Veronika Flatten, Henry-Aravinth Devendranath, Janik Kroh, Matthias Witt, Kilian-Simon Baumann, Kenneth Gall, Bill Simon, Jörg Wulff, Andreas A. Schoenfeld
Background
Quality assurance (QA) on a daily basis is an essential task in radiotherapy. In pencil beam scanning proton therapy (PBS), there is a lack of available practical QA devices for routine daily QA in comparison to conventional radiotherapy.
Purpose
The aim was to characterize and evaluate a prototype for the task of daily QA routine for PBS with parameters recommended by the AAPM TG 224, that is, the dose output constancy, the spot position and the distal range verification. Furthermore, a time efficient calibration method for fast and reliable daily QA routine was established for the prototype.
Methods
First, a calibration routine was designed and evaluated, which characterizes the array response and allows for a conversion of the measured signal to clinically needed QA parameters. Finally, a time and resource efficient daily QA routine was developed and tested.
Results
The prototype array can distinguish spot position deviations with sub-millimeter accuracy, as well as changes in the spot size in terms of FWHM with a 2 sensitivity. The range and thus the energy can be evaluated at different depths also with sub-millimeter precision. After some training, the setup of the prototype device took roughly two minutes and the total beamtime was about one minute on cyclotron site and five minutes for synchrotrons.
Conclusions
A prototype for daily QA in spot scanning proton therapy was evaluated, which features a fast and easy setup and allows for measuring relevant beam parameters, typically within less than a minute of beam time. All QA parameters as recommended by the AAPM TG 224 report can be analyzed with sufficient accuracy.
{"title":"Evaluation of a prototype array for daily quality assurance in spot scanning proton therapy","authors":"Veronika Flatten, Henry-Aravinth Devendranath, Janik Kroh, Matthias Witt, Kilian-Simon Baumann, Kenneth Gall, Bill Simon, Jörg Wulff, Andreas A. Schoenfeld","doi":"10.1002/acm2.14454","DOIUrl":"10.1002/acm2.14454","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Quality assurance (QA) on a daily basis is an essential task in radiotherapy. In pencil beam scanning proton therapy (PBS), there is a lack of available practical QA devices for routine daily QA in comparison to conventional radiotherapy.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>The aim was to characterize and evaluate a prototype for the task of daily QA routine for PBS with parameters recommended by the AAPM TG 224, that is, the dose output constancy, the spot position and the distal range verification. Furthermore, a time efficient calibration method for fast and reliable daily QA routine was established for the prototype.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>First, a calibration routine was designed and evaluated, which characterizes the array response and allows for a conversion of the measured signal to clinically needed QA parameters. Finally, a time and resource efficient daily QA routine was developed and tested.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The prototype array can distinguish spot position deviations with sub-millimeter accuracy, as well as changes in the spot size in terms of FWHM with a 2<span></span><math>\u0000 <semantics>\u0000 <mo>%</mo>\u0000 <annotation>$%$</annotation>\u0000 </semantics></math> sensitivity. The range and thus the energy can be evaluated at different depths also with sub-millimeter precision. After some training, the setup of the prototype device took roughly two minutes and the total beamtime was about one minute on cyclotron site and five minutes for synchrotrons.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>A prototype for daily QA in spot scanning proton therapy was evaluated, which features a fast and easy setup and allows for measuring relevant beam parameters, typically within less than a minute of beam time. All QA parameters as recommended by the AAPM TG 224 report can be analyzed with sufficient accuracy.</p>\u0000 </section>\u0000 </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11540015/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142361552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mohamed A. Bahloul, Saima Jabeen, Sara Benoumhani, Habib Abdulmohsen Alsaleh, Zehor Belkhatir, Areej Al-Wabil
<div> <section> <h3> Background</h3> <p>Magnetic resonance imaging (MRI) and Computed tomography (CT) are crucial imaging techniques in both diagnostic imaging and radiation therapy. MRI provides excellent soft tissue contrast but lacks the direct electron density data needed to calculate dosage. CT, on the other hand, remains the gold standard due to its accurate electron density information in radiation therapy planning (RTP) but it exposes patients to ionizing radiation. Synthetic CT (sCT) generation from MRI has been a focused study field in the last few years due to cost effectiveness as well as for the objective of minimizing side-effects of using more than one imaging modality for treatment simulation. It offers significant time and cost efficiencies, bypassing the complexities of co-registration, and potentially improving treatment accuracy by minimizing registration-related errors. In an effort to navigate the quickly developing field of precision medicine, this paper investigates recent advancements in sCT generation techniques, particularly those using machine learning (ML) and deep learning (DL). The review highlights the potential of these techniques to improve the efficiency and accuracy of sCT generation for use in RTP by improving patient care and reducing healthcare costs. The intricate web of sCT generation techniques is scrutinized critically, with clinical implications and technical underpinnings for enhanced patient care revealed.