To report and investigate a case of significant dose attenuation observed during quality assurance (QA) of an Elekta Unity MR-Linac system, and to elucidate its root cause and solution.
Methods
During a period of abnormal environmental conditions (elevated temperature and humidity), a >20% dose output reduction was detected at a gantry angle of 180°. A comprehensive investigation was undertaken, including dose output measurements at multiple gantry angles, beam quality assessment, mechanical accuracy checks, EPID image analysis, and gamma pass rate analysis using ArcCheck-MR. The source of the attenuation was localized through a process of elimination.
Results
The investigation revealed severe dose attenuation specifically at angles traversing beneath the treatment couch, with a gamma pass rate (3%/3 mm) dropping to 39.1% at 180°. All other system parameters, including MRI quality and mechanical alignments, were within tolerance. Visual inspection after disassembly confirmed the presence of a substantial pool of water beneath the table, estimated to be at least 3.5 cm deep, which acted as an unintended attenuator. The water was attributed to condensation and drainage issues exacerbated by the failed air-conditioning system. Mitigation strategies, including hardware sealing, enhanced drainage, revised maintenance protocols, and the addition of a 180° dose check to daily QA, successfully resolved the issue.
Conclusion
This case highlights a previously unreported and critical failure mode for MR-Linac systems. It underscores the profound impact of environmental control on machine performance and patient safety. Robust HVAC maintenance, proactive monitoring of sub-table areas, and the inclusion of oblique-angle dosimetry in QA routines are essential for all Elekta Unity and similar high-precision radiotherapy systems.
{"title":"Identification and mitigation of unexplained dose attenuation caused by sub-table water accumulation in an MR-Linac (Elekta Unity)","authors":"Jinhu Chen, YuKun Li, Xingwei An, Zhenjiang Li","doi":"10.1002/acm2.70441","DOIUrl":"10.1002/acm2.70441","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>To report and investigate a case of significant dose attenuation observed during quality assurance (QA) of an Elekta Unity MR-Linac system, and to elucidate its root cause and solution.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>During a period of abnormal environmental conditions (elevated temperature and humidity), a >20% dose output reduction was detected at a gantry angle of 180°. A comprehensive investigation was undertaken, including dose output measurements at multiple gantry angles, beam quality assessment, mechanical accuracy checks, EPID image analysis, and gamma pass rate analysis using ArcCheck-MR. The source of the attenuation was localized through a process of elimination.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The investigation revealed severe dose attenuation specifically at angles traversing beneath the treatment couch, with a gamma pass rate (3%/3 mm) dropping to 39.1% at 180°. All other system parameters, including MRI quality and mechanical alignments, were within tolerance. Visual inspection after disassembly confirmed the presence of a substantial pool of water beneath the table, estimated to be at least 3.5 cm deep, which acted as an unintended attenuator. The water was attributed to condensation and drainage issues exacerbated by the failed air-conditioning system. Mitigation strategies, including hardware sealing, enhanced drainage, revised maintenance protocols, and the addition of a 180° dose check to daily QA, successfully resolved the issue.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>This case highlights a previously unreported and critical failure mode for MR-Linac systems. It underscores the profound impact of environmental control on machine performance and patient safety. Robust HVAC maintenance, proactive monitoring of sub-table areas, and the inclusion of oblique-angle dosimetry in QA routines are essential for all Elekta Unity and similar high-precision radiotherapy systems.</p>\u0000 </section>\u0000 </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"27 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12745900/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145850005","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}
Koufigar S, Ford E, He Y, Olsen S, Fagerstrom JM. A case study on SSD to SAD linear acceleartor calibration transition. J Appl Clin Med Phys. 2025;26:e70298.
Description of error:
There is a spelling error in the article title. The word “accelerator” is incorrectly written as “acceleartor.” The correct title is “A case study on SSD to SAD linear accelerator calibration transition.” The online version of the article has been corrected accordingly.
{"title":"Correction to “A case study on SSD to SAD linear acceleartor calibration transition”","authors":"","doi":"10.1002/acm2.70421","DOIUrl":"https://doi.org/10.1002/acm2.70421","url":null,"abstract":"<p>Koufigar S, Ford E, He Y, Olsen S, Fagerstrom JM. A case study on SSD to SAD linear acceleartor calibration transition. <i>J Appl Clin Med Phys</i>. 2025;26:e70298.</p><p>Description of error:</p><p>There is a spelling error in the article title. The word <b>“accelerator”</b> is incorrectly written as <b>“acceleartor.”</b> The correct title is “A case study on SSD to SAD linear accelerator calibration transition.” The online version of the article has been corrected accordingly.</p><p>We apologize for this error.</p>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"27 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://aapm.onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.70421","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145905182","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 Sánchez-Artuñedo, Paula Navarro-Palomas, Marcelino Hermida-López, Maria Amor Duch-Guillén, Mercè Beltran-Vilagrasa
Purpose
To determine the gamma criteria that maximize the sensitivity and specificity of the transit dosimetry software PerFRACTION (Sun Nuclear Corporation) under five possible failure modes in external beam radiotherapy.
Methods
We simulated five failure modes that potentially introduce large changes in the absorbed dose distribution: (1) Linac hardware incidents. In a VMAT head and neck treatment plan, erroneous plans were created, introducing known errors in the MLC aperture, the collimator angle, and the monitor units. (2) Breathing management protocol incidents. Based on a 4D-CT of a lung treatment, we created expiration and inspiration CT images. A treatment plan was created using each CT and recalculated in the alternate CT. (3) Patient identification incidents. The treatment plan of one breast patient was recalculated on another patient, and vice versa. (4) Selection of the planning CT incidents. A lung patient had two planning CTs with the presence/absence of lung fluid. A treatment plan was generated for each CT and recalculated for the other. (5) Bolus positioning incidents. An erroneous treatment plan for a breast plan was created without the bolus. For the five failure modes, the transit images were compared using seven gamma criteria, both global and local. Receiver-operating characteristic (ROC) curves were generated based on the change in the PTV mean dose: > 5%, > 10%, and > 20%. The area under the curve (AUC) and optimal passing rate were calculated.
