Medical physics最新文献

{"title":"Photon counting CT versus energy-integrating CT: A comparative evaluation of advances in image resolution, noise, and dose efficiency","authors":"Björn Heismann,&nbsp;Björn Kreisler,&nbsp;Robert Fasbender","doi":"10.1002/mp.17591","DOIUrl":"10.1002/mp.17591","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Photon counting computed tomography (PCCT) employs direct and spectrally resolved counting of individual x-ray quanta, enhancing image quality compared to the standard energy-integrating CT (EICT).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>To evaluate the quantitative improvements in CT image quality metrics by comparing the first medical PCCT with a state-of-the-art EICT.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The PCCT versus EICT noise improvement ratio <i>R</i> was derived from the quantum statistics of the measurement process and measured across the clinical x-ray flux range for both systems. Detector and system modulation transfer functions (MTFs) were obtained using tilted-slit and wire phantom measurements. Image root mean square (RMS) noise, noise power spectrum (NPS), and x-ray patient dose were compared using a CatPhan phantom at two identical clinical target resolutions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The measurement of the PCCT noise improvement ratio <i>R</i> showed an elimination of electronic noise and a 10% noise transfer advantage. The PCCT detector MTF exhibited 3x higher angular resolution limits in comparison to EICT and close to ideal sinc behavior due to the electromagnetic formation of pixels in the PCCT semiconductor detector. This translated to 3.5x enhancements in CT system MTF ratios at 10 LP/cm, reflecting a significant improvement in millimeter range CT imaging. Both the improved quantum detection and the system MTF ratio improvement contribute to the measured 3x enhancements in image NPS at 10 LP/cm for identical image target resolution. An improvement of up to 1.7x in RMS image noise was observed accordingly. For low and ultra-low dose imaging with image filtering, dose efficiency increased between 2x and 10x, demonstrating the PCCT's capability to advance CT ultra-low dose imaging.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The direct counting detection in PCCT has been shown to significantly improve sinogram noise and detector MTF ratios compared to energy integrating EICT. The observed translations into CT system MTF, image NPS, image noise, and dose ratios reflect a paradigm shift for CT image quality and dose efficiency.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 3","pages":"1526-1535"},"PeriodicalIF":3.2,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17591","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142866840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feasibility of volumetric-modulated arc therapy gating for cardiac radioablation using real-time ECG signal acquisition and a dynamic phantom","authors":"Cristiano Q. M. Reis,&nbsp;Alex Cross,&nbsp;Jennifer M. Borsavage,&nbsp;Greg Berryhill,&nbsp;Scott Karnas,&nbsp;James L. Robar,&nbsp;Stewart Gaede","doi":"10.1002/mp.17582","DOIUrl":"10.1002/mp.17582","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Stereotactic arrythmia radioablation (STAR) is a noninvasive technique to treat ventricular tachycardia (VT). Management of cardiorespiratory motion plays an essential role in VT-STAR treatments to improve treatment outcomes by reducing positional uncertainties and increasing dose conformality. Use of an electrocardiogram (ECG) signal, acquired in real-time, as a surrogate to gate the beam has the potential to fulfil that intent.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;To investigate the gated delivery of volumetric-modulated arc therapy (VMAT) for STAR on a TrueBeam linear accelerator (linac) using a real-time acquired ECG signal and a dynamic cardiac phantom.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Methods and materials&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Dosimetric characteristics of a 6 MV flattening filter free (FFF) beam from a Varian TrueBeam linac were initially evaluated under high-frequency gating scenarios relevant to cardiac rhythms with respect to dose linearity, beam output, and energy quality. A microcontroller board was used to interface and gate the linac, sending a beam on/off signal. For real-time cardiac gated measurements, an AD8232 Heart Monitor board was used to acquire the ECG signal and synchronize the VMAT delivery to an ArcCHECK detector to a specific phase of the cardiac cycle. Gated dose distributions were compared against those acquired for a non-gated delivery mode. An in-house dynamic cardiac phantom was developed to simulate cardiorespiratory motion that correlates target position with the signal to gate the beam. Measured dose distributions using gafchromic film were also compared against the static (reference) mode in different scenarios with and without gating.