</p> </section> <section> <h3> Purpose</h3> <p>This review aims to provide an overview of the most recent advancements in sCT generation from MRI with a particular focus of its use within RTP, emphasizing on techniques, performance evaluation, clinical applications, future research trends and open challenges in the field.</p> </section> <section> <h3> Methods</h3> <p>A thorough search strategy was employed to conduct a systematic literature review across major scientific databases. Focusing on the past decade's advancements, this review critically examines emerging approaches introduced from 2013 to 2023 for generating sCT from MRI, providing a comprehensive analysis of their methodologies, ultimately fostering further advancement in the field. This study highlighted significant contributions, identified challenges, and provided an overview of successes within RTP. Classifying the identified approaches, contrasting their advantages and disadvantages, and identifying broad trends were all part of the review's synthesis process.</p> </section> <section> <h3> Results</h3> <p>The review identifies various
{"title":"Advancements in synthetic CT generation from MRI: A review of techniques, and trends in radiation therapy planning","authors":"Mohamed A. Bahloul, Saima Jabeen, Sara Benoumhani, Habib Abdulmohsen Alsaleh, Zehor Belkhatir, Areej Al-Wabil","doi":"10.1002/acm2.14499","DOIUrl":"10.1002/acm2.14499","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Magnetic resonance imaging (MRI) and Computed tomography (CT) are crucial imaging techniques in both diagnostic imaging and radiation therapy. MRI provides excellent soft tissue contrast but lacks the direct electron density data needed to calculate dosage. CT, on the other hand, remains the gold standard due to its accurate electron density information in radiation therapy planning (RTP) but it exposes patients to ionizing radiation. Synthetic CT (sCT) generation from MRI has been a focused study field in the last few years due to cost effectiveness as well as for the objective of minimizing side-effects of using more than one imaging modality for treatment simulation. It offers significant time and cost efficiencies, bypassing the complexities of co-registration, and potentially improving treatment accuracy by minimizing registration-related errors. In an effort to navigate the quickly developing field of precision medicine, this paper investigates recent advancements in sCT generation techniques, particularly those using machine learning (ML) and deep learning (DL). The review highlights the potential of these techniques to improve the efficiency and accuracy of sCT generation for use in RTP by improving patient care and reducing healthcare costs. The intricate web of sCT generation techniques is scrutinized critically, with clinical implications and technical underpinnings for enhanced patient care revealed.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>This review aims to provide an overview of the most recent advancements in sCT generation from MRI with a particular focus of its use within RTP, emphasizing on techniques, performance evaluation, clinical applications, future research trends and open challenges in the field.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A thorough search strategy was employed to conduct a systematic literature review across major scientific databases. Focusing on the past decade's advancements, this review critically examines emerging approaches introduced from 2013 to 2023 for generating sCT from MRI, providing a comprehensive analysis of their methodologies, ultimately fostering further advancement in the field. This study highlighted significant contributions, identified challenges, and provided an overview of successes within RTP. Classifying the identified approaches, contrasting their advantages and disadvantages, and identifying broad trends were all part of the review's synthesis process.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The review identifies various ","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11539972/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142347140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abby Yashayaeva, Robert Lee MacDonald, James Robar, Amanda Cherpak
Purpose
The presence of metal implants can produce artifacts and distort Hounsfield units (HU) in patient computed tomography (CT) images. The purpose of this work was to characterize a novel metal artifact reduction (MAR) algorithm for reconstruction of CBCT images obtained by the HyperSight imaging system.
Methods
Three tissue-equivalent phantoms were fitted with materials commonly used in medical applications. The first consisted of a variety of metal samples centered within a solid water block, the second was an Advanced Electron Density phantom with metal rods, and the third consisted of hip prostheses positioned within a water tank. CBCT images of all phantoms were acquired and reconstructed using the MAR and iCBCT Acuros algorithms on the HyperSight system. The signal-to-noise ratio (SNR), artifact index (AI), structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and mean-square error (MSE) were computed to assess the image quality in comparison to artifact-free reference images. The mean HU at various VOI positions around the cavity was calculated to evaluate the artifact dependence on distance and angle from the center of the cavity. The artifact volume of the phantom (excluding the cavity) was estimated by summing the volume of all voxels with HU values outside the 5th and 95th percentiles of the phantom CBCT with no artifact.
Results
The SNR, AI, SSIM, PSNR, and MSE metrics demonstrated significantly higher similarity to baseline when using MAR compared to iCBCT Acuros for all high-density materials, except for aluminum. Mean HU returned to expected solid water background at a shorter distance from metal sample in the MAR images, and the standard deviation remained lower for the MAR images at all distances from the insert. The artifact volume decreased using the novel MAR algorithm for all metal samples excluding aluminum (p < 0.001) and all hip prostheses (p < 0.05).