Results
The global gamma criterion γ(10%/1 mm) maximizes PerFRACTION sensitivity and specificity to detect failure modes that introduce a change in the PTV mean dose exceeding 10%. The standard global gamma criterion, as γ(3%/3 mm), maximizes PerFRACTION sensitivity and specificity to detect deviations in the PTV mean dose above 5%.
Conclusions
A 10%, 1 mm global gamma parameter value produces the needed sensitivity and specificity to identify erroneous deliveries at a level of dose differences of 10% or greater.
{"title":"Determining optimal transit dosimetry gamma parameter values for the detection of failure modes using receiver operating curve analysis","authors":"David Sánchez-Artuñedo, Paula Navarro-Palomas, Marcelino Hermida-López, Maria Amor Duch-Guillén, Mercè Beltran-Vilagrasa","doi":"10.1002/acm2.70424","DOIUrl":"10.1002/acm2.70424","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>To determine the gamma criteria that maximize the sensitivity and specificity of the transit dosimetry software PerFRACTION (Sun Nuclear Corporation) under five possible failure modes in external beam radiotherapy.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We simulated five failure modes that potentially introduce large changes in the absorbed dose distribution: (1) Linac hardware incidents. In a VMAT head and neck treatment plan, erroneous plans were created, introducing known errors in the MLC aperture, the collimator angle, and the monitor units. (2) Breathing management protocol incidents. Based on a 4D-CT of a lung treatment, we created expiration and inspiration CT images. A treatment plan was created using each CT and recalculated in the alternate CT. (3) Patient identification incidents. The treatment plan of one breast patient was recalculated on another patient, and vice versa. (4) Selection of the planning CT incidents. A lung patient had two planning CTs with the presence/absence of lung fluid. A treatment plan was generated for each CT and recalculated for the other. (5) Bolus positioning incidents. An erroneous treatment plan for a breast plan was created without the bolus. For the five failure modes, the transit images were compared using seven gamma criteria, both global and local. Receiver-operating characteristic (ROC) curves were generated based on the change in the PTV mean dose: > 5%, > 10%, and > 20%. The area under the curve (AUC) and optimal passing rate were calculated.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The global gamma criterion γ(10%/1 mm) maximizes PerFRACTION sensitivity and specificity to detect failure modes that introduce a change in the PTV mean dose exceeding 10%. The standard global gamma criterion, as γ(3%/3 mm), maximizes PerFRACTION sensitivity and specificity to detect deviations in the PTV mean dose above 5%.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>A 10%, 1 mm global gamma parameter value produces the needed sensitivity and specificity to identify erroneous deliveries at a level of dose differences of 10% or greater.</p>\u0000 </section>\u0000 </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"27 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12746048/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145849944","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}
Sebastian Tattenberg, Jungwook Shin, Cornelia Hoehr, Xuanfeng Ding, Rohan L. Deraniyagala, Wonmo Sung
<div> <section> <h3> Background</h3> <p>In cancer radiotherapy, radiation-induced lymphopenia (RIL) has been reported to be correlated with adverse clinical outcomes such as reduced locoregional control (LRC), distant-metastasis-free survival (DMFS), and overall survival (OS) in various treatment sites. Frameworks to simulate the radiation dose to circulating blood have been developed in response, and simulated blood dose values have been reported to be correlated with severe RIL and/or adverse clinical outcomes. However, validations with different patient datasets or expansions to additional treatment sites, as well as the identification of particularly relevant blood dose metrics and blood compartments to allow for their inclusion during radiotherapy treatment planning, remain lacking.</p> </section> <section> <h3> Purpose</h3> <p>This study aims to investigate a potential correlation between simulated blood dose values and adverse clinical outcomes in 215 patients with head-and-neck squamous cell carcinoma (HNSCC), 180 patients with glioblastoma (GBM), and 490 patients with non-small-cell lung cancer (NSCLC), and to identify particularly relevant blood dose metrics and blood compartments to allow for their inclusion during radiotherapy treatment planning and thereby enable the optimization of the estimated dose delivered to circulating blood.</p> </section> <section> <h3> Methods</h3> <p>For all 885 patients, TotalSegmentator was used to automatically delineate additional organs-at-risk (OARs), blood vessels, and tissues which were not already manually delineated for radiotherapy treatment planning. Subsequently, the dynamic HEDOS model, which considers temporal aspects such as blood flow dynamics and treatment delivery time, was used to simulate the radiation dose delivered to circulate blood during radiotherapy. Static blood dose models consisting of the mean dose to the union of all HEDOS blood compartments (<i>D</i><sub>static,HEDOS</sub>) and the integral body dose (<i>D</i><sub>static,body</sub>) were also investigated to verify whether a simplified blood dose model equally exhibited any correlations with adverse clinical outcomes.</p> </section> <section> <h3> Results</h3> <p>During multivariable Cox regression analysis, the blood dose estimates from the dynamic blood dose model exhibited a statistically significant (<i>p</i> < 0.05) correlation with DMFS and OS in the HNSCC and NSCLC datasets as well as with LRC in the HNSCC dataset. <i>D</i><sub>static,body</sub> and <i>D</i><sub>static,HEDOS</sub> only exhibited a statistically sign