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Maximum difference in dose per monitor unit (MU) was found to be no greater than 1% as compared to static mode with variation in the chamber response within 0.2% in the range of 50 MUs to 200 MUs. Maximum percentage differences for the beam output and beam qualiy index (TPR&lt;sub&gt;20,10&lt;/sub&gt;) between gated and non-gated modes were 0.91% and -0.44%, respectively. Comparison of delivered dose distributions for the VMAT plan without gating versus ECG synchronized gating modes provided a passing rate 98% for the gamma analysis with 1% relative dose difference, 1 mm distance-to-agreement criteria. For the synchronization of dose delivery with target position, passing rates were 98%, 97%, and 99% for the axial, coronal, and sagittal planes, respectively, when gating the beam based on target position for cardiac","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 3","pages":"1758-1768"},"PeriodicalIF":3.2,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17582","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142857411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cover","authors":"","doi":"10.1002/mp.15762","DOIUrl":"https://doi.org/10.1002/mp.15762","url":null,"abstract":"","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"51 12","pages":"C1"},"PeriodicalIF":3.2,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.15762","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143110690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"List of Advertisers","authors":"","doi":"10.1002/mp.15763","DOIUrl":"https://doi.org/10.1002/mp.15763","url":null,"abstract":"","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"51 12","pages":"9405"},"PeriodicalIF":3.2,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.15763","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143110689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An automated treatment planning portfolio for whole breast radiotherapy","authors":"Hana Baroudi,&nbsp;Leonard Che Fru,&nbsp;Deborah Schofield,&nbsp;Dominique L. Roniger,&nbsp;Callistus Nguyen,&nbsp;Donald Hancock,&nbsp;Christine Chung,&nbsp;Beth M. Beadle,&nbsp;Kent A. Gifford,&nbsp;Tucker Netherton,&nbsp;Joshua S. Niedzielski,&nbsp;Adam Melancon,&nbsp;Manickam Muruganandham,&nbsp;Meena Khan,&nbsp;Simona F. Shaitelman,&nbsp;Sanjay Shete,&nbsp;Patricia Murina,&nbsp;Daniel Venencia,&nbsp;Sheeba Thengumpallil,&nbsp;Conny Vrieling,&nbsp;Joy Zhang,&nbsp;Melissa P. Mitchell,&nbsp;Laurence E. Court","doi":"10.1002/mp.17588","DOIUrl":"10.1002/mp.17588","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Automation in radiotherapy presents a promising solution to the increasing cancer burden and workforce shortages. However, existing automated methods for breast radiotherapy lack a comprehensive, end-to-end solution that meets varying standards of care.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;This study aims to develop a complete portfolio of automated radiotherapy treatment planning for intact breasts, tailored to individual patient factors, clinical approaches, and available resources.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;We developed five automated conventional treatment approaches and utilized an established RapidPlan model for volumetric arc therapy. These approaches include conventional tangents for whole breast treatment, two variants for supraclavicular nodes (SCLV) treatment with/without axillary nodes, and two options for comprehensive regional lymph nodes treatment. The latter consists of wide tangents photon fields with a SCLV field, and a photon tangents field with a matched electron field to treat the internal mammary nodes (IMNs), and a SCLV field. Each approach offers the choice of a single or two isocenter setup (with couch rotation) to accommodate a wide range of patient sizes. All algorithms start by automatically generating contours for breast clinical target volume, regional lymph nodes, and organs at risk using an in-house nnU-net deep learning models. Gantry angles and field shapes are then automatically generated and optimized to ensure target coverage while limiting the dose to nearby organs. The dose is optimized using field weighting for the lymph nodes fields and an automated field-in-field approach for the tangents. These algorithms were integrated into the RayStation treatment planning system and tested for clinical acceptability on 15 internal whole breast patients (150 plans) and 40 external patients from four different institutions in Switzerland, Argentina, Iran, and the USA (360 plans). Evaluation criteria included ensuring adequate coverage of targets and adherence to dose constraints for normal structures. A breast radiation oncologist reviewed the single institution dataset for clinical acceptability (5-point scale) and a physicist evaluated the multi-institutional dataset (use as is or edit).