Med Phys [JOURNAL]
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200 papers
An extended photon isoeffective dose model accounting for the energy-dependent effectiveness of secondary charged particles in BNCT.
Dattoli Viegas AM, Gossio S, Riback J … +11 more , Valeriano LM, Cruz GS, Casali A, Carante MP, Ballarini F, Fatemi S, Postuma I, Vercesi V, Bortolussi S, Ramos RL, González SJ
Med Phys · 2026 May · PMID 42108223 · Publisher ↗
BACKGROUND: The absorbed dose in Boron Neutron Capture Therapy (BNCT) arises from various radiation components, each contributing differently to the overall biological effect. These effects depend not only on the absorbe...
BACKGROUND: The absorbed dose in Boron Neutron Capture Therapy (BNCT) arises from various radiation components, each contributing differently to the overall biological effect. These effects depend not only on the absorbed dose but also on the type and energy of the involved secondary charged particles. Current dosimetric models convert absorbed dose into an equivalent photon dose using radiation-specific weighting factors that account for some differences in radiation type. However, these models generally neglect the energy dependence of biological effectiveness. PURPOSE: To evaluate the relevance of incorporating the energy dependence of secondary charged particles into BNCT dosimetry, and to assess its impact on dose calculations and clinical outcome estimations. METHODS: The photon isoeffective dose formalism was extended by reformulating the mathematical model for in terms of the secondary particle fields rather than dose components in BNCT. Tissue-specific radiobiological (RB) parameters and were introduced as functions of Linear Energy Transfer (LET), as predicted by the BIANCA biophysical model for normal skin and head and neck tumor tissues. Recoil proton spectra were analyzed at superficial and deep locations in tissues to evaluate their effectiveness relative to 583 keV protons from the (n,p) reaction. Four approaches to , with varying levels of detail regarding energy and radiation fields representation, were evaluated across three scenarios. The analysis moved from a simplified geometry using a cylindrical phantom irradiated with epithermal neutrons, to progressively more realistic clinical scenarios, including a head and neck cancer treatment planning case and a retrospective study of a cutaneous melanoma case treated with BNCT at the RA-6 reactor in Argentina. RESULTS: Recoil protons were found to have lower than 583 keV protons from (n,p) reactions, indicating that assuming equal effectiveness leads to overestimated doses in photon-equivalent units. In the phantom, detailed LET-based modeling proved essential in low-to-moderate boron concentration or superficial tissue scenarios, where simplified models showed deviations up to 30%. In contrast, boron-rich or deep tissue conditions tolerated simplifications with minimal loss of accuracy. In the head and neck case, simplified models led to skin overdoses up to 13%, increasing NTCP from negligible ( ) to high values ( ), thus raising the potential radiotoxicity risk. An apparent gain in TCP resulted from overestimating the required treatment time due to oversimplified modeling. In the retrospective melanoma case irradiated with the RA-6 mixed thermal-epithermal beam, simplified models underestimated the skin dose by 8% to 12%, potentially compromising dose-response interpretations. CONCLUSIONS: Beyond treatment planning, accurate dose modeling is also key for outcome assessment and meaningful comparisons with photon radiotherapy. Incorporating detailed LET-dependent RB modeling is especially important in scenarios involving low-to-moderate boron concentration levels or superficial tissues, where recoil protons dominate the dose composition. In contrast, simplified models may be acceptable in boron-rich, high-LET contexts, particularly when constrained by limited radiobiological data or computational resources. These findings support the development of a flexible photon isoeffective dose formalism that can evolve alongside advances in BNCT technologies and RB data.
A modified lethal-potentially lethal model for oxygen-mediated FLASH sparing in stem cell niches.
Zhou S
Med Phys · 2026 May · PMID 42108222 · Full text
BACKGROUND: Ultra-high dose rate (FLASH) irradiation can reduce normal-tissue toxicity while preserving tumor control, but a mechanistic explanation consistent with classical radiobiology remains incomplete. In particula...
BACKGROUND: Ultra-high dose rate (FLASH) irradiation can reduce normal-tissue toxicity while preserving tumor control, but a mechanistic explanation consistent with classical radiobiology remains incomplete. In particular, oxygen-depletion arguments based solely on bulk tissue oxygenation can appear inconsistent with clinically relevant fraction sizes, motivating a DNA-target-level oxygen formulation. PURPOSE: To develop a theory-based mechanistic extension of the Lethal and Potentially Lethal (LPL) model that explains oxygen-mediated FLASH trends without prescribing dose rate-dependent radiosensitivity, and to identify the baseline nuclear oxygen window in which sparing is expected to be largest. METHODS: We introduce an explicit Precursor Lesion population whose fate is governed by competing chemical restitution/repair versus oxygen-dependent fixation into potentially lethal and lethal lesion channels. Fixation kinetics are coupled to a time-varying nuclear oxygen tension, , which decreases via radiolytic depletion during irradiation and recovers toward a baseline via reduced-order reoxygenation kinetics. To address the oxygen paradox, we distinguish bulk vascular oxygenation from a lower effective DNA target-level oxygenation that may arise in regulated stem-cell niches because of niche hypoxia and intracellular oxygen consumption. Oxygen modulation is implemented through a mechanistic exponential OER formulation parameterized by an oxygen-fixation rate constant, while retaining classical LPL behavior in the conventional low-dose-rate limit. RESULTS: The model predicts that oxygen-mediated FLASH sparing is largest when baseline nuclear oxygenation lies in an intermediate physiologic-hypoxia regime, corresponding to the steep oxygen-responsive portion of the OER curve. In the reference parameter set, a quiescent normal-tissue niche with baseline = 3 mmHg shows appreciable sparing under FLASH delivery, whereas sparing is minimal when the baseline lies near either the OER floor ( = 0.2 mmHg) or the OER saturation plateau ( = 30 mmHg). Sensitivity analyses preserve this intermediate oxygen window while shifting the magnitude and threshold of the effect. CONCLUSIONS: By explicitly resolving Precursor Lesion fixation kinetics and by treating niche-to-nucleus oxygenation as an effective target-level variable, this mechanistic LPL framework predicts that oxygen-mediated FLASH sparing is most likely when baseline oxygenation lies within an intermediate physiologic-hypoxia window. The model should therefore be viewed as a mechanistic, testable framework rather than as a universal explanation of all FLASH responses.
Real-time, high-precision detection of aortic dissection key components for emergency triage using an enhanced deep learning model.