{"title":"Dynamic blood dose estimates in radiotherapy and correlations with adverse clinical outcomes in brain, head-and-neck, and lung cancer patients","authors":"Sebastian Tattenberg, Jungwook Shin, Cornelia Hoehr, Xuanfeng Ding, Rohan L. Deraniyagala, Wonmo Sung","doi":"10.1002/acm2.70341","DOIUrl":"10.1002/acm2.70341","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>In cancer radiotherapy, radiation-induced lymphopenia (RIL) has been reported to be correlated with adverse clinical outcomes such as reduced locoregional control (LRC), distant-metastasis-free survival (DMFS), and overall survival (OS) in various treatment sites. Frameworks to simulate the radiation dose to circulating blood have been developed in response, and simulated blood dose values have been reported to be correlated with severe RIL and/or adverse clinical outcomes. However, validations with different patient datasets or expansions to additional treatment sites, as well as the identification of particularly relevant blood dose metrics and blood compartments to allow for their inclusion during radiotherapy treatment planning, remain lacking.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>This study aims to investigate a potential correlation between simulated blood dose values and adverse clinical outcomes in 215 patients with head-and-neck squamous cell carcinoma (HNSCC), 180 patients with glioblastoma (GBM), and 490 patients with non-small-cell lung cancer (NSCLC), and to identify particularly relevant blood dose metrics and blood compartments to allow for their inclusion during radiotherapy treatment planning and thereby enable the optimization of the estimated dose delivered to circulating blood.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>For all 885 patients, TotalSegmentator was used to automatically delineate additional organs-at-risk (OARs), blood vessels, and tissues which were not already manually delineated for radiotherapy treatment planning. Subsequently, the dynamic HEDOS model, which considers temporal aspects such as blood flow dynamics and treatment delivery time, was used to simulate the radiation dose delivered to circulate blood during radiotherapy. Static blood dose models consisting of the mean dose to the union of all HEDOS blood compartments (<i>D</i><sub>static,HEDOS</sub>) and the integral body dose (<i>D</i><sub>static,body</sub>) were also investigated to verify whether a simplified blood dose model equally exhibited any correlations with adverse clinical outcomes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>During multivariable Cox regression analysis, the blood dose estimates from the dynamic blood dose model exhibited a statistically significant (<i>p</i> < 0.05) correlation with DMFS and OS in the HNSCC and NSCLC datasets as well as with LRC in the HNSCC dataset. <i>D</i><sub>static,body</sub> and <i>D</i><sub>static,HEDOS</sub> only exhibited a statistically sign","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"27 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12745927/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145849951","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}
Xiwen Shu, Ke Lu, Jingtong Zhao, John Ginn, Yongbok Kim, Zhenyu Yang, Justus Adamson, Trey Mullikin, Chunhao Wang
Purpose
To develop a knowledge-based deep model for sCT generation from a single MR volume in LINAC-based frameless SRS, enabling the MR-only workflow without extra CT simulation.
Methods
A total of 139 patients were included in the study, with 120 used for training and 19 for testing. A Deep Residual U-Net(DRU) was developed to generate sCT from patient-specific high-resolution T1 + Contrast MR volume, complemented by a healthy brain CT volume from the Visible Human Project that provides CT-specific anatomical knowledge. To simulate treatment conditions, a template immobilization mask was deformed to align with the patient-specific sCT anatomy, thereby creating a full sCTF volume. Four metrics, including PSNR, SSIM, RMSE, and MAE were derived to evaluate Hounsfield units(HU) accuracy of sCT compared to the ground-truth CT without immobilization masks. Single isocenter multi-target SRS plans developed with volumetric modulated arc therapy (VMAT) technique were recalculated within sCTF volumes to produce simulated dose distributions, which were compared with clinical plan dose distributions using the mean dose difference in the planning target volume(PTV) and gamma index evaluation.
Results
In the test set, the generated sCT (statistics are reported as mean ± standard deviation) achieved a PSNR(Peak Signal-to-Noise Ratio) of 75 ± 4 dB, Structural Similarity Index (SSIM) of 0.99 ± 0.01, root mean square error (RMSE) of 11.9 ± 5.8 HU, and mean average error (MAE) of 1.4 ± 0.8 HU for brain tissues. When comparing sCTF dose calculation results against the original plans, gamma index passing rates were 95.8 ± 4.2% for the entire volume and 84.4 ± 15.0% within PTVs, using 3%/1 mm/15% threshold criteria. The median/ interquartile range of PTV dose differences were –2.0% and 2.3%, with all discrepancies below –5.0%.
Conclusion
This study successfully demonstrated the generation and validation of sCT images from single-modality MRI using a knowledge-based deep model. The results confirm that single-modality MRI without simulation CT scan effectively supports frameless SRS planning and integrates seamlessly into current clinical workflows.