Conclusion
Varian's HyperSight MAR reconstruction algorithm shows a reduction in metal artifact metrics, motivating the use of MAR reconstruction for patients with metal implants.
目的金属植入物的存在会在患者的计算机断层扫描（CT）图像中产生伪影并扭曲 Hounsfield 单位（HU）。这项工作的目的是鉴定一种用于重建 HyperSight 成像系统获取的 CBCT 图像的新型金属伪影减少 (MAR) 算法。第一个模型是以固体水块为中心的各种金属样本，第二个模型是带有金属棒的高级电子密度模型，第三个模型是放置在水箱中的髋关节假体。所有模型的 CBCT 图像都是使用 HyperSight 系统上的 MAR 和 iCBCT Acuros 算法采集和重建的。计算信噪比 (SNR)、伪影指数 (AI)、结构相似性指数 (SSIM)、峰值信噪比 (PSNR) 和均方误差 (MSE)，以评估与无伪影参考图像相比的图像质量。计算空腔周围不同 VOI 位置的平均 HU 值，以评估伪影与空腔中心距离和角度的关系。通过将 HU 值在无伪影的幻影 CBCT 第 5 和第 95 百分位数之外的所有体素的体积相加，估算出幻影（不包括空腔）的伪影体积。结果对于除铝以外的所有高密度材料，与 iCBCT Acuros 相比，使用 MAR 时的 SNR、AI、SSIM、PSNR 和 MSE 指标与基线的相似度明显更高。在 MAR 图像中，平均 HU 值在与金属样本距离较短时恢复到预期的固态水背景值，而在与插入物的所有距离上，MAR 图像的标准偏差仍然较低。结论瓦里安的 HyperSight MAR 重建算法显示金属伪影指标有所减少，这促使对使用金属植入物的患者使用 MAR 重建。
{"title":"Evaluation of a Metal Artifact Reduction Algorithm for Image Reconstruction on a Novel CBCT Platform","authors":"Abby Yashayaeva, Robert Lee MacDonald, James Robar, Amanda Cherpak","doi":"10.1002/acm2.14516","DOIUrl":"10.1002/acm2.14516","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>The presence of metal implants can produce artifacts and distort Hounsfield units (HU) in patient computed tomography (CT) images. The purpose of this work was to characterize a novel metal artifact reduction (MAR) algorithm for reconstruction of CBCT images obtained by the HyperSight imaging system.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Three tissue-equivalent phantoms were fitted with materials commonly used in medical applications. The first consisted of a variety of metal samples centered within a solid water block, the second was an Advanced Electron Density phantom with metal rods, and the third consisted of hip prostheses positioned within a water tank. CBCT images of all phantoms were acquired and reconstructed using the MAR and iCBCT Acuros algorithms on the HyperSight system. The signal-to-noise ratio (SNR), artifact index (AI), structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and mean-square error (MSE) were computed to assess the image quality in comparison to artifact-free reference images. The mean HU at various VOI positions around the cavity was calculated to evaluate the artifact dependence on distance and angle from the center of the cavity. The artifact volume of the phantom (excluding the cavity) was estimated by summing the volume of all voxels with HU values outside the 5th and 95th percentiles of the phantom CBCT with no artifact.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The SNR, AI, SSIM, PSNR, and MSE metrics demonstrated significantly higher similarity to baseline when using MAR compared to iCBCT Acuros for all high-density materials, except for aluminum. Mean HU returned to expected solid water background at a shorter distance from metal sample in the MAR images, and the standard deviation remained lower for the MAR images at all distances from the insert. The artifact volume decreased using the novel MAR algorithm for all metal samples excluding aluminum (<i>p</i> < 0.001) and all hip prostheses (<i>p</i> < 0.05).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Varian's HyperSight MAR reconstruction algorithm shows a reduction in metal artifact metrics, motivating the use of MAR reconstruction for patients with metal implants.</p>\u0000 </section>\u0000 </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.14516","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142266808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accurate segmentation of brain tumors from multimodal magnetic resonance imaging (MRI) holds significant importance in clinical diagnosis and surgical intervention, while current deep learning methods cope with situations of multimodal MRI by an early fusion strategy that implicitly assumes that the modal relationships are linear, which tends to ignore the complementary information between modalities, negatively impacting the model's performance. Meanwhile, long-range relationships between voxels cannot be captured due to the localized character of the convolution procedure.
Method
Aiming at this problem, we propose a multimodal segmentation network based on a late fusion strategy that employs multiple encoders and a decoder for the segmentation of brain tumors. Each encoder is specialized for processing distinct modalities. Notably, our framework includes a feature fusion module based on a 3D discrete wavelet transform aimed at extracting complementary features among the encoders. Additionally, a 3D global context-aware module was introduced to capture the long-range dependencies of tumor voxels at a high level of features. The decoder combines fused and global features to enhance the network's segmentation performance.
