Dose Calculation and Prediction Methods for Gamma Knife Stereotactic Radiosurgery

감마나이프 방사선수술(GKRS)의 높은 정밀도와 정확성은 치료 성공을 위한 기본 요건이다. 방사선의 급격한 감소와 함께 정교한 방사선 전달 및 선량 기울기가 임상적으로 적용되어야 하므로 방사선량 측정 및 기하학적 정확성을 보장하고 감마나이프 방사선수술에서 발생할 수 있는 모든 위험 요인을 줄이기 위해서는 전용 정도관리(QA) 프로그램이 필요하다. 본 연구에서는 독립적인 검증 프로그램 가변 타원체 모형화기술(Variable Ellipsoid Modeling Technique: VEMT)을 적용해서 감마나이프 치료계획 시스템 렉셀 감마플랜의 알고리즘에 사용된 단일 샷 선량 분포의 정확성을 검증하였다. 감마나이프 퍼펙션(PFX)에 장착한 직경 160 mm의 구형 ABC 팬텀에 조사한 단일 샷의 선량 분포를 평가했다. 단일 샷의 조사는 ABC 팬텀의 중심으로 향하게 하여 x, y 및 z 축을 따라 4, 8 및 16 mm 크기의 시준기 배치가 고려되었다. 감마나이프 방사선수술에서 사용되는 감마나이프 퍼펙션 치료계획 시스템은 렉셀 감마플랜(LGP) 버전 10.1.1이 사용되었다. VEMT의 검증을 통해서 감마나이프 방사선수술의 정확성은 배가 될 것이다. 그래서 VEMT 검증 후 감마나이프 방사선수술의 정확성과 정밀성을 토대로 임상 적용이 최종적으로 수행되어야 한다. 특히 환자의 머리가 직경 160mm의 구형으로 시뮬레이션된 조건에서 50% 등선량 높이 수준의 너비, 즉 최대반값폭(FWHM)이 검토되었다. VEMT를 통해 예측된 x, y, z 축의 선량 분포에 관한 모든 데이터는 4 mm 및 8 mm 시준기 배치에 대해 z 축을 따라 최대반값폭과 반그늘(PENUMBRA)의 약간의 차이점을 제외하고는 사양 내(등선량 50%에서 1 mm 이내)에서 LGP의 선량 분포와 훌륭하게 일치했다. 최대반값폭의 최대 불일치는 모든 시준기 배치에서 2.3% 미만이었다. 반그늘의 최대 불일치는 z 축을 따라 8 mm 시준기에 대해서 0.07 mm로 주어졌다. VEMT와 LGP로 얻은 선량 분포에서 최대반값폭과 반그늘의 차이는 감마나이프 방사선수술에서 임상적 유의성을 부여하기에는 너무 작았다. 이 연구의 결과는 전 세계 감마나이프 방사선수술에 관련된 의학물리학자를 위한 참고 자료로 활용될 수 있으리라 사료된다. 따라서 우리는 LGP의 결과물에 대한 독립적인 검증방법 VEMT를 포함하는 정기 예방정비 프로그램을 통해 결정된 모든 시준기 배치에 대한 선량 분포의 유효성을 확인하고 감마나이프 방사선수술 환자에게 임상적으로 완벽한 치료를 보장할 수 있다. 그래서 VEMT의 활용은 시스템을 안전하게 검증하고 운영할 수 있는 정도관리의 한 부분이 될 것으로 기대한다. A high degree of precision and accuracy in Gamma Knife Radiosurgery(GKRS) is a fundamental requirement for therapeutical success. Elaborate radiation delivery and dose gradients with the steep fall-off of radiation are clinically applied thus necessitating a dedicated Quality Assurance(QA) program in order to guarantee dosimetric and geometric accuracy and reduce all the risk factors that can occur in GKRS. In this study, as a part of QA we verified the accuracy of single-shot dose profiles used in the algorithm of Gamma Knife Perfexion(PFX) treatment planning system employing Variable Ellipsoid Modeling Technique(VEMT). We evaluated the dose distributions of single-shots in a spherical ABC phantom with diameter 160 mm on Gamma Knife PFX. The single-shots were directed to the center of ABC phantom. Collimating configurations of 4, 8, and 16 mm sizes along x, y, and z axes were studied. Gamma Knife PFX treatment planning system being used in GKRS is called Leksell GammaPlan(LGP) ver 10.1.1. From the verification like this, the accuracy of GKRS will be doubled. Then the clinical application must be finally performed based on precision and accuracy of GKRS. Specifically the width at the 50% isodose level, that is, Full-Width-of-Half-Maximum(FWHM) was verified under such conditions that a patient's head is simulated as a sphere with diameter 160mm. All the data about dose profiles along x, y, and z axes predicted through VEMT were excellently consistent with dose profiles from LGP within specifications(<TEX>${\leq}1mm$</TEX> at 50% isodose level) except for a little difference of FWHM and PENUMBRA(isodose level: 20%~80%) along z axis for 4 mm and 8mm collimating configurations. The maximum discrepancy of FWHM was less than 2.3% at all collimating configurations. The maximum discrepancy of PENUMBRA was given for the 8 mm collimator along z axis. The difference of FWHM and PENUMBRA in the dose distributions obtained with VEMT and LGP is too small to give the clinical significance in GKRS. The results of this study are considered as a reference for medical physicists involved in GKRS in the whole world. Therefore we can work to confirm the validity of dose distributions for all collimating configurations determined through the regular preventative maintenance program using the independent verification method VEMT for the results of LGP and clinically assure the perfect treatment for patients of GKRS. Thus the use of VEMT is expected that it will be a part of QA that can verify and operate the system safely.