{"title":"Knowledge-based deep residual U-Net (DRU) for synthetic CT generation using a single MR volume for frameless radiosurgery","authors":"Xiwen Shu, Ke Lu, Jingtong Zhao, John Ginn, Yongbok Kim, Zhenyu Yang, Justus Adamson, Trey Mullikin, Chunhao Wang","doi":"10.1002/acm2.70343","DOIUrl":"10.1002/acm2.70343","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>To develop a knowledge-based deep model for <i>sCT</i> generation from a single MR volume in LINAC-based frameless SRS, enabling the MR-only workflow without extra CT simulation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A total of 139 patients were included in the study, with 120 used for training and 19 for testing. A Deep Residual U-Net(DRU) was developed to generate <i>sCT</i> from patient-specific high-resolution T1 + Contrast MR volume, complemented by a healthy brain CT volume from the Visible Human Project that provides CT-specific anatomical knowledge. To simulate treatment conditions, a template immobilization mask was deformed to align with the patient-specific <i>sCT</i> anatomy, thereby creating a full <i>sCT<sub>F</sub></i> volume. Four metrics, including PSNR, SSIM, RMSE, and MAE were derived to evaluate Hounsfield units(HU) accuracy of <i>sCT</i> compared to the ground-truth CT without immobilization masks. Single isocenter multi-target SRS plans developed with volumetric modulated arc therapy (VMAT) technique were recalculated within <i>sCT<sub>F</sub></i> volumes to produce simulated dose distributions, which were compared with clinical plan dose distributions using the mean dose difference in the planning target volume(PTV) and gamma index evaluation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>In the test set, the generated <i>sCT</i> (statistics are reported as mean ± standard deviation) achieved a PSNR(Peak Signal-to-Noise Ratio) of 75 ± 4 dB, Structural Similarity Index (SSIM) of 0.99 ± 0.01, root mean square error (RMSE) of 11.9 ± 5.8 HU, and mean average error (MAE) of 1.4 ± 0.8 HU for brain tissues. When comparing <i>sCT<sub>F</sub></i> dose calculation results against the original plans, gamma index passing rates were 95.8 ± 4.2% for the entire volume and 84.4 ± 15.0% within PTVs, using 3%/1 mm/15% threshold criteria. The median/ interquartile range of PTV dose differences were –2.0% and 2.3%, with all discrepancies below –5.0%.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>This study successfully demonstrated the generation and validation of <i>sCT</i> images from single-modality MRI using a knowledge-based deep model. The results confirm that single-modality MRI without simulation CT scan effectively supports frameless SRS planning and integrates seamlessly into current clinical workflows.</p>\u0000 </section>\u0000 </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"27 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12745906/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145850073","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}
<p>While my days of leading large, multidisciplinary teams are behind me, I remain acutely aware of the staffing challenges facing medical physics across the United States. Over the course of my career, I've witnessed the full spectrum, from periods in which positions were scarce and competition for jobs was intense to the current landscape, where shortages of qualified physicists strain clinics of every size. These cycles are familiar, and I have no reason to believe they won't recur. The pressures they create—for recruitment, retention, training, and workload distribution—continue to shape the day-to-day realities of the field, regardless of whether one is practicing in an academic center or a community clinic.</p><p>It's within this context of recurring workforce pressure that conversations about structure, accountability, and resource allocation inevitably arise. On two occasions, I've been asked by health-system leadership to develop a task-based, transparent model for physics resource allocation and accountability, drawing on the relative value unit (RVU) model that governs physician work valuation. It is an appealing idea precisely because it promises clarity, fairness, and an evidence-driven basis for staffing models and workload distribution. It is also inherently challenging, raising complex questions about how to quantify cognitive work, clinical judgment, quality assurance activities, and the nonlinear effort involved in safe radiotherapy delivery. In this editorial, I reflect on the origins and current state of the RVU system, particularly as it may offer guidance—conceptual or cautionary—for developing similar frameworks in medical physics. I also share observations on what has and hasn't worked, as well as the potential pitfalls that any such system must confront if it is to serve clinical practice rather than distort it.</p><p>In the late 1980s, the U.S. healthcare system faced a growing crisis: physician payments lacked transparency, fairness, and consistency across specialties. In response, the Resource-Based Relative Value Scale (RBRVS) was developed, spearheaded by Harvard economist William Hsiao.<span><sup>1, 2</sup></span> Shortly thereafter, the Omnibus Budget Reconciliation Act of 1989 authorized the creation of the Medicare Fee Schedule (MFS) and the use of RBRVS for Medicare physician payments. The MFS, which went into effect on January 1, 1992, used RVUs to assign specific payment amounts for each service. The goals of the RVU system were well-intentioned: to allocate value in proportion to time, effort, skill, and risk, and ultimately, to address the issue of rising medical costs by creating a system that incentivizes efficient and effective care while curbing specialist-driven cost inflation.</p><p>At its inception, the RVU model was a rational attempt to bring structure to physician reimbursement. The physician work component—which includes time, mental effort, technical skill, and stress—was meant to recognize the c
