Pub Date : 2026-01-01Epub Date: 2025-12-21DOI: 10.1016/j.phro.2025.100897
Sara Bornedal , Jeehong Lee , Tim Melhus , Anna Embring , Eva Onjukka
When reirradiation dose constraints are derived using accumulated dose, the underlying image registrations contribute to the uncertainty. We performed a structure-based evaluation of deformable image registrations, to estimate the uncertainty in previously published dose constraints related to carotid blowout and osteoradionecrosis after head and neck reirradiation. With the workflow of the current analysis, the uncertainty was small in the majority of the cases (<4 Gy in accumulated equivalent dose in 2-Gy fractions), but with substantial outliers resulting from anatomical alterations. Our previously suggested dose constraints appear to be reliable with regard to the underlying image registrations.
{"title":"Accuracy of reirradiation dose constraints for the mandible and carotids","authors":"Sara Bornedal , Jeehong Lee , Tim Melhus , Anna Embring , Eva Onjukka","doi":"10.1016/j.phro.2025.100897","DOIUrl":"10.1016/j.phro.2025.100897","url":null,"abstract":"<div><div>When reirradiation dose constraints are derived using accumulated dose, the underlying image registrations contribute to the uncertainty. We performed a structure-based evaluation of deformable image registrations, to estimate the uncertainty in previously published dose constraints related to carotid blowout and osteoradionecrosis after head and neck reirradiation. With the workflow of the current analysis, the uncertainty was small in the majority of the cases (<4 Gy in accumulated equivalent dose in 2-Gy fractions), but with substantial outliers resulting from anatomical alterations. Our previously suggested dose constraints appear to be reliable with regard to the underlying image registrations.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"37 ","pages":"Article 100897"},"PeriodicalIF":3.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145842006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2026-02-06DOI: 10.1016/j.phro.2026.100919
Sara Abdollahi , Rachid Boucenna , Cécile Chatelain , Nathan Corradini , Marie Fargier-Voiron , Vincent Fave , Juan Garcia , Sarah Ghandour , Matthias Guckenberger , Käthy Haller , Martin Härtig , Tanja Hertel , Maud Jaccard , Stephan Klöck , Jérôme Krayenbühl , Natacha Ruiz López , Philippe Logaritsch , Peter Pemler , Harald Petermann , Olivier Pisaturo , Stephanie Tanadini-Lang
Background and purpose
Stereotactic radiotherapy (SRT) is a standard approach for treating multiple brain metastases. However, variation in planning practices may impact treatment quality. This study assessed planning consistency and dose–volume–based outcomes across radiation oncology centers.
Materials and methods
A Computed Tomography (CT) scan of an anthropomorphic phantom with structure set was distributed to participating centers. Each center created SRT plans as for a clinical case. Dose distributions were evaluated based on Planning Target Volume (PTV) coverage (V100% (PTV)), dose to 95% of Gross Target Volume (GTV) volume (D95% (GTV)), maximal PTV dose (Dmax), conformity index (CI), gradient index (GI), brain volume receiving different percentages of the prescribed dose, and doses delivered to 0.035 cm3 and 0.5 cm3 of the brainstem.
Results
Twenty-four centers, using 30 treatment units, submitted plans. The V100% (PTV) ranged from 95% to 100%, with Dmax between 110% and 150% of the prescribed dose. Mean GTV dose ranged from 110% to 135%, and 81% of GTVs had D95% between 110% and 120%. High conformity was achieved in 74% of plans (CI < 1.1), while 67% had a GI between 3.4 and 5. All plans met clinical dose constraints for the brainstem and uninvolved brain.
Conclusion
This interinstitutional comparison demonstrated high plan quality and adherence to critical organ constraints, despite variability in planning strategies. These findings support nationwide planning and quality assurance standards to ensure consistently high-quality SRT.