Gamma Knife radiosurgery (GKRS) is a well-established technique in radiation therapy (RT) for treating small-size brain tumors. It administers highly concentrated doses during each treatment fraction, with even minor dose errors posing a significant risk of causing severe damage to healthy tissues. It underscores the critical need for precise and meticulous precision in GKRS. However, the planning process for GKRS is complex and time-consuming, heavily reliant on the expertise of medical physicists. Incorporating deep learning approaches for GKRS dose prediction can reduce this dependency, improve planning efficiency and homogeneity, streamline clinical workflows, and reduce patient lagging times. Despite this, precise Gamma Knife plan dose distribution prediction using existing models remains a significant challenge. The complexity stems from the intricate nature of dose distributions, subtle contrasts in CT scans, and the interdependence of dosimetric metrics. To overcome these challenges, we have developed a "Cascaded-Deep-Supervised" Convolutional Neural Network (CDS-CNN) that employs a hybrid-weighted optimization scheme. Our innovative method incorporates multi-level deep supervision and a strategic sequential multi-network training approach. It enables the extraction of intra-slice and inter-slice features, leading to more realistic dose predictions with additional contextual information. CDS-CNN was trained and evaluated using data from 105 brain cancer patients who underwent GKRS treatment, with 85 cases used for training and 20 for testing. Quantitative assessments and statistical analyses demonstrated high consistency between the predicted dose distributions and the reference doses from the treatment planning system (TPS). The 3D overall gamma passing rates (GPRs) reached 97.15% ± 1.36% (3mm/3%, 10% threshold), surpassing the previous best performance by 2.53% using the 3D Dense U-Net model. When evaluated against more stringent criteria (2mm/3%, 10% threshold, and 1mm/3%, 10% threshold), the overall GPRs still achieved 96.53% ± 1.08% and 95.03% ± 1.18%. Furthermore, the average target coverage (TC) was 98.33% ± 1.16%, dose selectivity (DS) was 0.57 ± 0.10, gradient index (GI) was 2.69 ± 0.30, and homogeneity index (HI) was 1.79 ± 0.09. Compared to the 3D Dense U-Net, CDS-CNN predictions demonstrated a 3.5% improvement in TC, and CDS-CNN's dose prediction yielded better outcomes than the 3D Dense U-Net across all evaluation criteria. The experimental results demonstrated that the proposed CDS-CNN model outperformed other models in predicting GKRS dose distributions, with predictions closely matching the TPS doses.