虽然我领导大型多学科团队的日子已经过去了，但我仍然敏锐地意识到美国医学物理学面临的人员配备挑战。在我的职业生涯中，我见证了各种各样的情况，从职位稀缺、工作竞争激烈的时期，到目前合格物理学家的短缺给各种规模的诊所带来压力的局面。这些周期很熟悉，我没有理由相信它们不会再发生。他们创造的压力——招聘、保留、培训和工作量分配——继续塑造着该领域的日常现实，无论一个人是在学术中心还是在社区诊所执业。正是在这种反复出现的劳动力压力背景下，有关结构、责任和资源分配的讨论不可避免地出现了。有两次，卫生系统领导要求我利用管理医生工作评估的相对价值单位（RVU）模型，为物理资源分配和问责制开发一个基于任务的透明模型。这是一个吸引人的想法，因为它保证了人员配置模型和工作量分配的清晰、公平和证据驱动的基础。它本身也具有挑战性，提出了关于如何量化认知工作、临床判断、质量保证活动以及涉及安全放射治疗递送的非线性努力的复杂问题。在这篇社论中，我反思了RVU系统的起源和现状，特别是它可能为医学物理学中类似框架的发展提供指导——概念上的或警告性的。我还分享了我对哪些有效哪些无效的观察，以及任何此类系统如果要服务于临床实践，而不是扭曲临床实践，就必须面对的潜在陷阱。在20世纪80年代后期，美国医疗保健系统面临着一个日益严重的危机：医生的薪酬缺乏透明度、公平性和跨专业的一致性。作为回应，资源基础相对价值量表（Resource-Based Relative Value Scale， RBRVS）被开发出来，由哈佛大学经济学家萧威廉（William hsiao2）率先提出。此后不久，1989年的《综合预算调节法案》授权建立医疗保险费用表（MFS），并使用RBRVS支付医疗保险医生的费用。MFS于1992年1月1日生效，它使用rvu为每项服务指定具体的付款金额。RVU系统的目标是善意的：按时间、努力、技能和风险的比例分配价值，最终，通过创建一个激励高效和有效护理的系统来解决医疗成本上升的问题，同时抑制由专家驱动的成本膨胀。最初，RVU模式是一种合理的尝试，旨在为医生报销带来结构。医生的工作组成部分——包括时间、精神努力、技术技能和压力——旨在识别不同服务的复杂性和劳动强度。在接下来的三十年里，RVU系统已经钙化到美国医疗保健的每个角落。随着它的扩展，它使医疗保健领域内庞大的管理基础设施得以发展，逐渐以一种令人深感不安的方式重塑临床行为。因此，以rvu为中心的补偿方案扭曲了提供者的行为，奖励数量超过价值，程序超过思考，病人吞吐量超过长期护理。此外，随着商业保险公司采用该系统，rvu从报销工具演变为监督和激励工具在大多数卫生系统中，医生的报酬现在直接与RVU的产出挂钩，通常是通过基于生产力的奖金或最低工资来实现的。最初的改革体系——一个更加公平、基于证据的定价体系——在市场压力下破裂了，但RVU本身依然存在。它的逻辑可能被打破了，但它的遗产仍然存在，影响着生产率指标、工资奖金，甚至是机构声望。具有讽刺意味的是，为医生报销带来透明度、公平性和一致性的最初目标已经成为美国医疗保健成本通胀的驱动因素。数量激励导致过度使用。程序偏见和专业膨胀倾向于干预，而不是初级保健和预防服务，专业协会通常主张增加其程序的RVU评估。与编码、计费、优化、遵从性和跟踪RVU产品相关的管理复杂性导致了过多的操作开销。此外，虽然RVU系统旨在衡量和奖励临床工作效率，但学术环境往往明确或隐含地表明，花在临床护理上的时间不如花在会议、撰写拨款、完成认证任务、指导学员或在委员会任职上的时间有价值。 承担这些角色的教师通常被视为“团队成员”，并获得能见度、领导机会或晋升的奖励——然而，在基于rvu的薪酬模式下，这些任务很少被跟踪、资助或保护。意想不到的结果是一种文化，在这种文化中，临床医生可能在结构上和声誉上受到激励，不去看病人。看更少的病人意味着更少的病毒，但也减少了安排头痛，减少了临床风险，并更自由地参与学术声望经济。随着时间的推移，这种动态扭曲了工作量和士气，因为各部门努力保持临床访问，同时过度奖励行政参与。如果没有经过深思熟虑的努力来量化和平衡这些相互竞争的需求，RVU框架不仅会变得不充分，而且会变得具有腐蚀性。为医学物理学建立一个类似rvu的系统与上述所有缺点相关，还有一些额外的独特挑战。首先，没有集中的物理RVU权威，因为有通过AMA/专业协会RVS更新委员会（RUC）和CMS的医生服务。如果没有这样一个裁决机构，即使在单个机构内，也很难建立和维护跨不同任务的标准化工作基线。物理工作是多种多样的，从直接的病人支持，如治疗计划和特殊程序，到质量保证、设备校准、辐射安全、研究和教育。物理工作本身也是可变的——不同的治疗方式（直线加速器、伽玛刀、质子治疗、近距离治疗）、不同的机构资源、不同的病例组合、不同的预期——我们的大部分工作是预防性的，减少风险，而不是产生可计费的事件。试图将严格的价值观强加于这种异构的工作，不可避免地会过度简化任务，并鼓励“复选框”行为，在这种行为中，个人追求小部件，努力的可数替代品，而不是关注患者安全、创新、解决问题或积极的智力参与。与医学或外科手术程序不同，这些活动不能整齐地映射到离散的CPT代码或工作单元。相反，它的价值是不那么有形的，部分原因是大部分工作本质上是预防性的，减少了风险，而不是生成复选框。我亲身经历过。通过仔细的设计过程，我们开发了一个类似于rbu的系统，该系统借鉴了适当的基准参考，包括Abt报告、ASTRO和IAEA指南等，并听取了整个部门的意见和公开讨论。我们进行了试点，征求了反馈意见，并相应地调整了工作价值。在6个月的执行过程中，很明显我们失败了；由于上述所有原因，这一进程失败了。从rvu学分成为一种货币的那一刻起，人们开始保护自己的任务，越来越多地对察觉到的不公平现象发出声音，更关注记录工作成果，而不是做出有意义的贡献。同事之间的攀比升级了，小不满扩大了，合作的本能让位于捍卫自己人数的本能。不属于工作范畴的工作很快就会让人觉得是无偿劳动。我们引入了一个道德风险：个人被激励去优化他们自己的可衡量产出，即使这样做与集体利益相冲突。正如一位同事恰如其分地指出的那样：“我们已经失去了正常的人际交往。”在像医学物理学这样的安全关键领域，这不是一个理论上的问题；这是一种结构性风险。回想起来，结果是可以预测的；医学物理学的真正价值不在于计算部件，计算时间，或者将自己的产出与同事的产出进行比较，而在于通过合作，判断和分担责任来保护患者。我们所做的工作从根本上来说是关系性的和情境性的：风险不是通过个人的生产力度量来减少的，而是通过集体意识、共享的专业知识和持续的信息交换来减少的，这些信息交换可以防止小问题变成大问题。当专业人士相互信任并并肩工作时，没有电子表格可以代替非正式的谈话、快速的问题和意外的见解。人们很容易相信，更复杂的工具、自动跟踪系统、人工智能衍生的工作量估计或越来越精细的任务日志可能会在早期类似rvu的模型失败的地方取得成功。我的经验告诉我并非如此。提高测量精度并不能解决根本问题；它往往通过进一步取代信任、判断和共同责任来加速它。最后，我们用简单得多的东西取代了类似rvu的框架——物理学家每天坐在诊所里，共同处理任务和
{"title":"What an RVU experiment taught me about Medical Physics","authors":"Timothy D. Solberg","doi":"10.1002/acm2.70451","DOIUrl":"10.1002/acm2.70451","url":null,"abstract":"<p>While my days of leading large, multidisciplinary teams are behind me, I remain acutely aware of the staffing challenges facing medical physics across the United States. Over the course of my career, I've witnessed the full spectrum, from periods in which positions were scarce and competition for jobs was intense to the current landscape, where shortages of qualified physicists strain clinics of every size. These cycles are familiar, and I have no reason to believe they won't recur. The pressures they create—for recruitment, retention, training, and workload distribution—continue to shape the day-to-day realities of the field, regardless of whether one is practicing in an academic center or a community clinic.</p><p>It's within this context of recurring workforce pressure that conversations about structure, accountability, and resource allocation inevitably arise. On two occasions, I've been asked by health-system leadership to develop a task-based, transparent model for physics resource allocation and accountability, drawing on the relative value unit (RVU) model that governs physician work valuation. It is an appealing idea precisely because it promises clarity, fairness, and an evidence-driven basis for staffing models and workload distribution. It is also inherently challenging, raising complex questions about how to quantify cognitive work, clinical judgment, quality assurance activities, and the nonlinear effort involved in safe radiotherapy delivery. In this editorial, I reflect on the origins and current state of the RVU system, particularly as it may offer guidance—conceptual or cautionary—for developing similar frameworks in medical physics. I also share observations on what has and hasn't worked, as well as the potential pitfalls that any such system must confront if it is to serve clinical practice rather than distort it.</p><p>In the late 1980s, the U.S. healthcare system faced a growing crisis: physician payments lacked transparency, fairness, and consistency across specialties. In response, the Resource-Based Relative Value Scale (RBRVS) was developed, spearheaded by Harvard economist William Hsiao.<span><sup>1, 2</sup></span> Shortly thereafter, the Omnibus Budget Reconciliation Act of 1989 authorized the creation of the Medicare Fee Schedule (MFS) and the use of RBRVS for Medicare physician payments. The MFS, which went into effect on January 1, 1992, used RVUs to assign specific payment amounts for each service. The goals of the RVU system were well-intentioned: to allocate value in proportion to time, effort, skill, and risk, and ultimately, to address the issue of rising medical costs by creating a system that incentivizes efficient and effective care while curbing specialist-driven cost inflation.</p><p>At its inception, the RVU model was a rational attempt to bring structure to physician reimbursement. The physician work component—which includes time, mental effort, technical skill, and stress—was meant to recognize the c","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"27 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12746431/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145850125","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}
Hydration of patients before the PET/CT examination may lead to non-specific FDG accumulation in the intestine. This study aims to explore the influence of isotonic saline and hypotonic water on physiologic intestinal 18F-FDG uptake in PET/CT imaging.