{"title":"Multicenter evaluation of planning quality in intracranial stereotactic radiotherapy for brain metastases","authors":"Sara Abdollahi , Rachid Boucenna , Cécile Chatelain , Nathan Corradini , Marie Fargier-Voiron , Vincent Fave , Juan Garcia , Sarah Ghandour , Matthias Guckenberger , Käthy Haller , Martin Härtig , Tanja Hertel , Maud Jaccard , Stephan Klöck , Jérôme Krayenbühl , Natacha Ruiz López , Philippe Logaritsch , Peter Pemler , Harald Petermann , Olivier Pisaturo , Stephanie Tanadini-Lang","doi":"10.1016/j.phro.2026.100919","DOIUrl":"10.1016/j.phro.2026.100919","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Stereotactic radiotherapy (SRT) is a standard approach for treating multiple brain metastases. However, variation in planning practices may impact treatment quality. This study assessed planning consistency and dose–volume–based outcomes across radiation oncology centers.</div></div><div><h3>Materials and methods</h3><div>A Computed Tomography (CT) scan of an anthropomorphic phantom with structure set was distributed to participating centers. Each center created SRT plans as for a clinical case. Dose distributions were evaluated based on Planning Target Volume (PTV) coverage (V<sub>100</sub>% (PTV)), dose to 95% of Gross Target Volume (GTV) volume (D<sub>95</sub>% (GTV)), maximal PTV dose (D<sub>max</sub>), conformity index (CI), gradient index (GI), brain volume receiving different percentages of the prescribed dose, and doses delivered to 0.035 cm<sup>3</sup> and 0.5 cm<sup>3</sup> of the brainstem.</div></div><div><h3>Results</h3><div>Twenty-four centers, using 30 treatment units, submitted plans. The V<sub>100</sub>% (PTV) ranged from 95% to 100%, with D<sub>max</sub> between 110% and 150% of the prescribed dose. Mean GTV dose ranged from 110% to 135%, and 81% of GTVs had D<sub>95</sub>% between 110% and 120%. High conformity was achieved in 74% of plans (CI < 1.1), while 67% had a GI between 3.4 and 5. All plans met clinical dose constraints for the brainstem and uninvolved brain.</div></div><div><h3>Conclusion</h3><div>This interinstitutional comparison demonstrated high plan quality and adherence to critical organ constraints, despite variability in planning strategies. These findings support nationwide planning and quality assurance standards to ensure consistently high-quality SRT.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"37 ","pages":"Article 100919"},"PeriodicalIF":3.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146228827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2026-01-07DOI: 10.1016/j.phro.2026.100901
Linus A. Carizzoni , Alexey Cherchik , Xia Li , Antony Lomax , Ye Zhang
Background and purpose
The robustness of pencil beam scanned (PBS) proton plans to respiratory motion is often assessed in clinical practice by static 4D dose recalculations on selected 4D computed tomography (4DCT) phases. These capture anatomical variation but neglect interplay effects from sequential beam delivery. This study investigates these effects by comparing static and dynamic 4DDC for esophageal cancer patients.
Materials and methods
PBS proton plans following the PROTECT trial protocol were created for ten esophageal cancer patients from the open-access DIR-Lab 4DCT dataset. Plan robustness was evaluated by static and dynamic 4DDC, where the static approach accumulated the computed dose in individual 4DCT phases, while dynamic incorporated the temporal delivery sequence to capture interplay effects. The two 4DDCs were compared by their compliance to the dose restrictions for target volumes and organs at risk (OARs)
Results
Static 4DDC consistently predicted higher target coverage than dynamic approach. Discrepancies were most pronounced in patients with substantial target motion (≳10 mm). However, dose metrics for the OARs showed high agreement between the two methods. Compliance with the clinical constraint on target coverage (V95%>97 %) was achieved in 100 % and 70 % of static and dynamic 4D recalculations. Rescanning improved the compliance of target coverage to 90 %.
Conclusion
Protocol-based static 4DDC tended to overestimate target coverage robustness to respiratory motion. Although differences were minor in most cases, patients with large motion can have significant discrepancies, underscoring the importance of implementing dynamic 4DDC in PBS proton planning for esophageal cancer.