Wu H, Chen F, Xia R … +5 more , Wang L, Zhang Z, Zhang J, Cheng Q, Guo L
Med Phys · 2026 May · PMID 42108221 · Publisher ↗
BACKGROUND: Aortic dissection (AD) is a life-threatening cardiovascular emergency. For Type B AD (TBAD), rapid and accurate identification of the true lumen (TL), false lumen (FL), and false lumen thrombus (FLT) from CTA...
BACKGROUND: Aortic dissection (AD) is a life-threatening cardiovascular emergency. For Type B AD (TBAD), rapid and accurate identification of the true lumen (TL), false lumen (FL), and false lumen thrombus (FLT) from CTA is critical for risk stratification and treatment planning. However, existing deep learning solutions often lack real-time capability and fail to address the detection of FLT. PURPOSE: To develop a real-time, high-precision deep learning framework for the simultaneous detection of all three key AD components to support emergency triage. METHODS: We propose AD-YOLO11, an enhanced YOLOv11 model integrating three key innovations: (1) a Recursive Information Distillation Network (RIDNet) for CTA noise suppression, (2) a Triplet Attention Mechanism for spatial and channel feature enhancement, and (3) the MPDIoU loss function for optimized bounding box regression. The model was trained and internally validated on a dataset of 25 176 slices from 106 TBAD patients and externally validated on 18 238 slices from 71 independent patients. RESULTS: On internal validation, AD-YOLO11 achieved a precision of 0.991 ± 0.004, recall of 0.936 ± 0.006, mAP@0.5 of 0.951 ± 0.007, and mAP@0.5:0.95 of 0.883 ± 0.008. It maintained high performance on the external test set, demonstrating strong generalizability. The inference speed was 3.18 ± 0.23 ms per slice on GPU, and it remained clinically feasible on CPU (53.15 ± 2.76 ms per slice). CONCLUSIONS: AD-YOLO11 achieves millisecond-level, high-accuracy detection of all three critical Type B aortic dissection components from CTA images. Its efficient inference on both GPU and CPU makes it a promising frontline tool for rapid triage in emergency and resource-limited settings, effectively complementing time-consuming 3D segmentation for aortic dissection assessment.
Correlation of ultrasound attenuation with proton density fat fraction across multiple organs in healthy volunteers.
Rohfritsch A, Courgenay J, Biénassis A … +5 more , Olhagaray E, Basso M, Leporq B, Allignet B, Melodelima D
Med Phys · 2026 May · PMID 42108220 · Full text
BACKGROUND: Local determination of the attenuation in biological tissues has been the focus of extensive research over the past decades. Its characterization is of major interest for both noninvasive thermal therapies an...
BACKGROUND: Local determination of the attenuation in biological tissues has been the focus of extensive research over the past decades. Its characterization is of major interest for both noninvasive thermal therapies and diagnostic applications. Numerous ultrasound (US)-based methods have been investigated in recent years to assess attenuation in vivo. PURPOSE: In this article, we aim to map US attenuation in four organs of human volunteers: liver, pancreas, kidney, and breast. We propose to compare the mean attenuation values obtained in each tissue with the corresponding proton density fat fraction (PDFF) derived from quantitative magnetic resonance imaging (qMRI). This comparison allows us to (i) present a direct calculation method of the local US attenuation and (ii) investigate the relationship between the average fat content of each organ and its global US attenuation. METHODS: The ultrasonic measurement method is in line with techniques originating from the spectral difference method. Here, the attenuation coefficient (AC) is estimated by insonifying tissues with a single plane wave and acquiring the backscattered echoes. This approach avoids the need for a reference medium to compensate for diffraction and focusing effects. The method is characterized and validated on a calibrated phantom and compared with two commonly used techniques on ex vivo liver tissues. Subsequently, attenuation maps and average values obtained from US imaging in healthy volunteers are compared with PDFF values measured by qMRI. Hepatic ( ) renal ( ), pancreatic ( ) and breast ( ) tissues were analyzed. Statistical significance was assessed using a paired t-test. To account for multiple comparisons, a Bonferroni correction was applied, resulting in an adjusted 5% significance threshold of . Effect sizes were also reported using Cohen's parameter. Effect sizes were considered large for . RESULTS: Measurements on the calibrated phantom showed relative errors between the measured mean values and the manufacturer values of 2% and 9%, respectively. Average AC of each organ was included in the confidence interval of the corresponding literature value. The Pearson correlation coefficient between (PDFF) and AC slope is ( ). When each organ was considered separately, no significant correlation was observed between PDFF average values and global US attenuation, as variations between volunteers were found of the same order of magnitude as the standard deviation around each average value. CONCLUSIONS: This work presents an alternative method for in vivo characterization of US attenuation based on the emission of a plane wave, and highlights the impact of fat density on inter-organ attenuation variations. Together, these results provide new insights into the relationship between tissue microstructure and US attenuation.
A parallel algorithm for generating Pareto-optimal radiosurgery treatment plans.
da Silva J, Hernández Escobar D, Kjellsson Lindblom T … +2 more , Nordström H, Sjölund J
Med Phys · 2026 May · PMID 42108219 · Full text
BACKGROUND: Using inverse planning tools to create radiosurgery treatment plans is an iterative process, where clinical trade-offs are explored by changing the relative importance given to different objectives and rerunn...