Methods
185 patients were included, 82 patients were randomized to receive oral administration of isotonic saline and 103 patients received oral administration of hypotonic water before examination. About 32 patients in the isotonic group and 19 patients in hypotonic group underwent repeated PET/CT with pre-administration of hypotonic water, and the intra-individual comparative analysis was performed for the first and second examinations. Segmental uptake patterns were documented and volumetric regions of interest were delineated. The SUVmax of intestinal segment and the SUVmean of liver were recorded.
Results
No significant differences existed in baseline characteristics between the two groups. The isotonic group demonstrated lower physiological uptake incidence and target-to-background ratio (TBR) in all segments except jejunum and ascending colon (p < 0.05). Physiological uptake incidence in jejunum showed no intergroup difference, whereas the TBR of isotonic group showed lower tendency. Notably, the isotonic group exhibited higher physiological uptake incidence (p < 0.001) in the ascending colon, though no significant difference existed in TBR between two groups. These results were similar in intra-individual comparative analysis of the first and second examinations. Moreover, constipation was positively associated with FDG uptake in the ileum, diarrhea was positively associated with FDG uptake in the transverse colon, ascending colon and rectum. Bone tumors and gynecological tumors were positively correlated with FDG uptake in the ascending colon and transverse colon. BMI was negatively correlated with FDG uptake in the duodenum, ileum, jejunum, and sigmoid colon.
Conclusion
Pre-administration of isotonic saline resulted in lower physiologic 18F-FDG uptake in different intestine segments (except ascending colon), which is helpful for accurate assessment of gastrointestinal diseases.
{"title":"Effect of oral hypotonic water and isotonic saline on physiological intestinal 18F-FDG uptake in PET/CT imaging","authors":"Shijia Weng, Yuan Li, Xiaoren Liu, Qian Wang","doi":"10.1002/acm2.70431","DOIUrl":"10.1002/acm2.70431","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>Hydration of patients before the PET/CT examination may lead to non-specific FDG accumulation in the intestine. This study aims to explore the influence of isotonic saline and hypotonic water on physiologic intestinal <sup>18</sup>F-FDG uptake in PET/CT imaging.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>185 patients were included, 82 patients were randomized to receive oral administration of isotonic saline and 103 patients received oral administration of hypotonic water before examination. About 32 patients in the isotonic group and 19 patients in hypotonic group underwent repeated PET/CT with pre-administration of hypotonic water, and the intra-individual comparative analysis was performed for the first and second examinations. Segmental uptake patterns were documented and volumetric regions of interest were delineated. The SUVmax of intestinal segment and the SUVmean of liver were recorded.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>No significant differences existed in baseline characteristics between the two groups. The isotonic group demonstrated lower physiological uptake incidence and target-to-background ratio (TBR) in all segments except jejunum and ascending colon (<i>p </i>< 0.05). Physiological uptake incidence in jejunum showed no intergroup difference, whereas the TBR of isotonic group showed lower tendency. Notably, the isotonic group exhibited higher physiological uptake incidence (<i>p </i>< 0.001) in the ascending colon, though no significant difference existed in TBR between two groups. These results were similar in intra-individual comparative analysis of the first and second examinations. Moreover, constipation was positively associated with FDG uptake in the ileum, diarrhea was positively associated with FDG uptake in the transverse colon, ascending colon and rectum. Bone tumors and gynecological tumors were positively correlated with FDG uptake in the ascending colon and transverse colon. BMI was negatively correlated with FDG uptake in the duodenum, ileum, jejunum, and sigmoid colon.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Pre-administration of isotonic saline resulted in lower physiologic <sup>18</sup>F-FDG uptake in different intestine segments (except ascending colon), which is helpful for accurate assessment of gastrointestinal diseases.</p>\u0000 </section>\u0000 </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"27 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12746345/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145850012","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}
Qiang Han, Songlin He, Yuebin Zheng, Huacai Zhong, Dengyao Luo, Jun Wu, Zhiqiang Zhao, Bincheng Yan, Chengjian Cao, Xiu Liu
Background
Head and neck cancer (HNC) involves anatomically intricate regions where precise target delineation is essential for radiotherapy. The superior soft-tissue contrast of MRI provides clearer boundary visualization compared with computed tomography (CT), enabling tighter margins and supporting daily plan adaptation in online adaptive radiotherapy. However, despite the advances of U-Net-based deep learning models, tumor segmentation in HNC remains challenging due to ill-defined borders and heterogeneous intensity patterns, which limit feature extraction and compromise small-lesion recognition.
Purpose
To overcome the limitations of traditional approaches, this study proposes an improved SCDU-Net model that integrates collaborative spatial-channel attention mechanisms with densely connected atrous spatial pyramid pooling techniques, aiming to significantly enhance the segmentation accuracy and robustness of head and neck tumor MRI images.