{"title":"Comparative evaluation of static and dynamic 4D dose recalculations in pencil beam scanning proton therapy for oesophageal cancer","authors":"Linus A. Carizzoni , Alexey Cherchik , Xia Li , Antony Lomax , Ye Zhang","doi":"10.1016/j.phro.2026.100901","DOIUrl":"10.1016/j.phro.2026.100901","url":null,"abstract":"<div><h3>Background and purpose</h3><div>The robustness of pencil beam scanned (PBS) proton plans to respiratory motion is often assessed in clinical practice by static 4D dose recalculations on selected 4D computed tomography (4DCT) phases. These capture anatomical variation but neglect interplay effects from sequential beam delivery. This study investigates these effects by comparing static and dynamic 4DDC for esophageal cancer patients.</div></div><div><h3>Materials and methods</h3><div>PBS proton plans following the PROTECT trial protocol were created for ten esophageal cancer patients from the open-access DIR-Lab 4DCT dataset. Plan robustness was evaluated by static and dynamic 4DDC, where the static approach accumulated the computed dose in individual 4DCT phases, while dynamic incorporated the temporal delivery sequence to capture interplay effects. The two 4DDCs were compared by their compliance to the dose restrictions for target volumes and organs at risk (OARs)</div></div><div><h3>Results</h3><div>Static 4DDC consistently predicted higher target coverage than dynamic approach. Discrepancies were most pronounced in patients with substantial target motion (≳10 mm). However, dose metrics for the OARs showed high agreement between the two methods. Compliance with the clinical constraint on target coverage (V<sub>95%</sub> <em>></em>97 %) was achieved in 100 % and 70 % of static and dynamic 4D recalculations. Rescanning improved the compliance of target coverage to 90 %.</div></div><div><h3>Conclusion</h3><div>Protocol-based static 4DDC tended to overestimate target coverage robustness to respiratory motion. Although differences were minor in most cases, patients with large motion can have significant discrepancies, underscoring the importance of implementing dynamic 4DDC in PBS proton planning for esophageal cancer.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"37 ","pages":"Article 100901"},"PeriodicalIF":3.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145939700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2025-12-11DOI: 10.1016/j.phro.2025.100890
Mathieu Gaudreault , Lachlan McIntosh , Katrina Woodford , Jason Li , Susan Harden , Sandro Porceddu , Nicholas Hardcastle , Vanessa Panettieri
Background and purpose
The monitor units (MU) per control point (CP) control the necessary fine-tuned ablative dose for hypofractionated radiotherapy of oligometastatic cancer. We aimed to introduce strategies maximising the sample size to accurately predict the MU per CP with artificial intelligence (AI).
Materials and methods
The 40/68/88 treatment plans of consecutive patients treated between 01/2019 and 06/2024 at our institution for metastatic cancer to the liver/bone/lung were included. Two approaches were considered to maximise the sample size. In one approach, the samples of each anatomical site were extensively augmented to predict the MU per CP from the dose distribution per CP, providing the MU per beam and meterset weight per CP. In the other approach, all samples from all anatomical sites were combined for training. The number of achieved clinical goals based on dose-volume calculation metrics in AI radiotherapy plans (AI-RTPlan) was compared with the number of achieved clinical goals in the clinical plans.
Results
The mean absolute percentage error between predicted and clinical MU per beam/meterset weight per CP was less than 6.2%. All AI-RTPlans were generated in less than 5 s. At least 90%/5% of patients had the same, or more, achieved clinical goals with AI-RTPlans. Target coverage and dose to organs at risk metrics were within ± 2% and ± 2.3 Gy of the clinical value in all patients, respectively.
Conclusions
Augmenting data extensively and combining anatomical sites were equivalent and proficient strategies to predict machine settings for radiotherapy planning of oligometastatic cancer.
{"title":"A deep learning approach for predicting linear accelerator output settings in automated radiotherapy planning of oligometastatic cancer","authors":"Mathieu Gaudreault , Lachlan McIntosh , Katrina Woodford , Jason Li , Susan Harden , Sandro Porceddu , Nicholas Hardcastle , Vanessa Panettieri","doi":"10.1016/j.phro.2025.100890","DOIUrl":"10.1016/j.phro.2025.100890","url":null,"abstract":"<div><h3>Background and purpose</h3><div>The monitor units (MU) per control point (CP) control the necessary fine-tuned ablative dose for hypofractionated radiotherapy of oligometastatic cancer. We aimed to introduce strategies maximising the sample size to accurately predict the MU per CP with artificial intelligence (AI).</div></div><div><h3>Materials and methods</h3><div>The 40/68/88 treatment plans of consecutive patients treated between 01/2019 and 06/2024 at our institution for metastatic cancer to the liver/bone/lung were included. Two approaches were considered to maximise the sample size. In one approach, the samples of each anatomical site were extensively augmented to predict the MU per CP from the dose distribution per CP, providing the MU per beam and meterset weight per CP. In the other approach, all samples from all anatomical sites were combined for training. The number of achieved clinical goals based on dose-volume calculation metrics in AI radiotherapy plans (AI-RTPlan) was compared with the number of achieved clinical goals in the clinical plans.</div></div><div><h3>Results</h3><div>The mean absolute percentage error between predicted and clinical MU per beam/meterset weight per CP was less than 6.2%. All AI-RTPlans were generated in less than 5 s. At least 90%/5% of patients had the same, or more, achieved clinical goals with AI-RTPlans. Target coverage and dose to organs at risk metrics were within ± 2% and ± 2.3 Gy of the clinical value in all patients, respectively.</div></div><div><h3>Conclusions</h3><div>Augmenting data extensively and combining anatomical sites were equivalent and proficient strategies to predict machine settings for radiotherapy planning of oligometastatic cancer.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"37 ","pages":"Article 100890"},"PeriodicalIF":3.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145760658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2026-01-12DOI: 10.1016/j.phro.2026.100903
Yifei Pi , Haiyang Wang , Yawei Zhang , Zhao Peng , Xianhu Zeng , Yuexin Guo , Chunbo Liu
Background and purpose
Accurate commissioning of proton beam models remained a major challenge in pencil beam scanning (PBS) proton therapy. This study presented an automated Monte Carlo (MC) modeling framework that was designed to automate and standardize beam model commissioning.