BACKGROUND: Using inverse planning tools to create radiosurgery treatment plans is an iterative process, where clinical trade-offs are explored by changing the relative importance given to different objectives and rerunning the optimizer until a desirable plan is found. Simultaneously generating many plans corresponding to different objective weights, while the patient is awaiting treatment, would allow the planner to navigate clinical trade-offs interactively, without optimizing a new plan between each update. PURPOSE: We seek to optimize hundreds of Gamma Knife radiosurgery treatment plans, corresponding to different weightings of objectives, fast enough to allow interactive Pareto navigation of clinical trade-offs to be incorporated into the clinical workflow. METHODS: We apply the alternating direction method of multipliers (ADMM) to the linear-program formulation of the optimization problem used in the clinical Lightning optimizer. We implement both a CPU and a GPU version of ADMM in Matlab and compare them to Matlab's built-in, single-threaded dual-simplex solver. The ADMM implementation is adapted to the optimization procedure used in the clinical software, with a bespoke algorithm for maximizing the overlap between low-dose points for different objective weights. The method is evaluated on a test dataset consisting of 20 cases from three different indications, with between one and nine targets and total target volumes ranging from 0.66 to 52 cm. RESULTS: The total optimization time to create 81 plans corresponding to different objective weightings varied from 63 to 520 s on CPU and from 1.8 to 40 s GPU, for the different test cases. As a reference, optimizing 81 plans using simplex took 100-51000 s, corresponding to ADMM speedups of 1.6-97 and 54-1500 times for the CPU and GPU, respectively. Increasing the number of plans to 441, corresponding to all combinations of slider values between 0.0 and 1.0 in steps of 0.05 in the clinical software, the total ADMM optimization time on GPU was between 3.0 and 110 s for the different test cases. Plan quality was evaluated by rerunning the ADMM optimization 20 times, each with a different random seed, for each test case and for nine objective weightings per case. The resulting relative differences in clinical metrics ( ) were 0.0 0.2%, 0.0 1.6%, 0.1 0.8%, and 0.1 3.0%, for coverage, selectivity, gradient index and beam-on time, respectively, compared to mean values for the corresponding reference simplex results. The standard deviations in these metrics closely mimicked those obtained when rerunning the simplex solver, verifying the validity of the method. CONCLUSIONS: We show how ADMM can be adapted for radiosurgery plan optimization, allowing hundreds of high-quality Gamma Knife treatment plans to be created in under two minutes on a single GPU, also for very large cases. The presented method would allow streamlined multicriteria optimization on the day of treatment, with interruption-free navigation of clinical trade-offs.
Integrating measurement and simulation for evaluation of patient-specific whole-body dosimetry in clinical BNCT.
Kakino R, Hu N, Sasaki A … +9 more , Nojiri M, Yoshino Y, Takeno S, Aihara T, Matsubayashi N, Takata T, Tanaka H, Nihei K, Ono K
Med Phys · 2026 May · PMID 42108218 · Full text
BACKGROUND: Boron neutron capture therapy (BNCT) is a treatment modality that utilises high intensity neutron beam in combination with a boron drug to target cancer cells at the cellular level. Despite decades of researc...
BACKGROUND: Boron neutron capture therapy (BNCT) is a treatment modality that utilises high intensity neutron beam in combination with a boron drug to target cancer cells at the cellular level. Despite decades of research in this field, there are very few reports on out-of-field patient dose of BNCT. PURPOSE: To quantify patient whole-body out-of-field dose during clinical BNCT for recurrent head-and-neck cancer using an integrated measurement-simulation workflow. METHODS: The data of 271 patients that received BNCT for recurrent head and neck were analyzed for this study.First, the neutron and gamma ray doses were measured using activation foils and thermoluminescent dosimeters, respectively. The detectors were placed onto the surface of the patient at various locations and the reaction rate of the metal foils were measured. Second, PHITS Monte Carlo simulation was performed to evaluate the neutron energy spectrum at each location and the corresponding neutron equivalent dose was determined. The total dose was calculated by summing the neutron equivalent dose and the gamma ray dose. The results were compared with other radiotherapy modalities. RESULTS: The mean ± standard deviation of equivalent dose at neck, chest, abdomen, waist, knee, and ankle were calculated to be 1.93 ± 1.12, 0.71 ± 0.42, 0.23 ± 0.12, 0.11 ± 0.06, 0.05 ± 0.03, 0.02 ± 0.01 Gy-eq, respectively. Excluding the neck region, the gamma ray dose was dominant at all measurement points. From the abdomen below, dose from the primary gamma ray were dominant, indicating a potential for reducing the dose to these regions by installing additional lead shielding in the treatment room walls. In comparison to other radiotherapy modalities, out-of-field dose of BNCT was found to be similar. CONCLUSION: The whole-body dose for patients that received BNCT for recurrent head and neck cancer were estimated by scaling PHITS derived neutron dose using multifoil reaction rates and adding measured gamma dose. The results showed the out-of-field dose was comparable to other radiotherapy modalities and further added confidence that BNCT is a safe treatment modality.
Integration of dual-energy CT characteristics and biomarkers: Noninvasive prediction of Ki-67 expression in pancreatic ductal adenocarcinoma.
Zhang W, Wang B, Shi D … +8 more , Ruan Y, Cheng P, Ming S, Li M, Cao W, Wang M, Li J, Wei W
Med Phys · 2026 May · PMID 42090202 · Publisher ↗
BACKGROUND: The Ki-67 proliferation index is a critical prognostic marker in pancreatic ductal adenocarcinoma (PDAC); however, its assessment relies on invasive tissue sampling. Ki-67 expression reflects active tumor cel...
BACKGROUND: The Ki-67 proliferation index is a critical prognostic marker in pancreatic ductal adenocarcinoma (PDAC); however, its assessment relies on invasive tissue sampling. Ki-67 expression reflects active tumor cell proliferation and is associated with aggressive tumor behavior. A preoperative, noninvasive method to predict Ki-67 status would therefore be valuable for clinical decision-making. Dual-energy CT (DECT) can provide quantitative parameters related to tumor vascularity and composition, potentially reflecting proliferative activity. Additionally, clinical biomarkers such as CA125 may offer complementary information regarding tumor biology. Therefore, the development of a reliable noninvasive approach to preoperatively determine Ki-67 status is of considerable clinical importance. PURPOSE: To develop and validate a noninvasive approach for predicting Ki-67 expression in pancreatic ductal adenocarcinoma by integrating quantitative dual-energy CT parameters and clinical biomarkers. METHODS: This retrospective study included 148 PDAC patients randomly divided into training (n = 89) and validation (n = 59) sets (6:4 ratio). All patients underwent preoperative DECT scans, and quantitative parameters including normalized iodine concentration (NIC), effective atomic number (Zeff), spectral attenuation slope (λ), etc. were obtained from three contrast phases. Serum tumor markers (CA19-9, CA125, CA50, CEA) and clinical features were analyzed. Multivariate logistic regression was used to identify predictors of Ki-67 expression. A nomogram and 3-D probability surface were developed to intuitively demonstrate the model's predictive structure and decision-making process. Model performance was validated using ROC analysis, calibration curves, and decision curve analysis. Innovatively, kernel-density ridgeline plots and prediction-error bar plots were employed to comprehensively evaluate risk distribution and prediction accuracy, demonstrating the model's stability. RESULTS: The joint model demonstrated excellent predictive performance, achieving AUCs of 0.803 in the training set and 0.810 in the validation set, outperforming both the clinical-only model (training AUC = 0.682, validation AUC = 0.751) and the DECT-only model (training AUC = 0.712, validation AUC = 0.702). Multivariate analysis identified arterial-phase normalized iodine concentration (A-NIC) (p = 0.046) and CA125 (p = 0.005) as independent predictors of Ki-67 expression. These two parameters formed the basis of the final predictive model, demonstrating consistent diagnostic value across both cohorts. CONCLUSION: Integration of DECT parameters and clinical biomarkers allows accurate noninvasive prediction of Ki-67 expression in PDAC, offering a potential tool for preoperative assessment of tumor proliferation.