In proton beam therapy (PBT) for hepatocellular carcinoma (HCC), deep learning (DL)-based dose prediction offers clinical value by providing immediate reference dose distributions as a guideline tool for treatment planners and enabling virtual PBT dose assessment at institutions lacking PBT facilities to support clinical decision-making. This study proposes a dose gradient-aware DL training approach and a beam arrangement-free framework that predicts dose distributions from computed tomography (CT) images and target/organs-at-risk structures without requiring beam arrangement inputs. Using data from 172 HCC patients, we systematically compared 20 DL models combining five architectures with four loss functions, including a novel dose gradient-aware loss capturing PBT-specific dose distribution characteristics arising from the Bragg peak. Prediction accuracy was evaluated using dose metrics for clinical target volume, planning target volume, and normal liver, 10% dose region volume assessment, visual examination, Dice similarity coefficient (DSC) for 10% dose region, and dose-volume histogram (DVH) analysis. DL models employing dose gradient-aware loss demonstrated reduced prediction errors in target volume dose metrics, achieved the lowest errors in 10% dose region volume when combined with U-Net (MAE: 35.4cm3, RMSE: 51.3cm3), attained the highest DSC (0.88 ± 0.05), showed the closest DVH agreement with clinical plans for both target structures and normal liver. A comprehensive evaluation consistently confirmed the superiority of dose gradient-aware loss across all metrics, demonstrating the importance of physics-informed, dose gradient-aware learning for accurate PBT dose distribution prediction. Particularly, DL models combining simple architectures with dose gradient-aware loss predicted doses that closely approximated clinical doses in PBT.

BackgroundNasopharyngeal carcinoma (NPC), an endemic malignancy in Southeast Asia and southern China, necessitates precise delineation of the gross tumor volume (GTV) for radiotherapy to optimize patient prognosis. Positron emission tomography (PET) imaging offers valuable metabolic insights to guide radiotherapy planning but is hampered by manual GTV segmentation, which is labor-intensive and prone to inter- and intra-observer variability. Conventional segmentation methods are inherently limited, and comparative evaluations of deep learning (DL) frameworks for PET-based primary NPC lesion segmentation remain scarce. Thus, this study aimed to optimize the delineation of primary NPC radiotherapy target volumes by leveraging PET-image-based DL segmentation technology, with the goal of enhancing both the precision and efficiency of lesion delineation.MethodsResearchers retrospectively collected PET imaging data from 212 NPC patients at the Affiliated Hospital of Guangdong Medical University. The patients were randomly divided into training (170 patients) and testing (42 patients) datasets. A radiation oncologist and a nuclear medicine physician collaboratively delineated the cancer lesion boundaries through consensus. After data preprocessing, three DL models (Res-Unet, Nn-Unet, and Nn-Former) were used to automatically segment the lesions. Training was based on the training dataset. During evaluation, the models’ segmentation performance was comprehensively assessed using Dice similarity coefficient (DSC) and Hausdorff distance (HD). A visual analysis of the results was also conducted to intuitively understand the models’ segmentation capabilities.ResultsIn the training set, Nn-Unet achieved the highest DSC of 0.869, whereas in the testing set, its DSC was 0.833. The DSC for Nn-Former hovered around 0.8 in both the training and testing sets. In contrast, Res-Unet demonstrated the lowest DSC values among the three models, particularly in the testing set (DSC =0.794). Statistical analysis revealed that the DSC of Nn-Unet was significantly higher than that of both Res-Unet and Nn-Former in the training set (P<0.01 for both comparisons). Regarding the 95% HD and average surface distance (ASD) values, Nn-Unet outperformed the other two models in both the training and testing sets. However, Res-Unet exhibited a much higher HD value in the testing set compared to the training set. The Loss curves for all models gradually decreased as training progressed, indicating that the models were learning relevant features of NPC lesions. The final Loss value for Res-Unet was approximately 0.7, whereas Nn-Unet and Nn-Former had final Loss values below 0.8.ConclusionsThe PET-image-based automatic segmentation model for the primary tumor of NPC established in this study demonstrates the clinical potential of Nn-Unet in primary lesion of NPC segmentation tasks.