Materials and methods
This framework supported commissioning workflows by optimizing beam parameters based on user-supplied data including integrated depth dose curves, lateral profiles, measured absolute dose per energy, etc. It incorporated optimization algorithms including particle swarm optimization and Nelder-Mead, and followed a modular pipeline including data preparation, phase space parameter fitting, energy spectrum tuning, and dose calibration. Validation was performed using 20 clinical cases and over 100 measurement 2D planes in water-based patient-specific quality assurance (QA) plans. The framework was commissioned with TOol for PArticle Simulation (TOPAS) and Monte Carlo square (MCsquare).
Results
After tuning, both MC engines reproduced maximum range errors of 0.3 % (TOPAS) and 0.6 % (MCsquare) at depths corresponding to 80 % and 20 % of the maximum dose, and similarly small deviations in the full width at half maximum and peak dose. For QA plans, the median gamma pass rate was 100.0 % for TOPAS under the 3 %/3 mm criterion (range: 95.3 %–100.0 %, mean: 99.9 %), with MCsquare achieved comparable results with minimum pass rates above 94.3 %.
Conclusions
This open-source, Python-based framework provided a robust and extensible solution for automated multi-engine MC beam commissioning in proton therapy. It enhanced reproducibility and efficiency, facilitating both clinical and research applications in medical physics.
{"title":"A novel automated framework for multi-engine Monte Carlo model commissioning in proton therapy","authors":"Yifei Pi , Haiyang Wang , Yawei Zhang , Zhao Peng , Xianhu Zeng , Yuexin Guo , Chunbo Liu","doi":"10.1016/j.phro.2026.100903","DOIUrl":"10.1016/j.phro.2026.100903","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Accurate commissioning of proton beam models remained a major challenge in pencil beam scanning (PBS) proton therapy. This study presented an automated Monte Carlo (MC) modeling framework that was designed to automate and standardize beam model commissioning.</div></div><div><h3>Materials and methods</h3><div>This framework supported commissioning workflows by optimizing beam parameters based on user-supplied data including integrated depth dose curves, lateral profiles, measured absolute dose per energy, etc. It incorporated optimization algorithms including particle swarm optimization and Nelder-Mead, and followed a modular pipeline including data preparation, phase space parameter fitting, energy spectrum tuning, and dose calibration. Validation was performed using 20 clinical cases and over 100 measurement 2D planes in water-based patient-specific quality assurance (QA) plans. The framework was commissioned with TOol for PArticle Simulation (TOPAS) and Monte Carlo square (MCsquare).</div></div><div><h3>Results</h3><div>After tuning, both MC engines reproduced maximum range errors of 0.3 % (TOPAS) and 0.6 % (MCsquare) at depths corresponding to 80 % and 20 % of the maximum dose, and similarly small deviations in the full width at half maximum and peak dose. For QA plans, the median gamma pass rate was 100.0 % for TOPAS under the 3 %/3<!--> <!-->mm criterion (range: 95.3 %–100.0 %, mean: 99.9 %), with MCsquare achieved comparable results with minimum pass rates above 94.3 %.</div></div><div><h3>Conclusions</h3><div>This open-source, Python-based framework provided a robust and extensible solution for automated multi-engine MC beam commissioning in proton therapy. It enhanced reproducibility and efficiency, facilitating both clinical and research applications in medical physics.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"37 ","pages":"Article 100903"},"PeriodicalIF":3.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2026-02-14DOI: 10.1016/j.phro.2026.100925
Febrio Lunardo , Alex Tan , Laura Baker , John Baines , Timothy Squire , Jason A. Dowling , Mostafa Rahimi Azghadi , Ashley G. Gillman
Introduction
Treatment uncertainties influenced by organ intrafraction motion complicate the widespread adoption of hypofractionated radiotherapy. This study aims to identify imaging features on pre-treatment magnetic resonance imaging (MRI) scans that describe prostate and seminal vesicle (SV) intrafraction motion, with the goal of informing and improving treatment planning.