Prediction of VMAT gamma passing rates using 3D CNNs based on leaf position analysis and gradient class activation mapping for plan complexity evaluation.
Berchtold J, Vockner S, Messner I … +4 more , Stana M, Röder F, Wolf F, Gaisberger C
Med Phys · 2026 May · PMID 42086465 · Full text
BACKGROUND: Volumetric Modulated Arc Therapy (VMAT) is a highly conformal radiotherapy technique that enables precise tumor irradiation while sparing surrounding healthy tissue. However, the high technical demands this t...
BACKGROUND: Volumetric Modulated Arc Therapy (VMAT) is a highly conformal radiotherapy technique that enables precise tumor irradiation while sparing surrounding healthy tissue. However, the high technical demands this technique places on Linear Accelerators (LinAc) necessitate reliable quality assurance (QA) tools. The Gamma Passing Rate (GPR), commonly used to compare planned and delivered dose distributions, requires extensive measurement resources. Many existing predictive metrics, such as the popular Modulation Complexity Score (MCS), are independent of the beam model, limiting their accuracy. Consequently, identifying appropriate metrics and their individual thresholds can be challenging. PURPOSE: This study aims to predict the GPR of VMAT arcs using a three-dimensional convolutional neural network (3D CNN). Gradient-weighted Class Activation Mapping (Grad-CAM) is applied to improve interpretability, identify weak segments, and potentially reveal beam model limitations. METHODS: A 3D CNN was trained on 140 6 MV VMAT arcs, with 30 arcs each used for validation and testing. All plans were delivered by an Elekta Harmony Pro LinAc with 4° control point (CP) spacing. Input data included discretized beam's eye view (BEV) representations and segment-specific monitor unit (MU) values. GPR evaluation was performed using a Delta4+ phantom with a 1%/ 2 mm criterion. Data augmentation enhanced training diversity. Grad-CAM was used to visualize influential plan regions. RESULTS: After 36 epochs, the model achieved a mean absolute error (MAE) of 2.0%(test set) and 1.3%(training set). With cropped input, the best MAEs were 2.1%(test) and 1.5%(training). Grad-CAM analysis indicated that dynamic delivery aspects had more influence on prediction accuracy than static features like field shape. CONCLUSIONS: This study highlights the potential of deep learning for automated GPR prediction, offering a more efficient QA workflow. Especially in time-critical settings like online adaptive radiotherapy, where traditional measurement-based QA is often impractical, this model provides a scalable solution to ensure treatment safety. The use of Grad-CAM enables insight into beam model and LinAc performance, allowing refinement of treatment planning and improved QA precision in clinical practice.
Clinical validation of a high-definition mid-position magnetic resonance imaging approach for lung radiotherapy planning.
Keijnemans K, van Lier ALHMW, Borman PTS … +5 more , Tekatli H, Pomp J, van Weelderen RE, Raaymakers BW, Fast MF
Med Phys · 2026 May · PMID 42086464 · Full text
BACKGROUND: Respiratory-correlated four-dimensional (4D) magnetic resonance imaging (4D-MRI) is useful to estimate breathing induced motion for MRI-guided radiotherapy. Based on 4D-MR image sets, a three-dimensional mid-...
BACKGROUND: Respiratory-correlated four-dimensional (4D) magnetic resonance imaging (4D-MRI) is useful to estimate breathing induced motion for MRI-guided radiotherapy. Based on 4D-MR image sets, a three-dimensional mid-position (MidP) MRI can be generated using deformable image registration (DIR) for radiotherapy planning. However, the desired spatial resolution and image contrast of the MidP MRI may differ from the original 4D-MRI. PURPOSE: This retrospective study validates a high-definition (HD)-MidP MRI approach that combines 4D-MRI motion information with a high-resolution MRI to enhance the spatial resolution of the MidP image. METHODS: Computed tomography (CT) and MR image sets of 25 lung cancer patients were eligible, of whom 17 were complete and suitable for analysis. Standard-definition (SD)-MidP images were derived by applying DIR to warp the ten respiratory phases of a 4D-CT or 4D-MRI, whereas the HD-MidP MRI was derived by warping a high-resolution respiratory-triggered MRI to the MidP. The MidP image quality was assessed with a 4-point Likert scale on tumor and organ at risk (OAR) distinctiveness by three readers. Additionally, the gross tumor volume (GTV) was delineated by the readers, from which a consensus contour was derived for each MidP image. Reader contours were evaluated using the Dice similarity coefficient (DSC) and mean distance to agreement (DTA). Anatomical accuracy was evaluated by comparing MidP tumor locations to manually determined tumor displacements, while DIR precision was analyzed using the distance to discordance metric (DDM). Moreover, deformation vector fields (DVFs) from the DIR were used to automatically calculate MidP-based treatment margins. RESULTS: Eighteen targets were identified in seventeen patients. All HD-MidP MR image sets were delineated, while 98% (53/54) of the SD-MidP CT and 87% (47/54) of the SD-MidP MR image sets were of adequate quality for delineation. The SD-MidP MRI was positively scored in 13 out of 47 assessments for tumor distinctiveness and in 6 out of 47 assessments for OAR distinctiveness. In contrast, the HD-MidP MRI showed a substantial improvement, with positive scores in 45 out of 54 assessments for tumor distinctiveness and 51 out of 54 assessments for OAR distinctiveness. Contour analyses revealed that the HD-MidP MRI achieved the highest average DSC value (0.83) and, simultaneously, the lowest mean DTA value (0.96 mm). Compared to the manually determined tumor displacements, subvoxel differences in MidP tumor location were observed in 96% (52/54) of the registrations. The distribution of DDM values (median: 1.1 mm) for the HD-MidP MRI was found to be significantly higher than the distributions for the SD-MidP CT (median: 0.2 mm) and SD-MidP MRI (median: 0.7 mm), indicating a lower, but still subvoxel, precision for the HD-MidP MRI approach. The DVF variability was higher for the HD-MidP MRI (median: 2.7 mm) than for the SD-MidP MRI (median: 2.3 mm). However, when used to derive treatment margins, these margins were identical. CONCLUSIONS: The presented HD-MidP MRI methodology scored highest on both tumor and OAR distinctiveness, with GTV contours demonstrating the best alignment. Combined with its high anatomical accuracy, these findings support its potential for lung radiotherapy planning.