&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The dosimetric evaluation across all datasets (510 plans) showed that 100% of the automated plans met the dose coverage requirements for the breast, 99% for the SCLV, 98% for the axillary nodes, and 91% for the IMN. As exp","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 3","pages":"1779-1788"},"PeriodicalIF":3.2,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17588","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142857406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A device-dependent auto-segmentation method based on combined generalized and single-device datasets","authors":"Hyeongjin Lim,&nbsp;Yongha Gi,&nbsp;Yousun Ko,&nbsp;Yunhui Jo,&nbsp;Jinyoung Hong,&nbsp;Jonghyun Kim,&nbsp;Sung Hwan Ahn,&nbsp;Hee-Chul Park,&nbsp;Haeyoung Kim,&nbsp;Kwangzoo Chung,&nbsp;Myonggeun Yoon","doi":"10.1002/mp.17570","DOIUrl":"10.1002/mp.17570","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Although generalized-dataset-based auto-segmentation models that consider various computed tomography (CT) scanners have shown great clinical potential, their application to medical images from unseen scanners remains challenging because of device-dependent image features.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>This study aims to investigate the performance of a device-dependent auto-segmentation model based on a combined dataset of a generalized dataset and single CT scanner dataset.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Method</h3>\u0000 \u0000 <p>We constructed two training datasets for 21 chest and abdominal organs. The generalized dataset comprised 1203 publicly available multi-scanner data. The device-dependent dataset comprised 1253 data, including the 1203 multi-CT scanner data and 50 single CT scanner data. Using these datasets, the generalized-dataset-based model (GDSM) and the device-dependent-dataset-based model (DDSM) were trained. The models were trained using nnU-Net and tested on ten data samples from a single CT scanner. The evaluation metrics included the Dice similarity coefficient (DSC), the Hausdorff distance (HD), and the average symmetric surface distance (ASSD), which were used to assess the overall performance of the models. In addition, DSC_diff, HD_ratio, and ASSD_ratio, which are variations of the three existing metrics, were used to compare the performance of the models across different organs.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Result</h3>\u0000 \u0000 <p>For the average DSC, the GDSM and DDSM had values of 0.9251 and 0.9323, respectively. For the average HD, the GDSM and DDSM had values of 10.66 and 9.139 mm, respectively; for the average ASSD, the GDSM and DDSM had values of 0.8318 and 0.6656 mm, respectively. Compared with the GDSM, the DDSM showed consistent performance improvements of 0.78%, 14%, and 20% for the DSC, HD, and ASSD metrics, respectively. In addition, compared with the GDSM, the DDSM had better DSC_diff values in 14 of 21 tested organs, better HD_ratio values in 13 of 21 tested organs, and better ASSD_ratio values in 14 of 21 tested organs. The three averages of the variant metrics were all better for the DDSM than for the GDSM.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The results suggest that combining the generalized dataset with a single scanner dataset resulted in an overall improvement in model performance for that device image.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 4","pages":"2375-2383"},"PeriodicalIF":3.2,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142857403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Personalized deep learning auto-segmentation models for adaptive fractionated magnetic resonance-guided radiation therapy of the abdomen","authors":"Maria Kawula,&nbsp;Sebastian Marschner,&nbsp;Chengtao Wei,&nbsp;Marvin F. Ribeiro,&nbsp;Stefanie Corradini,&nbsp;Claus Belka,&nbsp;Guillaume Landry,&nbsp;Christopher Kurz","doi":"10.1002/mp.17580","DOIUrl":"10.1002/mp.17580","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Manual contour corrections during fractionated magnetic resonance (MR)-guided radiotherapy (MRgRT) are time-consuming. Conventional population models for deep learning auto-segmentation might be suboptimal for MRgRT at MR-Linacs since they do not incorporate manual segmentation from treatment planning and previous fractions.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;In this work, we investigate patient-specific (PS) auto-segmentation methods leveraging expert-segmented planning and prior fraction MR images (MRIs) to improve auto-segmentation on consecutive treatment days.