deformably summed using the deformation vector fields obtained through a recent developed local topology preserved non-rigid point matching algorithm. The cumulative three-dimensional (3D) dose was flattened and mapped to a two-dimensional (2D) plane to obtain the rectum surface dose map (RSDM). The dose volume parameters (DVPs) were calculated from the 3D rectum surface, while the texture features and the dose geometric parameters (DGPs) were extracted from the 2D RSDM. Representative features further computed from DVPs, textures and DGPs by principle component analysis (PCA) and statistical analysis were respectively fed into a support vector machine equipped with a sequential feature selection procedure. The predictive powers of the representative features were compared with the GEC-ESTRO dosimetric parameters D.

Results: Satisfactory predictive accuracy of sensitivity 74.75 and 84.75%, specificity 72.67 and 79.87%, and area under the receiver operating characteristic curve (AUC) 0.82 and 0.91 were respectively achieved by the PCA features and statistical significant features, which were superior to the D (AUC 0.71). The relative area in dose levels of 64Gy, 67Gy, 68Gy, 87Gy, 88Gy and 89Gy, perimeters in dose levels of 89Gy, as well as two texture features were ranked as the important factors that were closely correlated with rectal toxicity.

Conclusions: Our extensive experimental results have demonstrated the feasibility of the proposed scheme. A future large patient cohort study is still needed for model validation.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1186/s13014-018-1068-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6035458PMC
July 2018

Online virtual cases to teach resource stewardship.

Clin Teach 2019 06 11;16(3):220-225. Epub 2018 Jun 11.

Department of Rheumatology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada.

Background: As health care costs rise, medical education must focus on high-value clinical decision making. To teach and assess efficient resource use in rheumatology, online virtual interactive cases (VICs) were developed to simulate real patient encounters to increase price transparency and reinforce cost consciousness. To teach and assess efficient resource use in rheumatology, online virtual interactive cases (VICs) were developed METHODS: The VIC modules were distributed to a sample of medical students and internal medicine residents, who were required to assess patients, order appropriate investigations, develop differential diagnoses and formulate management plans. Each action was associated with a time and price, with the totals compared against ideals. Trainees were evaluated not only on their diagnosis and patient management, but also on the total time, cost and value of their selected workup. Trainee responses were tracked anonymously, with opportunity to provide feedback at the end of each case.

Results: Seventeen medical trainees completed a total of 48 VIC modules. On average, trainees spent CAN $227.52 and 68 virtual minutes on each case, which was lower than expected. This may have been the result of a low management score of 52.4%, although on average 92.0% of participants in each case achieved the correct diagnosis. In addition, 85.7% felt more comfortable working up similar cases, and 57.1% believed that the modules increased their ability to appropriately order cost-conscious rheumatology investigations.

Discussion: Our initial assessment of the VIC rheumatology modules was positive, supporting their role as an effective tool in teaching an approach to rheumatology patients, with an emphasis on resource stewardship. Future directions include the expansion of cases, based on feedback, wider dissemination and an evaluation of learning retention.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1111/tct.12804DOI Listing
June 2019

Clinical prediction of delayed cerebral ischemia in aneurysmal subarachnoid hemorrhage.

J Neurosurg 2018 Jun 1:1-8. Epub 2018 Jun 1.

5Division of Neurology, The Ottawa Hospital, Ottawa, Ontario, Canada.

OBJECTIVEThe aim of this study was to derive a clinically applicable decision rule using clinical, radiological, and laboratory data to predict the development of delayed cerebral ischemia (DCI) in aneurysmal subarachnoid hemorrhage (aSAH) patients.METHODSPatients presenting over a consecutive 9-year period with subarachnoid hemorrhage (SAH) and at least 1 angiographically evident aneurysm were included. Variables significantly associated with DCI in univariate analysis underwent multivariable logistic regression. Using the beta coefficients, points were assigned to each predictor to establish a scoring system with estimated risks. DCI was defined as neurological deterioration attributable to arterial narrowing detected by transcranial Doppler ultrasonography, CT angiography, MR angiography, or catheter angiography, after exclusion of competing diagnoses.RESULTSOf 463 patients, 58% experienced angiographic vasospasm with an overall DCI incidence of 21%. Age, modified Fisher grade, and ruptured aneurysm location were significantly associated with DCI. This combination of predictors had a greater area under the receiver operating characteristic curve than the modified Fisher grade alone (0.73 [95% CI 0.67-0.78] vs 0.66 [95% CI 0.60-0.71]). Patients 70 years or older with modified Fisher grade 0 or 1 SAH and a posterior circulation aneurysm had the lowest risk of DCI at 1.2% (0 points). The highest estimated risk was 38% (17 points) in patients 40-59 years old with modified Fisher grade 4 SAH following rupture of an anterior circulation aneurysm.CONCLUSIONSAmong patients presenting with aSAH, this score-based clinical prediction tool exhibits increased accuracy over the modified Fisher grade alone and may serve as a useful tool to individualize DCI risk.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.3171/2018.1.JNS172715DOI Listing
June 2018

Systemic Monotherapy Treatments for Generalized Pustular Psoriasis: A Systematic Review.