Methods
SCDU-Net integrates two modified modules to improve segmentation capability. The model incorporates a spatial-channel dual attention module (SC) in the decoding pathway, which strengthens critical tumor feature expression through adaptive channel weight adjustment mechanisms, while capturing long-range spatial dependencies using coordinate axis attention to improve localization accuracy of small target lesions. Additionally, the network embeds a densely connected atrous spatial pyramid pooling module dense atrous spatial pyramid pooling (DenseASPP) in the bottleneck layer, which enhances edge contour detail perception through multi-scale receptive field fusion strategies, improving the network's segmentation performance.
Results
Our proposed model is evaluated on the publicly available HNTS-MRG2024 dataset, showing promising results compared to existing approaches.
Conclusions
The results indicate that by integrating two modules, our method performs spatial and channel feature recalibration and multi-scale contextual modeling within the deep neural network, yielding more accurate and promising head and neck tumor segmentation with potential to assist physicians in diagnosis.
{"title":"MRI segmentation of head and neck tumors using hybrid attention mechanism and dense dilated spatial pyramid pooling","authors":"Qiang Han, Songlin He, Yuebin Zheng, Huacai Zhong, Dengyao Luo, Jun Wu, Zhiqiang Zhao, Bincheng Yan, Chengjian Cao, Xiu Liu","doi":"10.1002/acm2.70426","DOIUrl":"10.1002/acm2.70426","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Head and neck cancer (HNC) involves anatomically intricate regions where precise target delineation is essential for radiotherapy. The superior soft-tissue contrast of MRI provides clearer boundary visualization compared with computed tomography (CT), enabling tighter margins and supporting daily plan adaptation in online adaptive radiotherapy. However, despite the advances of U-Net-based deep learning models, tumor segmentation in HNC remains challenging due to ill-defined borders and heterogeneous intensity patterns, which limit feature extraction and compromise small-lesion recognition.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>To overcome the limitations of traditional approaches, this study proposes an improved SCDU-Net model that integrates collaborative spatial-channel attention mechanisms with densely connected atrous spatial pyramid pooling techniques, aiming to significantly enhance the segmentation accuracy and robustness of head and neck tumor MRI images.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>SCDU-Net integrates two modified modules to improve segmentation capability. The model incorporates a spatial-channel dual attention module (SC) in the decoding pathway, which strengthens critical tumor feature expression through adaptive channel weight adjustment mechanisms, while capturing long-range spatial dependencies using coordinate axis attention to improve localization accuracy of small target lesions. Additionally, the network embeds a densely connected atrous spatial pyramid pooling module dense atrous spatial pyramid pooling (DenseASPP) in the bottleneck layer, which enhances edge contour detail perception through multi-scale receptive field fusion strategies, improving the network's segmentation performance.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Our proposed model is evaluated on the publicly available HNTS-MRG2024 dataset, showing promising results compared to existing approaches.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The results indicate that by integrating two modules, our method performs spatial and channel feature recalibration and multi-scale contextual modeling within the deep neural network, yielding more accurate and promising head and neck tumor segmentation with potential to assist physicians in diagnosis.</p>\u0000 </section>\u0000 </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"27 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12746348/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145850029","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}
Alexander von Hessling, Carlos Vinícius Gomes, Pia Niederau, Thiago Lima, Justus Roos, Natalia Saltybaeva
Background
Computed tomography is a highly effective diagnostic tool for evaluating pathological conditions of the temporal bone. However, the relatively high radiation doses pose risks to radiosensitive organs, making dose optimization essential in accordance with the ALARA principle. Recent technological advances, such as tin-based photon-shaping filters, have enabled significant dose reduction in both dual- and single-energy CT without compromising image quality.
Purpose
To evaluate the impact of a spectral shaping tin filter on image quality and radiation dose in temporal bone CT examinations.
Methods
Thirty-six patients undergoing temporal bone CT were retrospectively analysed. Group A (n = 13) underwent standard CT, while Group B (n = 23) underwent CT examination using 150 kVp beam with additional tin filtration. Images were reconstructed with model-based iterative reconstruction (ADMIRE, level 3) and assessed by two neuroradiologists using a 3-point Likert scale. Radiation dose was quantified via CTDIvol, DLP, and eye lens dose using Monte Carlo simulations.
Results
Both protocols provided diagnostic-quality images. The protocol with tin filter achieved slightly superior image quality (median scores: overall 3.00 vs. 2.88, contrast 3.00 vs. 2.96, resolution 2.58 vs. 2.53; p < 0.05) with substantial inter-rater agreement (κ = 0.75–0.97). Mean CTDIvol and DLP decreased from 59.1 ± 12.1 mGy and 551.8 ± 226.8 mGy×cm (Group A) to 39.9 ± 1.7 mGy and 302.4 ± 33.8 mGy×cm (Group B) (p < 0.001). Eye lens dose was reduced by 34% (54.7 ± 10.9 mGy vs. 36.4 ± 2.2 mGy, p < 0.001).
Conclusions
Incorporating a tin filter into temporal bone CT significantly reduces radiation exposure, including lens dose, without compromising—and in some cases slightly improving—image quality. Routine implementation of spectral shaping may enhance patient safety in clinical practice.