AAPM Spring Clinical Meeting - Program.
Med Phys · 2026 May · PMID 42065677 · Publisher ↗
Abstract loading — click title to view on PubMed.
AAPM Spring Clinical Meeting - Abstract.
Med Phys · 2026 May · PMID 42065669 · Publisher ↗
Abstract loading — click title to view on PubMed.
A GPU-accelerated radiation dose engine for small animal proton radiotherapy.
Gong H, Wang Y, Liu Z … +5 more , Liu M, Gu Y, Zheng C, Lu HM, Yang Y
Med Phys · 2026 May · PMID 42055767 · Publisher ↗
BACKGROUND: Preclinical small animal experiments play an indispensable role in proton therapy research. However, accurate dose calculation poses a significant challenge because of the low beam energy and the requirement...
BACKGROUND: Preclinical small animal experiments play an indispensable role in proton therapy research. However, accurate dose calculation poses a significant challenge because of the low beam energy and the requirement for submillimeter spatial resolution. Although the Monte Carlo method offers the necessary precision, its high computational cost hinders efficient implementation. PURPOSE: This study aims to develop a GPU-accelerated radiation dose engine for proton radiotherapy (pGARDEN) based on the Monte Carlo method, specifically designed for fast and accurate dose calculation in small animal irradiation. METHODS: In pGARDEN, we optimized the particle transport algorithm to better align with the GPU architecture. Moreover, various acceleration techniques were implemented to boost computational efficiency. To enhance precision, physical parameters, such as energy cutoffs for proton and electron, were tuned to better suit small animal conditions. The performance of pGARDEN was validated against Geant4 simulations and measurements across various beams and phantoms. To demonstrate its practical utility, pGARDEN was applied to calculate a multi-beam proton treatment plan for a lung tumor-bearing mouse model. RESULTS: Compared to Geant4, the engine achieved a > 1000-fold speedup and a 3D gamma passing rate of > 97% with a strict 1%/0.15 mm criterion in all phantom testing scenarios. The integrated depth dose curves and dose profiles showed good agreement with measurements. In the in vivo validation, the 2D gamma passing rates with a 2%/0.3 mm criterion were 95.52% ± 0.74% for the abdomen and 94.18% ± 1.08% for the thorax. Furthermore, pGARDEN calculated the treatment plan with < 1% statistical uncertainty in 4.3 s on an NVIDIA GeForce RTX 4070 Ti GPU, achieving a 100% 3D gamma passing rate with a 2%/0.3 mm criterion. CONCLUSION: pGARDEN can calculate proton dose distribution rapidly and accurately at submillimeter resolution for small animal. It provides a valuable tool for supporting small animal proton radiation experiments, such as the investigation of relative biological effectiveness (RBE) and new therapeutic strategies.
Spectral deep learning-based patient and bowtie scatter correction for clinical photon-counting CT.
Hennemann L, Erath J, Heinkele A … +4 more , Fournié E, Petersilka M, Stierstorfer K, Kachelrieß M
Med Phys · 2026 May · PMID 42050788 · Full text
BACKGROUND: The presence of scatter in computed tomography degrades image quality, and can be caused by the patient and by other components in the beam path, such as the bowtie filter. While conventional energy-integrati...
BACKGROUND: The presence of scatter in computed tomography degrades image quality, and can be caused by the patient and by other components in the beam path, such as the bowtie filter. While conventional energy-integrating detectors do not provide spectral distinction, photon-counting (PC) detectors are energy-selective and provide spectral information about the incoming X-ray photons. Since each energy threshold is affected differently by scatter, this spectral information implicitly encodes the scatter content of a projection. PURPOSE: The purpose of this work is to investigate how the spectral information can be exploited to improve deep learning (DL)-based scatter correction. Furthermore, the performance of joint and separate patient and bowtie scatter correction will be investigated, addressing that bowtie scatter has not been considered in current DL-based approaches. METHODS: We present a DL-based approach that can estimate bowtie and patient scatter jointly and compare it against a separate correction. We also introduce neural network-based methods that incorporate the spectral information inherent in PCCT for scatter correction. We present networks that estimate scatter for up to four energy thresholds simultaneously. Training and validation was performed with Monte Carlo data as well as with real data measured by a clinical PCCT system. RESULTS: When comparing joint and separate patient and bowtie scatter estimation, both methods reduce the mean absolute error (MAE) from 8 HU to 1 HU. All proposed DSE methods effectively reduce scatter artifacts and perform better than the convolution-based reference approach. Incorporating the spectral information further improves the performance, with the DSE variant with four energy thresholds achieving the best overall results for all thresholds. For all energy thresholds tested, the spectral DSE methods reduced scatter errors originating from the patient and the bowtie in PCCT from up to 8 HU to below 1 HU. In addition to the global MAE, we report a critical MAE (MAE) restricted to voxels with uncorrected errors 10 HU, as such deviations are visually perceptible in soft tissue and exceed the noise level of modern CT systems. In all test cases, the proposed spectral methods reduced the MAE from 23.8 HU in the uncorrected images to 1.6 HU after spectral correction. The affected voxels comprised on average 25 % of the image volume, indicating a significant reduction in artifact intensity in the most affected areas. In virtual monoenergetic images (VMI), the application of spectral neural networks resulted in a significant reduction in MAE from 16 HU to 2 HU at 45 keV, from 8 HU to 1 HU at 70 keV, and from 5 HU to under 1 HU at 100 keV. CONCLUSIONS: This paper presents a combined method for correcting patient and bowtie scatter that delivers results equivalent to separate corrections and thus eliminates the need for multiple networks. Further, we demonstrate that deep scatter estimation can effectively exploit the spectral information available to improve scatter correction, especially for spectral applications, like VMIs. Spectral DSE networks slightly outperformed non-spectral variants, with multiple energy thresholds lead to more accurate estimations. This enables the use of one network for scatter correction and eliminates the need for multiple ones, thereby saving computational cost and complexity.