Materials/methods
Thirty prostate cancer participants treated on an Elekta Unity 1.5T MR-Linac were recruited, with a series of volumetric MR images acquired pre-, during and post- treatment over multiple fractions. nnU-Net was used to automatically contour the prostate, rectum, SV and bladder. These contours quantified prostate and SV intrafraction motion and enabled extraction of imaging features. A linear regression model assessed relationships between the organs intrafraction motion, treatment margins, and the extracted features.
Results
Bladder filling during treatment influenced both SV and prostate intrafraction motion, especially, when baseline bladder volume was <190 mL for both prostate (R2 = 0.142) and SV (R2 = 0.258). Rectum volume showed no strong correlation with motion. Baseline bladder volume below 332 mL increased the required SV treatment margins to 5.8 mm, compared to 3.5 mm for larger volumes.
Conclusion
This study demonstrated that the baseline bladder volume at start of a treatment fraction predicts for both SV and prostate intrafraction motion, by mediating the effect of bladder filling, and that SV treatment margins could be reduced for a favourably sized bladder. These findings may support refining treatment protocols such as aiming for an initial bladder volume of at least 190 mL.
{"title":"The impact of bladder and rectal dynamics on prostate and seminal vesicles intrafraction motion and deformation in radiotherapy","authors":"Febrio Lunardo , Alex Tan , Laura Baker , John Baines , Timothy Squire , Jason A. Dowling , Mostafa Rahimi Azghadi , Ashley G. Gillman","doi":"10.1016/j.phro.2026.100925","DOIUrl":"10.1016/j.phro.2026.100925","url":null,"abstract":"<div><h3>Introduction</h3><div>Treatment uncertainties influenced by organ intrafraction motion complicate the widespread adoption of hypofractionated radiotherapy. This study aims to identify imaging features on pre-treatment magnetic resonance imaging (MRI) scans that describe prostate and seminal vesicle (SV) intrafraction motion, with the goal of informing and improving treatment planning.</div></div><div><h3>Materials/methods</h3><div>Thirty prostate cancer participants treated on an Elekta Unity 1.5T MR-Linac were recruited, with a series of volumetric MR images acquired pre-, during and post- treatment over multiple fractions. nnU-Net was used to automatically contour the prostate, rectum, SV and bladder. These contours quantified prostate and SV intrafraction motion and enabled extraction of imaging features. A linear regression model assessed relationships between the organs intrafraction motion, treatment margins, and the extracted features.</div></div><div><h3>Results</h3><div>Bladder filling during treatment influenced both SV and prostate intrafraction motion, especially, when baseline bladder volume was <190 mL for both prostate (R<sup>2</sup> = 0.142) and SV (R<sup>2</sup> = 0.258). Rectum volume showed no strong correlation with motion. Baseline bladder volume below 332 mL increased the required SV treatment margins to 5.8 mm, compared to 3.5 mm for larger volumes.</div></div><div><h3>Conclusion</h3><div>This study demonstrated that the baseline bladder volume at start of a treatment fraction predicts for both SV and prostate intrafraction motion, by mediating the effect of bladder filling, and that SV treatment margins could be reduced for a favourably sized bladder. These findings may support refining treatment protocols such as aiming for an initial bladder volume of at least 190 mL.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"37 ","pages":"Article 100925"},"PeriodicalIF":3.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147327492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2025-12-20DOI: 10.1016/j.phro.2025.100898
Xingyue Ruan , Xia Li , Muheng Li , Barbara Bachtiary , Antony Lomax , Zhiling Chen , Ye Zhang
Background and purpose
The registration of individual dose distributions to a reference anatomy represents a key step in voxel-based analysis (VBA), a tool for spatially informed dose–response assessment. Accurate deformable image registration (DIR) is essential for addressing anatomical variability across patients. To improve both global and region-specific alignment, we enhanced our in-house DIR algorithm (CPT-DIR) by incorporating contour-informed regularization.
Materials and methods
We evaluated contour-informed CPT-DIR using CT images from 37 Head-and-Neck patients, with seven cases providing ground-truth dose distribution for dose warping validation. Organs at risk (OARs) were delineated manually, with bone contours auto-generated using TotalSegmentator. Contour-informed constraints (Dice Similarity) were integrated to enhance registration in clinically relevant regions. The global registration results were evaluated using MAE, SSIM and PSNR. Geometric accuracy and warped dose accuracy were assessed using Dice Similarity Coefficient (DSC) and Dose-Organ Overlap (DOO). The performance of CPT-DIR, with and without constraints, was benchmarked against conventional B-spline.
Results
CPT-DIR achieved superior accuracy with a MAE of 98.9 ± 6.3 HU, lower than 179.1 ± 17.8 HU for B-spline. Incorporating brainstem contours as regularization improved the DSC from 0.604 ± 0.116 to 0.878 ± 0.017 and DOO from 0.430 ± 0.117 to 0.753 ± 0.043 for brainstem. For the remaining OARs, the enhanced CPT-DIR consistently achieved higher DSC and DOO metrics.