背景：计算机断层扫描是评估颞骨病理状况的一种非常有效的诊断工具。然而，相对较高的辐射剂量对辐射敏感器官构成风险，因此根据ALARA原则进行剂量优化至关重要。最近的技术进步，如锡基光子整形滤波器，在不影响图像质量的情况下，大大降低了双能和单能CT的剂量。目的：探讨光谱成形锡滤光片对颞骨CT成像质量和辐射剂量的影响。方法：回顾性分析36例颞骨CT患者的临床资料。A组（n = 13）行标准CT检查，B组（n = 23）行150kvp波束加锡过滤CT检查。使用基于模型的迭代重建（钦佩，3级）重建图像，并由两名神经放射学家使用3点李克特量表进行评估。使用蒙特卡罗模拟，通过CTDIvol， DLP和眼晶状体剂量来量化辐射剂量。结果：两种方案均可提供诊断质量的图像。使用锡滤光片的方案获得了稍好的图像质量（中位数评分：总体3.00 vs 2.88，对比度3.00 vs 2.96，分辨率2.58 vs 2.53）； p结论：将锡滤光片纳入颞骨CT可显著减少辐射暴露，包括透镜剂量，而不影响图像质量，在某些情况下略有改善。在临床实践中，谱整形的常规实施可以提高患者的安全性。
{"title":"Dose optimization for CT scans of the temporal bone using spectral shaping tin filter","authors":"Alexander von Hessling, Carlos Vinícius Gomes, Pia Niederau, Thiago Lima, Justus Roos, Natalia Saltybaeva","doi":"10.1002/acm2.70359","DOIUrl":"10.1002/acm2.70359","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Computed tomography is a highly effective diagnostic tool for evaluating pathological conditions of the temporal bone. However, the relatively high radiation doses pose risks to radiosensitive organs, making dose optimization essential in accordance with the ALARA principle. Recent technological advances, such as tin-based photon-shaping filters, have enabled significant dose reduction in both dual- and single-energy CT without compromising image quality.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>To evaluate the impact of a spectral shaping tin filter on image quality and radiation dose in temporal bone CT examinations.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Thirty-six patients undergoing temporal bone CT were retrospectively analysed. Group A (<i>n</i> = 13) underwent standard CT, while Group B (<i>n</i> = 23) underwent CT examination using 150 kVp beam with additional tin filtration. Images were reconstructed with model-based iterative reconstruction (ADMIRE, level 3) and assessed by two neuroradiologists using a 3-point Likert scale. Radiation dose was quantified via CTDIvol, DLP, and eye lens dose using Monte Carlo simulations.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Both protocols provided diagnostic-quality images. The protocol with tin filter achieved slightly superior image quality (median scores: overall 3.00 vs. 2.88, contrast 3.00 vs. 2.96, resolution 2.58 vs. 2.53; <i>p</i> < 0.05) with substantial inter-rater agreement (<i>κ</i> = 0.75–0.97). Mean CTDIvol and DLP decreased from 59.1 ± 12.1 mGy and 551.8 ± 226.8 mGy×cm (Group A) to 39.9 ± 1.7 mGy and 302.4 ± 33.8 mGy×cm (Group B) (<i>p</i> < 0.001). Eye lens dose was reduced by 34% (54.7 ± 10.9 mGy vs. 36.4 ± 2.2 mGy, <i>p</i> < 0.001).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Incorporating a tin filter into temporal bone CT significantly reduces radiation exposure, including lens dose, without compromising—and in some cases slightly improving—image quality. Routine implementation of spectral shaping may enhance patient safety in clinical practice.</p>\u0000 </section>\u0000 </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"27 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12745887/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145849953","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}
Wenlong Xia, Bin Liang, Kuo Men, Ke Zhang, Yuan Tian, Ningning Lu, Jianrong Dai
Background and purpose
In MR-guided adaptive radiotherapy (MRgART), adaptive strategies are currently mainly determined through subjective review of anatomical changes. Machine learning (ML) models based on deformable image registration (DIR) have been developed to predict type of adaptive strategy: adapt to position (ATP) versus adapt to shape (ATS). However, subjective review may result in sub-optimal plans and DIR processing for ML models can be time-consuming. This study aims to develop a deep learning (DL) model that uses images as input data for fast and accurate adaptive strategy selection.
Methods
Data from 180 fractions of 36 prostate cancer patients were used retrospectively for this study. The optimal adaptive strategy was determined between ATP and ATS according to dosimetric evaluation. A multi-stage network method was proposed and used for adaptive strategy prediction. A DL-based image registration (DLIR) network was first trained to register the reference image to the daily image. Then, a DL-based adaptive strategy prediction (DLSP) model was constructed and trained using the encoder section of the trained DLIR network. Data from 24 patients were used for training, while the data from the remaining 12 patients formed the independent test set.
Results
The DLSP model demonstrated high performance with an area under the curve (AUC) value of 0.861, and corresponding accuracy (ACC), sensitivity (SEN), and specificity (SPC) were 0.867, 0.898, and 0.727, respectively. The DL prediction process required approximately 2.5 min, representing a 5-fold improvement in efficiency over the existing ML method.
Conclusions
The DL-based model could provide fast and accurate adaptive strategy selection, which further improves the efficiency of the MRgART process.
{"title":"A deep-learning framework for the prediction of the type of adaptive strategy of MR-guided prostate radiotherapy","authors":"Wenlong Xia, Bin Liang, Kuo Men, Ke Zhang, Yuan Tian, Ningning Lu, Jianrong Dai","doi":"10.1002/acm2.70395","DOIUrl":"10.1002/acm2.70395","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background and purpose</h3>\u0000 \u0000 <p>In MR-guided adaptive radiotherapy (MRgART), adaptive strategies are currently mainly determined through subjective review of anatomical changes. Machine learning (ML) models based on deformable image registration (DIR) have been developed to predict type of adaptive strategy: adapt to position (ATP) versus adapt to shape (ATS). However, subjective review may result in sub-optimal plans and DIR processing for ML models can be time-consuming. This study aims to develop a deep learning (DL) model that uses images as input data for fast and accurate adaptive strategy selection.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Data from 180 fractions of 36 prostate cancer patients were used retrospectively for this study. The optimal adaptive strategy was determined between ATP and ATS according to dosimetric evaluation. A multi-stage network method was proposed and used for adaptive strategy prediction. A DL-based image registration (DLIR) network was first trained to register the reference image to the daily image. Then, a DL-based adaptive strategy prediction (DLSP) model was constructed and trained using the encoder section of the trained DLIR network. Data from 24 patients were used for training, while the data from the remaining 12 patients formed the independent test set.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The DLSP model demonstrated high performance with an area under the curve (AUC) value of 0.861, and corresponding accuracy (ACC), sensitivity (SEN), and specificity (SPC) were 0.867, 0.898, and 0.727, respectively. The DL prediction process required approximately 2.5 min, representing a 5-fold improvement in efficiency over the existing ML method.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The DL-based model could provide fast and accurate adaptive strategy selection, which further improves the efficiency of the MRgART process.</p>\u0000 </section>\u0000 </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"27 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12746477/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145850014","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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