Result
Our proposed model is experimented on the publicly available BraTS2018 and BraTS2021 datasets. The experimental results show competitiveness with state-of-the-art methods.
Conclusion
The results demonstrate that our approach applies a novel concept for multimodal fusion within deep neural networks and delivers more accurate and promising brain tumor segmentation, with the potential to assist physicians in diagnosis.
{"title":"Brain tumor segmentation by combining MultiEncoder UNet with wavelet fusion","authors":"Yuheng Pan, Haohan Yong, Weijia Lu, Guoyan Li, Jia Cong","doi":"10.1002/acm2.14527","DOIUrl":"10.1002/acm2.14527","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background and objective</h3>\u0000 \u0000 <p>Accurate segmentation of brain tumors from multimodal magnetic resonance imaging (MRI) holds significant importance in clinical diagnosis and surgical intervention, while current deep learning methods cope with situations of multimodal MRI by an early fusion strategy that implicitly assumes that the modal relationships are linear, which tends to ignore the complementary information between modalities, negatively impacting the model's performance. Meanwhile, long-range relationships between voxels cannot be captured due to the localized character of the convolution procedure.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Method</h3>\u0000 \u0000 <p>Aiming at this problem, we propose a multimodal segmentation network based on a late fusion strategy that employs multiple encoders and a decoder for the segmentation of brain tumors. Each encoder is specialized for processing distinct modalities. Notably, our framework includes a feature fusion module based on a 3D discrete wavelet transform aimed at extracting complementary features among the encoders. Additionally, a 3D global context-aware module was introduced to capture the long-range dependencies of tumor voxels at a high level of features. The decoder combines fused and global features to enhance the network's segmentation performance.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Result</h3>\u0000 \u0000 <p>Our proposed model is experimented on the publicly available BraTS2018 and BraTS2021 datasets. The experimental results show competitiveness with state-of-the-art methods.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The results demonstrate that our approach applies a novel concept for multimodal fusion within deep neural networks and delivers more accurate and promising brain tumor segmentation, with the potential to assist physicians in diagnosis.</p>\u0000 </section>\u0000 </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.14527","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142267021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hussam Hameed Jassim, Hassan Ali Nedaie, Nooshin Banaee, Ghazale Geraily, Ali Kazemian, Danial Seifi Makrani
<div> <section> <h3> Purpose</h3> <p>Kilovoltage cone beam computed tomography (kVCBCT)-guided adaptive radiation therapy (ART) uses daily deformed CT (dCT), which is generated automatically through deformable registration methods. These registration methods may perform poorly in reproducing volumes of the target organ, rectum, and bladder during treatment. We analyzed the registration errors between the daily kVCBCTs and corresponding dCTs for these organs using the default optical flow algorithm and two registration procedures. We validated the effectiveness of these registration methods in replicating the geometry for dose calculation on kVCBCT for ART.</p> </section> <section> <h3> Methods</h3> <p>We evaluated three deformable image registration (DIR) methods to assess their registration accuracy and dose calculation effeciency in mapping target and critical organs. The DIR methods include (1) default intensity-based deformable registration, (2) hybrid deformable registration, and (3) a two-step deformable registration process. Each technique was applied to a computerized imaging reference system (CIRS) phantom (Model 062 M) and to five patients who received volumetric modulated arc therapy to the prostate. Registration accuracy was assessed using the 95% Hausdorff distance (HD<sub>95</sub>) and Dice similarity coefficient (DSC), and each method was compared with the intensity-based registration method. The improvement in the dCT image quality of the CIRS phantom and five patients was assessed by comparing dCT with kVCBCT. Image quality quantitative metrics for the phantom included the signal-to-noise ratio (SNR), uniformity, and contrast-to-noise ratio (CNR), whereas those for the patients included the mean absolute error (MAE), mean error, peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM). To determine dose metric differences, we used a dose-volume histogram (DVH) and 3.0%/0.3 mm gamma analysis to compare planning computed tomography (pCT) and kVCBCT recalculations with restimulated CT images used as a reference.</p> </section> <section> <h3> Results</h3> <p>The dCT images generated by the hybrid (dCT<sub>H</sub>) and two-step (dCT<sub>C</sub>) registration methods resulted in significant improvements compared to kVCBCT in the phantom model. Specifically, the SNR improved by 107% and 107.2%, the uniformity improved by 90% and 75%, and the CNR improved by 212.2% and 225.6 for dCT<sub>H</sub> and dCT<sub>C</sub> methods, respectively. For the patient images, the MAEs improved by 98% and 94%, the PSNRs improved by 16.3% and 22.9%, and the SSIMs improved by 1% and 1% in the dCT<sub>H</sub> and dCT<sub>C</sub> metho