Conclusions
The integration of contour-informed regularization in CPT-DIR improved DIR accuracy, particularly in anatomically and dosimetrically relevant regions. This enhanced spatial alignment demonstrated strong potential for advancing reliable inter-patient dosimetric studies in HN radiotherapy.
{"title":"Contour-informed inter-patient deformable registration for more reliable voxel-based analysis of Head-and-Neck cancer patients","authors":"Xingyue Ruan , Xia Li , Muheng Li , Barbara Bachtiary , Antony Lomax , Zhiling Chen , Ye Zhang","doi":"10.1016/j.phro.2025.100898","DOIUrl":"10.1016/j.phro.2025.100898","url":null,"abstract":"<div><h3>Background and purpose</h3><div>The registration of individual dose distributions to a reference anatomy represents a key step in voxel-based analysis (VBA), a tool for spatially informed dose–response assessment. Accurate deformable image registration (DIR) is essential for addressing anatomical variability across patients. To improve both global and region-specific alignment, we enhanced our in-house DIR algorithm (CPT-DIR) by incorporating contour-informed regularization.</div></div><div><h3>Materials and methods</h3><div>We evaluated contour-informed CPT-DIR using CT images from 37 Head-and-Neck patients, with seven cases providing ground-truth dose distribution for dose warping validation. Organs at risk (OARs) were delineated manually, with bone contours auto-generated using TotalSegmentator. Contour-informed constraints (Dice Similarity) were integrated to enhance registration in clinically relevant regions. The global registration results were evaluated using MAE, SSIM and PSNR. Geometric accuracy and warped dose accuracy were assessed using Dice Similarity Coefficient (DSC) and Dose-Organ Overlap (DOO). The performance of CPT-DIR, with and without constraints, was benchmarked against conventional B-spline.</div></div><div><h3>Results</h3><div>CPT-DIR achieved superior accuracy with a MAE of 98.9 ± 6.3 HU, lower than 179.1 ± 17.8 HU for B-spline. Incorporating brainstem contours as regularization improved the DSC from 0.604 ± 0.116 to 0.878 ± 0.017 and DOO from 0.430 ± 0.117 to 0.753 ± 0.043 for brainstem. For the remaining OARs, the enhanced CPT-DIR consistently achieved higher DSC and DOO metrics.</div></div><div><h3>Conclusions</h3><div>The integration of contour-informed regularization in CPT-DIR improved DIR accuracy, particularly in anatomically and dosimetrically relevant regions. This enhanced spatial alignment demonstrated strong potential for advancing reliable inter-patient dosimetric studies in HN radiotherapy.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"37 ","pages":"Article 100898"},"PeriodicalIF":3.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145842008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2026-01-18DOI: 10.1016/j.phro.2026.100906
Zeyu Zhang , Dongyang Guo , Ke Lu , Zhuoran Jiang , Hualiang Zhong , Fang-Fang Yin , Lei Ren , Zhenyu Yang
Background and purpose
Accurate registration of pretreatment Magnetic Resonance Imaging (MRI) to onboard Cone Beam Computed Tomography (CBCT) is critical for liver Stereotactic Body Radiation Therapy (SBRT) but is challenged by poor CBCT soft-tissue contrast and respiratory motion. We developed and validated PhysMorph, a physics-informed deep learning framework designed to provide rapid, anatomically plausible MR-CBCT image registration of the liver.
Materials and methods
We developed PhysMorph, a registration framework that incorporated finite element method (FEM) simulations as biomechanical regularization alongside image similarity metrics. The framework was validated on two datasets: (1) simulated data with a known ground-truth deformation derived from longitudinal MR-Linac scans, and (2) clinical MR-CBCT pairs from liver SBRT patients. Performance was assessed using target registration error (TRE), mean surface distance (MSD), and metrics of biomechanical fidelity.
Results
On clinical data, PhysMorph achieved a mean TRE of 2.2 ± 1.4 mm and a MSD of 1.60 ± 0.05 mm, significantly outperforming VoxelMorph (4.11 ± 1.53 mm) and SynthMorph (4.41 ± 1.67 mm) while maintaining high biomechanical fidelity. The framework reduced registration time from over 10 min for conventional finite element methods to 103.4 ms, enabling practical real-time application.