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Materials and Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Data from 151 abdominal cancer patients treated at a 0.35 T MR-Linac (151 planning and 215 fraction MRIs) were included. Population baseline models (BMs) were trained on 107 planning MRIs for one-class segmentation of the aorta, bowel, duodenum, kidneys, liver, spinal canal, and stomach. PS models were obtained by fine-tuning the BMs using the planning MRI (&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;msub&gt;\u0000 &lt;mtext&gt;PS&lt;/mtext&gt;\u0000 &lt;mi&gt;BM&lt;/mi&gt;\u0000 &lt;/msub&gt;\u0000 &lt;annotation&gt;$text{PS}_{mathrm{BM}}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;). Maximal improvement by continuously updating the PS models was investigated by adding the first four out of five fraction MRIs (&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;msubsup&gt;\u0000 &lt;mtext&gt;PS&lt;/mtext&gt;\u0000 &lt;mi&gt;BM&lt;/mi&gt;\u0000 &lt;mo&gt;F4&lt;/mo&gt;\u0000 &lt;/msubsup&gt;\u0000 &lt;annotation&gt;$text{PS}_{mathrm{BM}}^{operatorname{F4}}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;). Similarly, PS models without BM were trained (&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;msub&gt;\u0000 &lt;mtext&gt;PS&lt;/mtext&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;no&lt;/mi&gt;\u0000 &lt;mi&gt;BM&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;annotation&gt;$text{PS}_{mathrm{no BM}}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; and &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;msubsup&gt;\u0000 &lt;mtext&gt;PS&lt;/mtext&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;no&lt;/mi&gt;\u0000 &lt;mi&gt;BM&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;mo&gt;F4&lt;/mo&gt;\u0000 ","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 4","pages":"2295-2304"},"PeriodicalIF":3.2,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17580","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142857416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction method for ionization chamber dosimetry in flattening filter free radiotherapy based on Monte Carlo simulation","authors":"Guolong Zhang,&nbsp;Ji Huang,&nbsp;Guoxin Wu,&nbsp;Sunjun Jin,&nbsp;Kun Wang,&nbsp;Hao Wu,&nbsp;Hui Zhang,&nbsp;Haizheng Yue,&nbsp;Ruijie Yang,&nbsp;Yujie Wang,&nbsp;Zhipeng Wang,&nbsp;Yaping Qi","doi":"10.1002/mp.17585","DOIUrl":"10.1002/mp.17585","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The clinical use of flattening filter free (FFF) radiotherapy has significantly increased in recent years due to its effective enhancement of dose rates and reduction of scatter dose. A proposal has been made to adjust the incident electron angle of the accelerator to expand the application of FFF beams in areas such as large planning target volumes (PTVs). However, the inherent softening characteristics and non-uniformity of lateral dose distribution in FFF beams inevitably lead to increased dosimetry errors, especially for ionization chambers widely used in clinical practice, which may result in serious accidents during FFF radiotherapy.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;This study constructs a comprehensive Monte Carlo model that encompasses not only conventional FFF beams but also incorporates FFF beams with varying incident electron angles, to investigate dosimetry errors and correction methods in FFF radiotherapy.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;We have innovatively introduced a FFF output correction factor (&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;k&lt;/mi&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;Q&lt;/mi&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;F&lt;/mi&gt;\u0000 &lt;mi&gt;F&lt;/mi&gt;\u0000 &lt;mi&gt;F&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;Q&lt;/mi&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;W&lt;/mi&gt;\u0000 &lt;mi&gt;F&lt;/mi&gt;\u0000 &lt;mi&gt;F&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;annotation&gt;${k}_{{Q}_{FFF},{Q}_{WFF}}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;) to address dosimetry errors in various ionization chambers under different incident electron angle conditions in FFF beams. The primary variations in &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;k&lt;/mi&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;Q&lt;/mi&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;F&lt;/mi&gt;\u0000 &lt;mi&gt;F&lt;/mi&gt;\u0000 &lt;mi&gt;F&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msub&gt;\u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 ","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 3","pages":"1833-1844"},"PeriodicalIF":3.2,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142840718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Model-based CBCT scatter correction with dual-layer flat-panel detector","authors":"Xin Zhang,&nbsp;Jixiong Xie,&nbsp;Yuhang Tan,&nbsp;Ting Su,&nbsp;Jiongtao Zhu,&nbsp;Han Cui,&nbsp;Dongmei Xia,&nbsp;Hairong Zheng,&nbsp;Dong Liang,&nbsp;Yongshuai Ge","doi":"10.1002/mp.17567","DOIUrl":"10.1002/mp.17567","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Recently, the popularity of dual-layer flat-panel detector (DL-FPD) based dual-energy cone-beam CT (CBCT) imaging has been increasing. However, the image quality of dual-energy CBCT remains constrained by the Compton scattered x-ray photons.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The objective of this study is to develop a novel scatter correction method, named e-Grid, for DL-FPD based CBCT imaging.