{"title":"Evaluation of the geometric and dosimetric accuracies of deformable image registration of targets and critical organs in prostate CBCT-guided adaptive radiotherapy","authors":"Hussam Hameed Jassim, Hassan Ali Nedaie, Nooshin Banaee, Ghazale Geraily, Ali Kazemian, Danial Seifi Makrani","doi":"10.1002/acm2.14490","DOIUrl":"10.1002/acm2.14490","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>Kilovoltage cone beam computed tomography (kVCBCT)-guided adaptive radiation therapy (ART) uses daily deformed CT (dCT), which is generated automatically through deformable registration methods. These registration methods may perform poorly in reproducing volumes of the target organ, rectum, and bladder during treatment. We analyzed the registration errors between the daily kVCBCTs and corresponding dCTs for these organs using the default optical flow algorithm and two registration procedures. We validated the effectiveness of these registration methods in replicating the geometry for dose calculation on kVCBCT for ART.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We evaluated three deformable image registration (DIR) methods to assess their registration accuracy and dose calculation effeciency in mapping target and critical organs. The DIR methods include (1) default intensity-based deformable registration, (2) hybrid deformable registration, and (3) a two-step deformable registration process. Each technique was applied to a computerized imaging reference system (CIRS) phantom (Model 062 M) and to five patients who received volumetric modulated arc therapy to the prostate. Registration accuracy was assessed using the 95% Hausdorff distance (HD<sub>95</sub>) and Dice similarity coefficient (DSC), and each method was compared with the intensity-based registration method. The improvement in the dCT image quality of the CIRS phantom and five patients was assessed by comparing dCT with kVCBCT. Image quality quantitative metrics for the phantom included the signal-to-noise ratio (SNR), uniformity, and contrast-to-noise ratio (CNR), whereas those for the patients included the mean absolute error (MAE), mean error, peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM). To determine dose metric differences, we used a dose-volume histogram (DVH) and 3.0%/0.3 mm gamma analysis to compare planning computed tomography (pCT) and kVCBCT recalculations with restimulated CT images used as a reference.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The dCT images generated by the hybrid (dCT<sub>H</sub>) and two-step (dCT<sub>C</sub>) registration methods resulted in significant improvements compared to kVCBCT in the phantom model. Specifically, the SNR improved by 107% and 107.2%, the uniformity improved by 90% and 75%, and the CNR improved by 212.2% and 225.6 for dCT<sub>H</sub> and dCT<sub>C</sub> methods, respectively. For the patient images, the MAEs improved by 98% and 94%, the PSNRs improved by 16.3% and 22.9%, and the SSIMs improved by 1% and 1% in the dCT<sub>H</sub> and dCT<sub>C</sub> metho","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.14490","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142269762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Total body irradiation before bone marrow transplantation for hematological malignancies using Radixact, a high-precision radiotherapy machine, can potentially reduce side effects and the risk of secondary malignancies. However, stable control of couch speed is critical, and direct assessment methods outlined in quality assurance guidelines are lacking. This study aims to develop a real-time couch speed verification system for the Radixact.
Methods
The developed system used a linear encoder to measure couch speed directly. Accuracy was verified via a linear stage, comparing measurements with a laser distance sensor. After placing a phantom simulating the human body on the Radixact couch, the couch speed was verified using predefined speed plans.
Results
Operating the linear stage at 0.1, 0.5, and 1.0 mm/s revealed that the maximum position error of the developed verification system compared to the laser distance sensor was nearly equivalent to the distance resolution of the system (0.05 mm/pulse), with negligible average speed error. When the Radixact couch operated at 0.1, 0.5, and 1.0 mm/s, the values obtained by the verification system agreed with the theoretical values within the sampling period (0.01 s) and distance resolution (0.05 mm).
Conclusion
The verification system developed provides real-time monitoring of the speed of the Radixact table, ensuring treatment effectiveness and patient safety. It would guarantee the couch speed's soundness and contribute to the “visualization” of safety.