Conclusions
PhysMorph enables fast, accurate, and physically realistic registration of pretreatment MRI to on-board CBCT for liver SBRT. By integrating MRI’s superior soft-tissue visualization while ensuring anatomical plausibility, our approach facilitates precise tumor localization that could enable smaller planning target volumes and more conformal dose distributions, potentially enhancing tumor control while reducing radiation exposure to healthy tissues.
{"title":"PhysMorph: A biomechanical and image-guided deep learning framework for real-time multi-modal liver image registration","authors":"Zeyu Zhang , Dongyang Guo , Ke Lu , Zhuoran Jiang , Hualiang Zhong , Fang-Fang Yin , Lei Ren , Zhenyu Yang","doi":"10.1016/j.phro.2026.100906","DOIUrl":"10.1016/j.phro.2026.100906","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Accurate registration of pretreatment Magnetic Resonance Imaging (MRI) to onboard Cone Beam Computed Tomography (CBCT) is critical for liver Stereotactic Body Radiation Therapy (SBRT) but is challenged by poor CBCT soft-tissue contrast and respiratory motion. We developed and validated PhysMorph, a physics-informed deep learning framework designed to provide rapid, anatomically plausible MR-CBCT image registration of the liver.</div></div><div><h3>Materials and methods</h3><div>We developed PhysMorph, a registration framework that incorporated finite element method (FEM) simulations as biomechanical regularization alongside image similarity metrics. The framework was validated on two datasets: (1) simulated data with a known ground-truth deformation derived from longitudinal MR-Linac scans, and (2) clinical MR-CBCT pairs from liver SBRT patients. Performance was assessed using target registration error (TRE), mean surface distance (MSD), and metrics of biomechanical fidelity.</div></div><div><h3>Results</h3><div>On clinical data, PhysMorph achieved a mean TRE of 2.2 ± 1.4 mm and a MSD of 1.60 ± 0.05 mm, significantly outperforming VoxelMorph (4.11 ± 1.53 mm) and SynthMorph (4.41 ± 1.67 mm) while maintaining high biomechanical fidelity. The framework reduced registration time from over 10 min for conventional finite element methods to 103.4 ms, enabling practical real-time application.</div></div><div><h3>Conclusions</h3><div>PhysMorph enables fast, accurate, and physically realistic registration of pretreatment MRI to on-board CBCT for liver SBRT. By integrating MRI’s superior soft-tissue visualization while ensuring anatomical plausibility, our approach facilitates precise tumor localization that could enable smaller planning target volumes and more conformal dose distributions, potentially enhancing tumor control while reducing radiation exposure to healthy tissues.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"37 ","pages":"Article 100906"},"PeriodicalIF":3.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2026-01-14DOI: 10.1016/j.phro.2026.100905
Alex T. Price , Kenneth W. Gregg , Theodore H. Arsenault , Yilun Sun , Rojano Kashani , Lauren E. Henke
The Varian TrueBeam v4.1 enables a faster gantry rotation speed (9 deg/s) than previous versions (6 deg/s). In this longitudinal study, we assessed the impact of gantry rotation speed, fan geometry, and gating on imaging isocenter stability over 1 year. A ball bearing (BB) was CBCT imaged and raw projections were analyzed to extract BB displacement at each projection angle. The average BB displacement was 0.04 ± 0.03 mm. The maximum displacement of the BB was 0.15 mm, which is within 1.00 mm isocenter tolerances. Increased gantry rotation speed does not have a clinically meaningful impact on imaging isocenter stability.
{"title":"Quantifying gantry rotation speed and gating effects on CBCT isocenter accuracy for radiotherapy image acquisition","authors":"Alex T. Price , Kenneth W. Gregg , Theodore H. Arsenault , Yilun Sun , Rojano Kashani , Lauren E. Henke","doi":"10.1016/j.phro.2026.100905","DOIUrl":"10.1016/j.phro.2026.100905","url":null,"abstract":"<div><div>The Varian TrueBeam v4.1 enables a faster gantry rotation speed (9 deg/s) than previous versions (6 deg/s). In this longitudinal study, we assessed the impact of gantry rotation speed, fan geometry, and gating on imaging isocenter stability over 1 year. A ball bearing (BB) was CBCT imaged and raw projections were analyzed to extract BB displacement at each projection angle. The average BB displacement was 0.04 ± 0.03 mm. The maximum displacement of the BB was 0.15 mm, which is within 1.00 mm isocenter tolerances. Increased gantry rotation speed does not have a clinically meaningful impact on imaging isocenter stability.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"37 ","pages":"Article 100905"},"PeriodicalIF":3.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146182788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01Epub Date: 2026-02-13DOI: 10.1016/j.phro.2026.100924
Lisa Milan , Francesco Pupillo , Margherita Corsi , Matteo Coppotelli , Alessio Minoggio , Paula Sargenti , Stefano Moretto , Margherita Casiraghi , Maria Antonietta Piliero , Klaudia Krzekotowska , Davide Giovanni Bosetti , Gianfranco Pesce , Francesco Mosè Castronovo , Stefano Presilla , Thomas Zilli
Background and purpose
Magnetic resonance (MR)-based synthetic computed tomography (sCT) can improve target and organs-of-interest delineation in brain radiotherapy. However, metallic implants may cause anatomical inconsistencies, dose inaccuracies, and distortions. This study evaluates the impact of dental metal artifacts on MR-only workflow.