BackgroundAutomatic radiotherapy (RT) planning based on deep learning (DL) has been extensively researched. However, it is challenging to import the predicted dose distribution into mainstream treatment planning systems (TPSs) and generate clinically deliverable plans.PurposeTo investigate the feasibility and accuracy of an automatic volumetric modulated arc therapy (VMAT) and intensity‐modulated radiation therapy (IMRT) planning method for generation of universally deliverable plans based on DL dose prediction and dose rings optimization.MethodsFirst, dose distributions were predicted using a three‐dimensional (3D) Fusion Residual Unet (F‐ResUnet) DL network with data from two hospitals, which included 230 and 210 gynecological cancer (GC) patients underwent VMAT and IMRT, respectively. Then, the predicted dose distributions were discretized into dose rings to optimize the plans automatically in two mainstream TPSs based on the dose rings. Finally, the deliverability of generated plans was verified with patient‐specific quality assurance (PSQA).ResultsThe predicted dose distributions were clinically acceptable with a target coverage over 95%. Compared with the clinical plans, the automatic plans optimized with dose rings achieved a similar dose coverage on planning target volumes (PTV) with an average target coverage over 96.5%. For organs at risk (OARs) sparing, automatic VMAT plans markedly decreased the V30Gy of left femoral head (p = 0.05), right femoral head (p = 0.004), and small intestine (p = 0.04). The V45Gy of bladder and rectum in the automatic IMRT plans were reduced by approximately 7% and 9%, respectively. Deliverability verification with PSQA achieved a mean gamma passing rate of 99.1%, 97.1% and 98.3%, 95.0% under the criteria of 3%/3 mm and 3%/2 mm for VMAT and IMRT plans, respectively.ConclusionsThe proposed automatic planning method combining DL dose prediction and dose rings optimization was feasible to generate universally deliverable VMAT and IMRT plans for gynecological cancer (GC) patients.

BackgroundThe evaluation of automatic segmentation algorithms is commonly performed using geometric metrics. An analysis based on dosimetric parameters might be more relevant in clinical practice but is often lacking in the literature. The aim of this study was to investigate the impact of state-of-the-art 3D U-Net-generated organ delineations on dose optimization in radiation therapy (RT) for prostate cancer patients.MethodsA database of 69 computed tomography images with prostate, bladder, and rectum delineations was used for single-label 3D U-Net training with dice similarity coefficient (DSC)-based loss. Volumetric modulated arc therapy (VMAT) plans have been generated for both manual and automatic segmentations with the same optimization settings. These were chosen to give consistent plans when applying perturbations to the manual segmentations. Contours were evaluated in terms of DSC, average and 95% Hausdorff distance (HD). Dose distributions were evaluated with the manual segmentation as reference using dose volume histogram (DVH) parameters and a 3%/3 mm gamma-criterion with 10% dose cut-off. A Pearson correlation coefficient between DSC and dosimetric metrics, i.e. gamma index and DVH parameters, has been calculated.Results3D U-Net-based segmentation achieved a DSC of 0.87 (0.03) for prostate, 0.97 (0.01) for bladder and 0.89 (0.04) for rectum. The mean and 95% HD were below 1.6 (0.4) and below 5 (4) mm, respectively. The DVH parameters, V_{60/65/70,{mathrm{Gy}}} for the bladder and V_{50/65/70,{mathrm{Gy}}} for the rectum, showed agreement between dose distributions within pm , 5% and pm ,2%, respectively. The D_{98/2%} and V_{95%}, for prostate and its 3 mm expansion (surrogate clinical target volume) showed agreement with the reference dose distribution within 2% and 3 Gy with the exception of one case. The average gamma pass-rate was 85%. The comparison between geometric and dosimetric metrics showed no strong statistically significant correlation.ConclusionsThe 3D U-Net developed for this work achieved state-of-the-art geometrical performance. Analysis based on clinically relevant DVH parameters of VMAT plans demonstrated neither excessive dose increase to OARs nor substantial under/over-dosage of the target in all but one case. Yet the gamma analysis indicated several cases with low pass rates. The study highlighted the importance of adding dosimetric analysis to the standard geometric evaluation.