{"title":"Development of an external system for monitoring the couch speed in radiotherapy using continuous bed movement","authors":"Hidetoshi Shimizu, Osamu Nakamura, Koji Sasaki, Takahiro Aoyama, Tomoki Kitagawa, Takeshi Kodaira","doi":"10.1002/acm2.14497","DOIUrl":"10.1002/acm2.14497","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>Total body irradiation before bone marrow transplantation for hematological malignancies using Radixact, a high-precision radiotherapy machine, can potentially reduce side effects and the risk of secondary malignancies. However, stable control of couch speed is critical, and direct assessment methods outlined in quality assurance guidelines are lacking. This study aims to develop a real-time couch speed verification system for the Radixact.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The developed system used a linear encoder to measure couch speed directly. Accuracy was verified via a linear stage, comparing measurements with a laser distance sensor. After placing a phantom simulating the human body on the Radixact couch, the couch speed was verified using predefined speed plans.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Operating the linear stage at 0.1, 0.5, and 1.0 mm/s revealed that the maximum position error of the developed verification system compared to the laser distance sensor was nearly equivalent to the distance resolution of the system (0.05 mm/pulse), with negligible average speed error. When the Radixact couch operated at 0.1, 0.5, and 1.0 mm/s, the values obtained by the verification system agreed with the theoretical values within the sampling period (0.01 s) and distance resolution (0.05 mm).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The verification system developed provides real-time monitoring of the speed of the Radixact table, ensuring treatment effectiveness and patient safety. It would guarantee the couch speed's soundness and contribute to the “visualization” of safety.</p>\u0000 </section>\u0000 </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.14497","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142207489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Radixact system (Accuray Inc., Sunnyvale, CA) is the latest platform release based on the TomoTherapy technology. The most recent system does not apply a leaf latency model correction after plan optimization to ensure the correct MLC leaf-open time (LOT) agreement between the TPS and machine delivery. The MLC uses optical sensors to measure the delivered LOTs in real-time and individual leaf-specific latency corrections are made to ensure agreement. The aim of this study was to assess the performance of the Radixact MLC with leaf-specific latency correction using the optical sensor's real-time feedback. Specifically, the study statistically evaluated the MLC LOT errors observed from 290 plan-specific quality assurance (PSQA) measurements. Repeatability testing was performed to quantify the uncertainty in the MLC feedback system delivery by analyzing > 1300 delivered treatment fractions throughout the course of radiotherapy. The clinical impact was evaluated by estimating the resulting dose difference in the patient targets due to the measured plan latencies. Our study measured an average plan latency equal to 2.0 ± 0.4 ms (0.6% ± 0.2%) for 290 PSQAs. Repeatability tests showed a mean standard deviation in plan latencies measuring 0.05 ms (0.02%). The deviation from the TPS in the mean target dose due to the plan latencies was estimated to be 0.0% ± 0.2% (range: -0.7%–1.1%). The current MLC system with real-time optical sensor feedback is capable of accurately delivering the TPS-generated sinograms. Repeatability test results showed that the system allows for high reliability in daily sinogram delivery. The MLC latency deviations were shown to have minimal clinical impact on the overall target dosimetry.
{"title":"Performance of binary MLC using real-time optical sensor feedback system","authors":"Nathan A. Corradini, Cristina Vite, Patrizia Urso","doi":"10.1002/acm2.14506","DOIUrl":"10.1002/acm2.14506","url":null,"abstract":"<p>The Radixact system (Accuray Inc., Sunnyvale, CA) is the latest platform release based on the TomoTherapy technology. The most recent system does not apply a leaf latency model correction after plan optimization to ensure the correct MLC leaf-open time (LOT) agreement between the TPS and machine delivery. The MLC uses optical sensors to measure the delivered LOTs in real-time and individual leaf-specific latency corrections are made to ensure agreement. The aim of this study was to assess the performance of the Radixact MLC with leaf-specific latency correction using the optical sensor's real-time feedback. Specifically, the study statistically evaluated the MLC LOT errors observed from 290 plan-specific quality assurance (PSQA) measurements. Repeatability testing was performed to quantify the uncertainty in the MLC feedback system delivery by analyzing > 1300 delivered treatment fractions throughout the course of radiotherapy. The clinical impact was evaluated by estimating the resulting dose difference in the patient targets due to the measured plan latencies. Our study measured an average plan latency equal to 2.0 ± 0.4 ms (0.6% ± 0.2%) for 290 PSQAs. Repeatability tests showed a mean standard deviation in plan latencies measuring 0.05 ms (0.02%). The deviation from the TPS in the mean target dose due to the plan latencies was estimated to be 0.0% ± 0.2% (range: -0.7%–1.1%). The current MLC system with real-time optical sensor feedback is capable of accurately delivering the TPS-generated sinograms. Repeatability test results showed that the system allows for high reliability in daily sinogram delivery. The MLC latency deviations were shown to have minimal clinical impact on the overall target dosimetry.</p>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.14506","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142207488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
David P. Gierga, Tiffany Zewe, Su Yoon, Thomas R. Bortfeld
Compensation is a key component of career satisfaction and professional growth. A new compensation model was developed to provide a framework for career growth and a compensation ladder for medical physicists with clinical responsibilities in an academic radiation oncology department. The goals for the new model were: (1) create a market competitive plan to support recruitment and retention of top physics talent, (2) incentivize clinical effort, innovation, citizenship/professional service, and academic achievement, (3) provide compensation growth opportunities separate from medical school promotions, and (4) create consistent, transparent, and fair metrics applicable to all clinical physicists in the department. The model includes a base salary, and credits for board certification, clinical tier, leadership, and academic level. Further, metrics were developed to inform the clinical tier. Years of experience is not explicitly included in the model. The model was successfully implemented for clinical physicists in a relatively large academic radiation oncology department.