Materials and methods
Ninety-six patients underwent MR and CT for radiotherapy planning; fifty-two with dental implants undergoing standard or stereotactic treatments were eligible. Dose-volume metrics (Dmean, D2%, and D98%) and γ-index analyses (1%/1 mm and 3%/3 mm) were used to compare dose for targets and organs. For a subgroup of 15 patients, further analyses were performed. Image quality of sCT was evaluated against planning CT using Mean Absolute Error (MAE) of Hounsfield units (HU) within targets and organs. Offline sCT-cone beam CT (CBCT) registrations were compared with online CT-CBCT matches. Sequences for B0-map, the map of main static magnetic field, quantified geometric distortion.
Results
Median target dose differences remained within ±0.3%, with maximum of 3.7% for D98% due to artifact-induced body contour changes. Median organ dose deviations were ±0.3%. Passing rates for γ-index were 97.8% and 100% for 1%/1 mm and 3%/3 mm, respectively. Average MAE was below 10 HU for brain, reaching 40 HU in bone-target regions. No differences were observed between CT-CBCT and sCT-CBCT registration. Geometric distortions remained within 1 mm, satisfying radiotherapy requirements.
Conclusions
Despite dental metal artifacts, sCT demonstrate dose accuracy comparable to standard CT. The results support clinical use of sCT in brain radiotherapy, with caution when artifacts alter the body near targets.
{"title":"Dental metal artifacts in magnetic resonance-based synthetic computed tomography for brain radiotherapy: Impact on dose, patient setup, and geometric distortion","authors":"Lisa Milan , Francesco Pupillo , Margherita Corsi , Matteo Coppotelli , Alessio Minoggio , Paula Sargenti , Stefano Moretto , Margherita Casiraghi , Maria Antonietta Piliero , Klaudia Krzekotowska , Davide Giovanni Bosetti , Gianfranco Pesce , Francesco Mosè Castronovo , Stefano Presilla , Thomas Zilli","doi":"10.1016/j.phro.2026.100924","DOIUrl":"10.1016/j.phro.2026.100924","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Magnetic resonance (MR)-based synthetic computed tomography (sCT) can improve target and organs-of-interest delineation in brain radiotherapy. However, metallic implants may cause anatomical inconsistencies, dose inaccuracies, and distortions. This study evaluates the impact of dental metal artifacts on MR-only workflow.</div></div><div><h3>Materials and methods</h3><div>Ninety-six patients underwent MR and CT for radiotherapy planning; fifty-two with dental implants undergoing standard or stereotactic treatments were eligible. Dose-volume metrics (D<sub>mean</sub>, D<sub>2%</sub>, and D<sub>98%</sub>) and γ-index analyses (1%/1 mm and 3%/3 mm) were used to compare dose for targets and organs. For a subgroup of 15 patients, further analyses were performed. Image quality of sCT was evaluated against planning CT using Mean Absolute Error (MAE) of Hounsfield units (HU) within targets and organs. Offline sCT-cone beam CT (CBCT) registrations were compared with online CT-CBCT matches. Sequences for B0-map, the map of main static magnetic field, quantified geometric distortion.</div></div><div><h3>Results</h3><div>Median target dose differences remained within ±0.3%, with maximum of 3.7% for D<sub>98%</sub> due to artifact-induced body contour changes. Median organ dose deviations were ±0.3%. Passing rates for γ-index were 97.8% and 100% for 1%/1 mm and 3%/3 mm, respectively. Average MAE was below 10 HU for brain, reaching 40 HU in bone-target regions. No differences were observed between CT-CBCT and sCT-CBCT registration. Geometric distortions remained within 1 mm, satisfying radiotherapy requirements.</div></div><div><h3>Conclusions</h3><div>Despite dental metal artifacts, sCT demonstrate dose accuracy comparable to standard CT. The results support clinical use of sCT in brain radiotherapy, with caution when artifacts alter the body near targets.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"37 ","pages":"Article 100924"},"PeriodicalIF":3.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147285533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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