Development and validation of an automated, accurate in-house treatment planning system for pencil-beam scanning carbon ion radiotherapy.
Lee S, Yun Y, Han MC … +7 more , Kim C, Hong CS, Lee H, Kim DW, Jung DM, Kim JS, Kim H
Med Phys · 2026 May · PMID 42050784 · Publisher ↗
BACKGROUND: Carbon ion radiotherapy (CIRT) offers superior physical and biological advantages over photon and proton radiotherapy (PRT). However, due to a need for accurate modeling of the relative biological effectivene...
BACKGROUND: Carbon ion radiotherapy (CIRT) offers superior physical and biological advantages over photon and proton radiotherapy (PRT). However, due to a need for accurate modeling of the relative biological effectiveness (RBE), currently available treatment planning systems (TPS) for CIRT remain limited. This has constrained clinical and research applications by compelling reliance on a narrow set of systems and restricting flexibility for broader application. PURPOSE: This study aimed to develop and validate a new efficient and accurate CIRT TPS for pencil-beam scanning (PBS)-based CIRT, tailored for the Heavy-Ion Therapy Center (HITC) at Yonsei Cancer Center (YCC), incorporating modified microdosimetric kinetic model (mMKM)-based RBE-weighted dose calculation and spot weight optimization. METHODS: The proposed TPS was designed to automate the workflow from CT image import to CIRT plan optimization. Key refinements included 3D Siddon's ray tracing for spot trajectory tracking, air-gap modeling for beam profile adaptation, and 3D stopping power ratio (SPR) calculation. Gaussian beam modeling was performed to calibrate in-air measurements and Monte Carlo (MC)-driven 3D dose kernels in water. These procedures produced dose influence matrices (DIMs) and MC-estimated matrices required for spot weight optimization with mMKM-based RBE-weighted dose calculation, which was further accelerated by Adam optimizer. Validation was performed by generating plans for a water phantom (10 Gy-RBE), a lung case (15 Gy-RBE), and a prostate case (4.3 Gy-RBE), using single-field and multi-field optimizations (MFO). The resulting plans were recalculated in RayStation (v.2025) and TOPAS MC, and compared using dose-volume histogram (DVH) and gamma passing rate (GPR) at 2%/2 mm. RESULTS: The proposed TPS successfully automated the CIRT plan optimization within approximately 1 min for ∼1000 spots, which produced uniform RBE-weighted dose coverage in virtual water phantom, the lung patient, and the prostate patient cases. When the spot weights optimized by the proposed framework were recalculated using a commercial TPS and TOPAS MC, the resulting dose distributions closely matched those of the proposed TPS. Quantitatively, GPRs exceeding 98% were achieved for both physical and RBE-weighted dose at the 2%/2 mm criterion, except for the prostate single-field case, which yielded a GPR of 97.37% for the physical dose, relative to TOPAS MC, and a GPR of 96.28% for the RBE-weighted dose, relative to RayStation. These findings confirmed strong agreement between the proposed TPS, MC simulations, and a commercial TPS. CONCLUSION: A new, fully automated in-house CIRT TPS was developed and validated, demonstrating high dosimetric accuracy and computational efficiency, even comparable to a commercial TPS and MC simulations.
Cross-attention multimodal fusion for EGFR mutation prediction: A predictive model using CT and whole-slide images.
Jiang Y, Feng B, Chen Y … +6 more , Xu J, Li R, Zhang W, Chen X, Long W, Li K
Med Phys · 2026 May · PMID 42050780 · Publisher ↗
BACKGROUND: The prediction of Epidermal Growth Factor Receptor (EGFR) mutation status in advanced lung adenocarcinoma is crucial for targeted therapy. Since EGFR mutations manifest as both macroscopic imaging features on...
BACKGROUND: The prediction of Epidermal Growth Factor Receptor (EGFR) mutation status in advanced lung adenocarcinoma is crucial for targeted therapy. Since EGFR mutations manifest as both macroscopic imaging features on CT and microscopic morphological changes in tissue, integrating these multiscale signals is essential for a comprehensive diagnostic assessment. However, current related research faces two key limitations: on one hand, unimodal deep learning models suffer from limited representational power; on the other hand, existing multimodal methods fail to address the inherent data structural discrepancies between continuous CT and discrete WSI, often losing critical fine-grained details due to forced data compression or shared semantic bottlenecks. OBJECTIVE: To address the above limitations and improve the reliability of EGFR mutation status prediction, this study aims to propose a novel multimodal fusion framework (MFCA) that can effectively capture cross-modal semantic interactions and align imaging features across different scales. METHODS: A novel MFCA based on Cross-Attention (MFCA) is proposed, and its implementation steps are as follows: 1. First, a region-of-interest-guided approach is utilized to coarsely segment whole-slide histopathology images (WSI) into three constituent regions, namely cancerous, stromal, and other regions; 2. Then, a dual-branch encoder is employed to separately extract features from two types of imaging data-global features from Computed Tomography (CT) scans and region-specific features from the segmented WSI; 3. Critically, a bidirectional cross-attention module is introduced into the framework, which is designed to facilitate deep semantic interaction and alignment between the macroscopic context of CT imaging and the microscopic context of histopathology, thereby achieving highly efficient and discriminative feature fusion. RESULTS: On the external validation set, our MFCA framework achieved robust performance, with Area Under the Curve (AUC) values of 0.758(95% CI: 0.683-0.832) for cancerous regions, 0.805(95% CI: 0.716-0.900) for stromal regions, and 0.760(95% CI: 0.686-0.833) for other regions. The model's performance, particularly in the stromal component, was statistically superior to all baseline and competing models. CONCLUSION: The proposed MFCA framework predicts EGFR mutation status by innovatively integrating macroscopic CT imaging with region-specific microscopic WSI features. It serves as a valuable computational tool to support precision oncology for patients with advanced lung adenocarcinoma.
A proof-of-concept study of multitask learning for cranial synthetic CT generation across heterogeneous MRI field strengths.
Xin Z, Zhang Y, Wu C … +6 more , Liu D, Gu C, Greco E, Middlebrooks EH, Hua J, Guo J
Med Phys · 2026 May · PMID 42027157 · Full text
BACKGROUND: Accurate synthesis of computed tomography (CT) images from magnetic resonance imaging (MRI) is clinically valuable for cranial applications such as attenuation correction, radiotherapy planning, and image-gui...