{"title":"A framework for medical physics compensation in an academic department","authors":"David P. Gierga, Tiffany Zewe, Su Yoon, Thomas R. Bortfeld","doi":"10.1002/acm2.14505","DOIUrl":"10.1002/acm2.14505","url":null,"abstract":"<p>Compensation is a key component of career satisfaction and professional growth. A new compensation model was developed to provide a framework for career growth and a compensation ladder for medical physicists with clinical responsibilities in an academic radiation oncology department. The goals for the new model were: (1) create a market competitive plan to support recruitment and retention of top physics talent, (2) incentivize clinical effort, innovation, citizenship/professional service, and academic achievement, (3) provide compensation growth opportunities separate from medical school promotions, and (4) create consistent, transparent, and fair metrics applicable to all clinical physicists in the department. The model includes a base salary, and credits for board certification, clinical tier, leadership, and academic level. Further, metrics were developed to inform the clinical tier. Years of experience is not explicitly included in the model. The model was successfully implemented for clinical physicists in a relatively large academic radiation oncology department.</p>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.14505","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142207506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hui Ma, Yangchen Liu, Hongxun Ye, Fei Gao, Songbing Qin
Background
This study explores the significance of computed tomography (CT) radiomic features, along with inflammation and nutrition biomarkers, in the prognosis of postoperative patients with T3N0M0 esophageal squamous cell carcinoma (ESCC). The study aims to construct a related nomogram.
Methods
A total of 114 patients were enrolled and randomly assigned to training and validation cohorts in a 7:3 ratio. Radiomic features were extracted from their preoperative chest-enhanced CT arterial images of the primary tumor, and inflammatory and nutritional indices, including neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), and prognostic nutritional index (PNI), were calculated based on laboratory data from the 3 days before surgery. Intra-class correlations coefficient (ICC) and least absolute shrinkage and selection operator (Lasso) were applied to screen valuable radiomics features predicting overall survival (OS), and the Rad-score was calculated. In the training cohort, univariate and multivariate Cox regression analyses identified independent prognostic factors, which were adopted to establish the nomogram.
Results
Eight radiomic features were selected for Rad-score calculation. Multivariate Cox regression revealed Rad-score, PNI, NLR, and PLR as independent prognostic factors for ESCC patients (p < 0.05). A nomogram was constructed based on these variables. The concordance index (C-index) for the nomogram was 0.797 (95% CI: 0.726–0.868) in the training cohort and 0.796 (95% CI: 0.702–0.890) in the validation cohort. Calibration curves indicated good calibration ability, and the receiver operating characteristic (ROC) analysis demonstrated superior discriminative ability for the nomogram in comparison to the Rad-score alone. Decision curve analysis (DCA) confirmed the clinical utility of the nomogram.
Conclusion
We developed and validated a nomogram for predicting the OS of postoperative T3N0M0 ESCC patients, integrating nutritional, inflammatory markers, and radiomic signature. The combined nomogram can serve as a robust tool for risk stratification and clinical management.