J Cutan Med Surg 2018 Nov/Dec;22(6):591-601. Epub 2018 Apr 30.

3 Faculty of Medicine, University of Toronto, Toronto, ON, Canada.

Generalized pustular psoriasis (GPP) is a rare but serious and difficult to treat cutaneous disease, with high morbidity and mortality rates. Despite the numerous treatment regimens available, the overall quality of evidence-based research is limited with a lack of an algorithmic approach available. In this review, we aim to evaluate the current level of evidence regarding the efficacy and safety/tolerability of systemic monotherapies available in the treatment of GPP. A comprehensive MEDLINE, EMBASE, and PubMed search of clinical studies examining systemic monotherapy treatment options for GPP was conducted. In total, 31 studies met eligibility criteria. Described treatment modalities included retinoids, cyclosporine, biologics, and dapsone. Despite the lack of high-quality evidence or a well-accepted treatment algorithm for GPP, systemic retinoids, cyclosporine, biologics, and dapsone are all possible first-line agents, with retinoids being one of the best-supported treatment options and biologics as an emerging therapeutic field with great potential requiring additional data. However, the final choice of treatment should be considered within the unique context of each patient.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1177/1203475418773358DOI Listing
January 2019

Cutaneous lymphoid hyperplasia (pseudolymphoma).

CMAJ 2018 04;190(13):E398

Faculty of Medicine (Zhou), University of Ottawa, Ottawa, Ont.; Division of Dermatology (Mistry), Department of Medicine, University of Toronto, Toronto, Ont.

View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1503/cmaj.170812DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5880648PMC
April 2018

Influence of tube potential on quantitative coronary plaque analyses by low radiation dose computed tomography: a phantom study.

Int J Cardiovasc Imaging 2018 Aug 26;34(8):1315-1322. Epub 2018 Mar 26.

Department of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, Guangdong, China.

Previous studies have shown that employing the low dose computed tomography (CT) technique based on low tube potential reduces the radiation dose required for the coronary artery examination protocol. However, low tube potential may adversely influence the CT number of plaque composition. Therefore, we aimed to determine whether quantitative atherosclerotic plaque analysis by a multi-slice, low radiation dose CT protocol using 80 kilovolts (kV) yields results comparable to those of the standard 120 kV protocol. Artificial plaque samples (n = 17) composed of three kinds of plaque were scanned at 120 and 80 kV. Relative low-density and medium-density plaque component volumes obtained by three protocols (80 kV, 60 Hounsfield units [HU] threshold; 120 kV, 60 HU threshold; and 80 kV, 82 HU threshold) were compared. Using the 60 HU threshold, relative volume of the low-density plaque component obtained at 80 kV was lower than that obtained at 120 kV (27 ± 3% vs. 51 ± 5%, P < 0.001), whereas relative volume of the medium-density plaque component obtained at 80 kV was higher than that obtained at 120 kV (73 ± 3% vs. 48 ± 5%, P < 0.001). By contrast, no significant difference in relative volume obtained at 80 kV (82 HU threshold) versus 120 kV (60 HU threshold) was observed for either low-density (52 ± 5% vs. 51 ± 5%) or medium-density (48 ± 5% vs. 48 ± 5%) plaque component. Low tube potential may affect the accuracy of quantitative atherosclerotic plaque analysis. For our phantom test, 82 HU was the optimal threshold for scanning at 80 kV.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10554-018-1344-yDOI Listing
August 2018

Internal Motion Estimation by Internal-external Motion Modeling for Lung Cancer Radiotherapy.

Sci Rep 2018 02 27;8(1):3677. Epub 2018 Feb 27.

Department of Radiation Oncology, The University of Texas, Southwestern Medical Center, Dallas, Texas, 75390, USA.

The aim of this study is to develop an internal-external correlation model for internal motion estimation for lung cancer radiotherapy. Deformation vector fields that characterize the internal-external motion are obtained by respectively registering the internal organ meshes and external surface meshes from the 4DCT images via a recently developed local topology preserved non-rigid point matching algorithm. A composite matrix is constructed by combing the estimated internal phasic DVFs with external phasic and directional DVFs. Principle component analysis is then applied to the composite matrix to extract principal motion characteristics, and generate model parameters to correlate the internal-external motion. The proposed model is evaluated on a 4D NURBS-based cardiac-torso (NCAT) synthetic phantom and 4DCT images from five lung cancer patients. For tumor tracking, the center of mass errors of the tracked tumor are 0.8(±0.5)mm/0.8(±0.4)mm for synthetic data, and 1.3(±1.0)mm/1.2(±1.2)mm for patient data in the intra-fraction/inter-fraction tracking, respectively. For lung tracking, the percent errors of the tracked contours are 0.06(±0.02)/0.07(±0.03) for synthetic data, and 0.06(±0.02)/0.06(±0.02) for patient data in the intra-fraction/inter-fraction tracking, respectively. The extensive validations have demonstrated the effectiveness and reliability of the proposed model in motion tracking for both the tumor and the lung in lung cancer radiotherapy.
View Article and Find Full Text PDF

Download full-text PDF

Source
http://dx.doi.org/10.1038/s41598-018-22023-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5829085PMC
February 2018
-->