BACKGROUND: Accurate synthesis of computed tomography (CT) images from magnetic resonance imaging (MRI) is clinically valuable for cranial applications such as attenuation correction, radiotherapy planning, and image-guided interventions. However, the heterogeneity of MRI data across magnetic field strengths and pulse sequences limits the generalizability of existing methods, posing a barrier to clinical translation. PURPOSE: This study aims to reformulate cranial CT synthesis as a modular, structurally coupled problem using a deep learning approach, enhancing adaptability across heterogeneous MRI conditions, including different field strengths and sequence protocols. METHODS: We implemented a cascaded multitask pipeline that jointly models skull segmentation and Hounsfield Unit (HU) regression in anatomically targeted regions. A 3D patch-based training paradigm shifts the modeling focus from global image translation to localized bone feature extraction. The backbone leverages a residual Mamba-based state space model within a 3D U-Net structure to improve spatial representation, while a Transformer U-Net serves as a widely recognized reference baseline comparison. The model was trained and evaluated in terms of multi-modal (T1-weighted and T2-FLAIR) and cross-domain transferability from a public 1.5 Tesla brain dataset (n = 37) to an independent 7 Tesla clinical brain dataset (n = 44). Performance was assessed using Dice and Jaccard indices for segmentation accuracy and mean absolute error (MAE) for HU regression. RESULTS: Quantitative analysis showed that our multitask pipeline, incorporating both morphological and HU map prediction stages, significantly outperformed conventional direct MRI-to-CT mapping across all metrics on the public 1.5T dataset (p < 0.05). Consistent performance on an external 7T clinical dataset (p < 0.001) further demonstrated the method's robustness and adaptability across field strengths. CONCLUSION: These findings support that task-structured training for modality transformation markedly improves both accuracy and generalizability of cranial CT synthesis across heterogeneous MRI conditions. The observed consistency across field strengths validates the robustness of the proposed methodology.
A Hybrid-Mamba network with dual level attention fusion for multimodal COPD diagnosis.
Zhang F, Wu D, Han S … +3 more , Wang H, Zhang W, Ma C
Med Phys · 2026 May · PMID 42027155 · Publisher ↗
BACKGROUND: Chronic obstructive pulmonary disease (COPD) remains difficult to diagnose reliably due to limitations of conventional spirometry and CT interpretation. Although deep learning has shown promise, CNNs are cons...
BACKGROUND: Chronic obstructive pulmonary disease (COPD) remains difficult to diagnose reliably due to limitations of conventional spirometry and CT interpretation. Although deep learning has shown promise, CNNs are constrained by local receptive fields, and ViTs are constrained by high computational cost, highlighting the relevance of multimodal integration of CT and clinical data for improving COPD diagnostic accuracy. PURPOSE: In this study, we propose a Hybrid-Mamba Network with Dual Level Attention Fusion for multimodal COPD diagnosis. METHODS: We retrospectively enrolled 381 participants (184 with COPD and 197 healthy controls). Clinical data encompassed basic information, respiratory symptoms, blood gas analysis, pulmonary function tests, and blood routine tests. The framework employs a Hybrid-Mamba architecture for efficient CT feature representation, leverages a tailored Hybrid-DWConv-AAS Block for enhanced feature integration, and incorporates a Dual Level Attention Fusion Block to adaptively integrate CT and clinical data. RESULTS: On the test set, our proposed network achieved an AUC of 0.985 and an accuracy of 0.947, with an average per-patient inference time of 97.54 ms, while maintaining robust diagnostic performance under simulated perturbations. CONCLUSIONS: These findings indicate that the framework provides an efficient and robust approach for COPD diagnosis.
A preclinical setup for spatially fractionated radiation therapy with electrons.
Taylor ERJF, Ruan JL, Paillas S … +3 more , Tullis IDC, Higgins GS, Petersson K
Med Phys · 2026 May · PMID 42027150 · Full text
BACKGROUND: Spatially fractionated radiation therapy (SFRT) has therapeutic potential as a priming therapy which boosts tumor control. However, the optimal delivery and spatial fractionation parameters have not been deci...
BACKGROUND: Spatially fractionated radiation therapy (SFRT) has therapeutic potential as a priming therapy which boosts tumor control. However, the optimal delivery and spatial fractionation parameters have not been deciphered and the mechanisms at play are not yet fully understood. PURPOSE: This paper highlights our preclinical setups for mini-grid SFRT with 6 MeV electrons delivered at conventional to ultrahigh dose rates, using a flexible collimator system. These setups let us explore relevant spatial fractionation parameters to observe their effect on tumor growth and normal tissue toxicity. Preclinical studies here may reveal the parameters of highest clinical relevance for SFRT and combination therapies. METHODS: For preclinical experiments with electron spatial fractionation, 6.5 mm thick brass collimators were made with 7- (or 19-) hole hexagonally packed ∅ 0.65-2 mm apertures, with 1.6-5 mm CTC distances. Irradiated EBT-XD Gafchromic film downstream of collimators were analyzed to obtain peak-to-valley dose ratios (PVDR), full width at half maxima (FWHM), and peak doses at various depths in solid water, and at surface when increasing the separations from collimators in air. Male and female C57BL/6 mice were injected subcutaneously with UPPL1541 bladder cancer cells in the right flank. After 10-13 days, a single dose treatment was delivered to the tumors with either ∅14 mm circular homogeneous field (10 Gy delivered at 3 kGy s), or using a 7-hole (∅ of 2 and 5 mm center-to-center distances) spatially fractionated field; peak doses of 30 Gy delivered at 820 Gy s, 20 Gy at 860 Gy s, and 20 Gy at < 0.1 Gy s. Tumor growth and time to triple tumor volume (TTTV) were measured and compared between treatment regimens. RESULTS: Similar PVDRs were obtained with 7- and 19-hole inserts (35 and 31 at surface, respectively). Peak widths increased with depth, and maximal peak dose rates were > 1.8 kGy s. A displacement in air from the collimator exit decreased PVDRs at the phantom surface; from 32 to 16 at ∼10 mm distance, and to 6 at ∼20 mm distance. Peak doses also reduced to ∼57 % at 10 mm distance, and to ∼33% at 20 mm distance. Film measurements at the mouse phantom surface produced peak and valley dose rates of > 850 Gy s and ∼60 Gy s respectively, with a PVDR > 14. Tumor growth delays for spatially fractionated FLASH 30 Gy (peak dose, with a 2.1 Gy valley dose, and a 10 Gy average dose) and homogeneous FLASH 10 Gy electron irradiation regimens were similar. Both regimens also demonstrated significantly longer TTTV compared to control and spatially fractionated conventional 20 Gy (peak dose, with a 1.4 Gy valley dose, and a 6.7 Gy average dose) regimens (p < 0.05). No significant differences in body weight and skin damage were observed, indicating acceptable treatment tolerability. CONCLUSIONS: Spatially fractionated electron FLASH treatments with 30 Gy peak doses and 2.1 Gy valley doses provide effective tumor growth delay and prolonged tumor control akin to 10 Gy homogeneous irradiations. Here we demonstrate that combining spatial modulation, higher peak doses, and FLASH dose rates can produce favorable tumor response.