{"title":"A prognostic nomogram for T3N0M0 esophageal squamous cell carcinoma patients undergoing radical surgery based on computed tomography radiomics and inflammatory nutritional biomarkers","authors":"Hui Ma, Yangchen Liu, Hongxun Ye, Fei Gao, Songbing Qin","doi":"10.1002/acm2.14504","DOIUrl":"10.1002/acm2.14504","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>This study explores the significance of computed tomography (CT) radiomic features, along with inflammation and nutrition biomarkers, in the prognosis of postoperative patients with T3N0M0 esophageal squamous cell carcinoma (ESCC). The study aims to construct a related nomogram.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A total of 114 patients were enrolled and randomly assigned to training and validation cohorts in a 7:3 ratio. Radiomic features were extracted from their preoperative chest-enhanced CT arterial images of the primary tumor, and inflammatory and nutritional indices, including neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), and prognostic nutritional index (PNI), were calculated based on laboratory data from the 3 days before surgery. Intra-class correlations coefficient (ICC) and least absolute shrinkage and selection operator (Lasso) were applied to screen valuable radiomics features predicting overall survival (OS), and the Rad-score was calculated. In the training cohort, univariate and multivariate Cox regression analyses identified independent prognostic factors, which were adopted to establish the nomogram.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Eight radiomic features were selected for Rad-score calculation. Multivariate Cox regression revealed Rad-score, PNI, NLR, and PLR as independent prognostic factors for ESCC patients (<i>p</i> < 0.05). A nomogram was constructed based on these variables. The concordance index (C-index) for the nomogram was 0.797 (95% CI: 0.726–0.868) in the training cohort and 0.796 (95% CI: 0.702–0.890) in the validation cohort. Calibration curves indicated good calibration ability, and the receiver operating characteristic (ROC) analysis demonstrated superior discriminative ability for the nomogram in comparison to the Rad-score alone. Decision curve analysis (DCA) confirmed the clinical utility of the nomogram.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>We developed and validated a nomogram for predicting the OS of postoperative T3N0M0 ESCC patients, integrating nutritional, inflammatory markers, and radiomic signature. The combined nomogram can serve as a robust tool for risk stratification and clinical management.</p>\u0000 </section>\u0000 </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11539971/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dharmin D. Desai, Ivan L. Cordrey, E. Lee Johnson, Thomas A. Oldland
Introduction
HyperArc (HA) auto-planning offers simplicity for the end user and consistently high-quality SRS plans. The “Ask For It” (AFI) optimization strategy offers a manual planning technique that, when coupled with R50%Analytic, can be guided to deliver a plan with an intermediate dose spill “as low as reasonably achievable” and high target dose conformity. A direct comparison of SRS plan quality obtained using the manual planning AFI strategy and HA has been performed.
Methods
Using a CT data set available from the Radiosurgery Society, 54 PTVs were created and used to generate 19 individual SRS/SRT cases. Case complexity ranged from single PTV plans to multiple PTV plans with a single isocenter. PTV locations ranged from relative isolation from critical structures to lesions within 1.5 mm of the optic apparatus and abutting the brainstem. All cases were planned using both the AFI and HA optimization strategies as implemented in the Varian Medical Systems Eclipse Treatment Planning System. A range of treatment plan quality metrics were obtained including Intermediate Dose Spill (R50%), Conformity Indices CIRTOG and CIPaddick, PTV Dose Coverage (Dn%), PTV Mean Dose, and Modulation Factor. The Wilcoxon Signed Rank Sum non-parametric statistical method was utilized to compare the obtained plan quality metrics.
Results
Statistically significant improvements were found for the AFI strategy for metrics R50%, CIRTOG, CIPaddick, and PTV Mean Dose (p < 0.001). HA achieved superior coverage for Dn% (p = 0.018), while the Modulation Factors were not significantly different for AFI compared to HA optimization (p = 0.13).
Conclusion
This study provides evidence that the AFI manual planning strategy can produce high-quality planning metrics similar to the HA auto-planning method.
{"title":"AFI manual planning versus HyperArc auto-planning: A head-to-head comparison of SRS plan quality","authors":"Dharmin D. Desai, Ivan L. Cordrey, E. Lee Johnson, Thomas A. Oldland","doi":"10.1002/acm2.14503","DOIUrl":"10.1002/acm2.14503","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>HyperArc (HA) auto-planning offers simplicity for the end user and consistently high-quality SRS plans. The “Ask For It” (AFI) optimization strategy offers a manual planning technique that, when coupled with R50%<sub>Analytic</sub>, can be guided to deliver a plan with an intermediate dose spill “as low as reasonably achievable” and high target dose conformity. A direct comparison of SRS plan quality obtained using the manual planning AFI strategy and HA has been performed.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Using a CT data set available from the Radiosurgery Society, 54 PTVs were created and used to generate 19 individual SRS/SRT cases. Case complexity ranged from single PTV plans to multiple PTV plans with a single isocenter. PTV locations ranged from relative isolation from critical structures to lesions within 1.5 mm of the optic apparatus and abutting the brainstem. All cases were planned using both the AFI and HA optimization strategies as implemented in the Varian Medical Systems Eclipse Treatment Planning System. A range of treatment plan quality metrics were obtained including Intermediate Dose Spill (R50%), Conformity Indices CI<sub>RTOG</sub> and CI<sub>Paddick</sub>, PTV Dose Coverage (Dn%), PTV Mean Dose, and Modulation Factor. The Wilcoxon Signed Rank Sum non-parametric statistical method was utilized to compare the obtained plan quality metrics.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Statistically significant improvements were found for the AFI strategy for metrics R50%, CI<sub>RTOG</sub>, CI<sub>Paddick</sub>, and PTV Mean Dose (<i>p</i> < 0.001). HA achieved superior coverage for Dn% (<i>p</i> = 0.018), while the Modulation Factors were not significantly different for AFI compared to HA optimization (<i>p</i> = 0.13).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>This study provides evidence that the AFI manual planning strategy can produce high-quality planning metrics similar to the HA auto-planning method.</p>\u0000 </section>\u0000 </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11540047/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142132824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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