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;In DL-FPD, a certain portion of the x-ray photons (mainly low-energy [LE] primary and scattered photons) passing through the object are captured by the top detector layer, while the remaining x-ray photons (mainly high-energy [HE] primary and scattered photons) are collected by the bottom detector layer. A linear signal model was approximated between the HE primary and scatter signals and the LE primary and scatter signals. Physical calibration experiments were performed on cone beam and fan beam to validate the aforementioned signal model via linear fittings. Monte Carlo (MC) simulations of a 10 cm diameter water phantom were conducted on GATE at first to verify this newly developed scatter estimation method. In addition, physical validation experiments of water phantom, head phantom, and abdominal phantom were carried out on a DL-FPD based benchtop CBCT imaging system. The image non-uniformity (NU), which represents the relative difference between the center and the edges of CT images, was measured to quantify the reduction of image shading artifacts. Finally, multi-material decomposition was conducted.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The MC results, CBCT images and line profiles, showed that the newly proposed e-Grid approach was able to accurately predict the scatter distributions in both shape and intensity. As a result, uniform CBCT images that are close to the scatter artifact-free reference images can be obtained. Moreover, the physical experiments demonstrated that the e-Grid method can greatly reduce the shading artifacts in both LE and HE CBCT images acquired from DL-FPD. Results also demonstrated that the e-Grid method is effective for varied objects that having different diameters (from 10 to 28 cm). Quantitatively, the NU value was reduced by over 77% in the LE CBCT image and by over 66% in the HE CBCT image on average. As a consequence, the accuracy of the decomposed multi-material bases, iodine and gadolinium, was substantially improved.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Conclusions&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The Compton scattered x-ray signals could be sign","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 3","pages":"1500-1514"},"PeriodicalIF":3.2,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142840735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Virtual imaging trials in medicine: A brief takeaway of the lessons from the first international summit","authors":"Ehsan Samei,&nbsp;Ehsan Abadi,&nbsp;Predrag Bakic,&nbsp;Kristina Bliznakova,&nbsp;Hilde Bosmans,&nbsp;Ann-Katherine Carton,&nbsp;Alejandro F. Frangi,&nbsp;Stephen Glick,&nbsp;Joseph Y. Lo,&nbsp;Paul Kinahan,&nbsp;Andrew Maidment,&nbsp;Francesco Ria,&nbsp;Ioannis Sechopoulos,&nbsp;William Paul Segars,&nbsp;Rie Tanaka,&nbsp;Liesbeth Vancoillie","doi":"10.1002/mp.17587","DOIUrl":"10.1002/mp.17587","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The rapid advancement of medical technologies presents significant challenges for researchers and practitioners. While traditional clinical trials remain the gold standard, they are often limited by high costs, lengthy durations, and ethical constraints. In contrast, in-silico trials and digital twins have emerged not only as efficient and ethical alternatives but also as a complementary technology that can extend beyond classical trials to predict and design new strategies. The successful application of digital twins in industries like nuclear energy, automotive engineering, and aviation underscores their potential in human health.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>In April 2024, Duke University hosted the first international summit on Virtual Imaging Trials in Medicine (VITM). The summit brought together over 130 experts from academia, industry, and regulatory bodies to discuss the latest developments, challenges, and future directions in this field. The event featured plenary speakers, presentations, and panel discussions, emphasizing the integration of clinical and in-silico methods to enhance medical evaluations.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Key takeaways included the necessity of diverse and realistic digital patient representations, the integration of physics and biology in simulations, and the development of robust validation frameworks. The summit also highlighted the importance of regulatory science and the establishment of Good Simulation Practices to ensure the credibility and reliability of virtual trials.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The key discussions and insights from the VITM summit underscore the potential of in-silico trials to revolutionize medical research and patient care through personalized, efficient, and ethical evaluation methods. The collaborative efforts and recommendations from this summit aim to drive future advancements in virtual imaging trials in medicine.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"52 3","pages":"1950-1959"},"PeriodicalIF":3.2,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17587","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142840738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}