Performance evaluation of a clinical SPECT system with multifocal collimator for brain studies.
Otani T, Kunikane Y, Takashi S … +4 more , Azane S, Fujita A, Matsuda N, Amano M
Med Phys · 2026 May · PMID 42027145 · Full text
BACKGROUND: Single-photon emission computed tomography (SPECT) is an indispensable examination for evaluating brain function to diagnose dementia. During head examination, the collimator makes contact with the shoulders,...
BACKGROUND: Single-photon emission computed tomography (SPECT) is an indispensable examination for evaluating brain function to diagnose dementia. During head examination, the collimator makes contact with the shoulders, which often causes the patient discomfort. A new multifocal high-resolution collimator (SMARTZOOM high resolution and extended [SZHRX]) can maintain image quality, even when there is distance between the patient and the collimator. Therefore, it may be a useful tool to prevent patient discomfort during head imaging. PURPOSE: In this study, we evaluated spatial resolution, sensitivity, uniformity, and %contrast in several basic experiments using different phantoms to clarify the image quality of the multifocal collimator. METHODS: We used Tc and I nuclides, which were sealed in several phantoms and imaged using SZHRX. The rotation radius for SZHRX was varied from 24-34.5 cm. For comparison, low-energy high-resolution (LEHR) and low-medium energy general purpose (LMEGP) images were acquired with a rotation radius of 14 cm. Spatial resolution, sensitivity, uniformity, and %contrast were calculated from the images obtained using each phantom and collimator. RESULTS: For both Tc and I, the wider the radius of rotation, the larger the full-width half maximum (FWHM) of SZHRX. The FWHM of SZHRX was larger than that of LEHR, and the FWHM of SZHRX was smaller than that of LMEGP. The sensitivity of SZHRX increased as the distance increased, regardless of nuclides. For Tc, SZHRX had higher sensitivity than LEHR. The sensitivity of SZHRX for I was lower than that of LMEGP at short distances, with the same sensitivity at a radius of rotation of 26-28 cm. The coefficient of variation (CV) of Tc for SZHRX was lower than that for LEHR, and the further the distance, the lower the CV. For I, the CVs of SZHRX and LMEGP were comparable. The %contrast of SZHRX worsened as the rotation radius increased. For Tc, the %contrast of SZHRX was lower than that of LEHR. For I, the %contrast of SZHRX and LMEGP were comparable. CONCLUSIONS: It is possible to acquire images with high spatial resolution and uniformity, even if the rotation radius is widened by the use of SZHRX. The image quality of SZHRX obtained in this study will be helpful for future clinical applications.
A transformer-based patient-agnostic tumor motion tracking model (TransTracking): A feasibility study in radiotherapy for lung cancer.
Abuduxiku M, Chai N, An X … +8 more , Li Y, Wang J, Luo Y, Wu W, Liu Y, Hu Y, Li Q, He P
Med Phys · 2026 May · PMID 42027140 · Publisher ↗
BACKGROUND: Accurate and real-time localization of thoracic tumor targets is essential for effective radiation therapy. Recently, Transformer architectures have demonstrated strong global reasoning capabilities across mu...
BACKGROUND: Accurate and real-time localization of thoracic tumor targets is essential for effective radiation therapy. Recently, Transformer architectures have demonstrated strong global reasoning capabilities across multiple frames by leveraging both self-attention and cross-attention mechanisms. Transformers have therefore been applied to object tracking with great success. By combining Image Guided Radiation Therapy (IGRT) technologies and deep learning-based object tracking architecture, it is possible to deliver radiation doses to the target area with high accuracy. PURPOSE: This study develops a transformer-based patient-agnostic tracking model (TransTracking) for surface and markerless internal target tracking in thoracic tumor radiotherapy. METHODS: We trained the TransTracking model using the training splits of publicly available object tracking datasets. Subsequently, for internal target tracking, the model is fine-tuned using 10,000 digitally reconstructed radiograph (DRR) images generated from the actual 4DCT datasets of 25 patients. The DRR images are annotated with bounding boxes of the moving tumor. Our method learns to directly predict the target classification and bounding-box regression weights through end-to-end training, enabling accurate target localization in each frame for both surface and internal target tracking sequences. The tracking performance of the trained model was evaluated in 20 volunteers for surface tracking and using DRR images generated from 20 4DCT datasets for internal tumor tracking. To address the limited availability of medical images for training, we conducted the data augmentation procedure to 4DCT datasets and expanded the data scale 40-fold in total. RESULTS: For the surface marker tracking, the mean absolute deviation (MAD) ± standard deviation (SD) between the model-predicted and the actual positions for 20 volunteers was 0.07 ± 0.06 mm, 0.12 ± 0.13 mm, and 0.29 ± 0.20 mm in left-right, superior-inferior, and anterior-posterior directions, respectively. In each directional axis, over 85% of frames exhibited a model-predicted target position within 0.5 mm of the corresponding ground-truth position. For the internal tumor tracking, the MAD ± SD between the predicted and annotated center positions of the tumor bounding boxes is 1.49 ± 1.39 mm, with a mean Intersection over Union (IoU) value of 0.83 and an area under curve (AUC) score of 82% for 20 patients. Additionally, our transformer-based model can extract the target position in 81 ms after an image is acquired. CONCLUSIONS: This study proposed a novel Transformer-based deep learning method aimed at training a patient-agnostic tumor motion tracking model in radiotherapy. The model enables real-time, high-precision tracking of surface markers using vision cameras, offering a cost-effective and compact solution. Additionally, we demonstrated that our method can accurately locate tumor target areas in DRR images with high precision, without the need for individualized training or the implantation of fiducial markers. This feasibility study demonstrates the strong potential of our strategy as a clinically viable solution for moving tumor IGRT.
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