Segmentation of cardiac structures is essential for cardiac function evaluation using cine magnetic resonance imaging (MRI). Deep learning can be used to segment cardiac structures in cine cardiac MRI with high accuracy, but this approach requires fully annotated datasets for training, which are difficult to obtain. Semi-supervised segmentation methods provide a way to alleviate the burden of manual labeling by using labeled and unlabeled data for training. However, these methods generally provide suboptimal segmentationaccuracies. To develop a semi-supervised method that utilizes relatively small training datasets and under-annotations for improved cine cardiac MRIsegmentation. The proposed approach consists of deformable registration, fully and weakly supervised segmentation, and a temporal attention perceiver (TAP). The registration module was trained to warp labeled frames to generate pseudo labels for unlabeled frames. The warped labeled images were used to train the fully supervised segmentation network. The unlabeled images and the pseudo label were used to train the weakly supervised segmentation model, and the segmentation prediction was compared with the input pseudo label as an auxiliary loss to the registration module. The TAP module was employed to generate optimized features for the warped labeled and the original unlabeled images both paired with the original labeled image. Consistency between the resulting features was enforced to refine cross-instance feature alignment to facilitate the registration. One hundred, twenty, and ten subjects from the Automatic Cardiac Diagnosis Challenge (ACDC) and seventy-five, thirty, and fifteen cases from the Multi-Vendor & Multi-Disease (M&Ms) Cardiac Image Segmentation Challenge were used for training, each with random end-systolic (ES)/end-diastolic (ED) frames labeled. The optimized models were used to segment the remaining 50 ACDC and 50 M&Ms subjects. The proposed approach was compared with several commonly used semi-supervised segmentation methods in terms of Dice-similarity-coefficients (DSC), average-symmetric-surface-distance (ASSD), and Hausdorff-distance (HD) for left (LV) and right (RV) ventricular cavity and myocardium (Myo). A Unet trained on the same subjects each with both frames labeled was used as an upper bound (Unet_UB). Using 100 ACDC training subjects, our approach yielded DSC = 0.910 0.063, ASSD = 1.37 0.63 mm, and HD = 6.38 2.99 mm for RV, DSC = 0.894 0.024, ASSD = 1.20 1.12 mm, and HD = 4.67 3.22 mm for Myo, and DSC = 0.934 0.056, ASSD = 1.25 1.63 mm, and HD = 3.97 5.76 mm for LV. A bidirectional copy-paste (BCP) method performed the best among the comparative methods and generated DSC = 0.902 0.060, ASSD = 1.45 0.60 mm, and HD = 7.50 3.20 mm for RV, DSC = 0.885 0.030, ASSD = 1.28 0.80 mm, and HD = 5.80 2.80 mm for Myo, and DSC = 0.920 0.068, ASSD = 1.15 0.40 mm, and HD = 4.20 3.30 mm for LV. For Unet_UB, these were 0.905 0.068, 1.48 0.61 mm, and 6.35 2.85 mm for RV, 0.895 0.030, 1.05 0.45 mm, and 4.40 3.09 mm for Myo, and 0.941 0.044, 1.02 0.34 mm, and 3.17 1.63 mm for LV. Similar trends were observed when using 75 M&Ms training subjects. For all the experiments, our approach outperformed BCP in general and yielded segmentation accuracies comparable to Unet_UB. The proposed approach outperformed several commonly used semi-supervised segmentation methods and yielded segmentation accuracies on par with fully supervised Unet using various relatively small datasets and under annotations fortraining.

PurposeThe purpose of this study is to investigate the use of a deep learning architecture for automated treatment planning for proton pencil beam scanning (PBS).MethodsA 3‐dimensional (3D) U‐Net model has been implemented in a commercial treatment planning system (TPS) that uses contoured regions of interest (ROI) binary masks as model inputs with a predicted dose distribution as the model output. Predicted dose distributions were converted to deliverable PBS treatment plans using a voxel‐wise robust dose mimicking optimization algorithm. This model was leveraged to generate machine learning (ML) optimized plans for patients receiving proton PBS irradiation of the chest wall. Model training was carried out on a retrospective set of 48 previously‐treated chest wall patient treatment plans. Model evaluation was carried out by generating ML‐optimized plans on a hold‐out set of 12 contoured chest wall patient CT datasets from previously treated patients. Clinical goal criteria and gamma analysis were used to compare dose distributions of the ML‐optimized plans against the clinically approved plans across the test patients.ResultsStatistical analysis of mean clinical goal criteria indicates that compared to the clinical plans, the ML optimization workflow generated robust plans with similar dose to the heart, lungs, and esophagus while achieving superior dosimetric coverage to the PTV chest wall (clinical mean V95 = 97.6% vs. ML mean V95 = 99.1%, p < 0.001) across the 12 test patients.ConclusionsML‐based automated treatment plan optimization using the 3D U‐Net model can generate treatment plans of similar clinical quality compared to human‐driven optimization.

The vessel-plaque interface detection and lesion length of coronary artery disease are still challenging using the deep learning with intravascular ultrasound (IVUS). We used AI-based image segmentation technology to develop a deep learning model for classifying frames with IVUS-derived target lesion. Methods. A total of 3,782 target lesion frames from 28 acute coronary syndrome(ACS) patients were randomized into training and test sets in a 2:1 ratio. The training algorithm was based on the measurement of the core lab. A segmentation model using non-local blocks was used to increase the similarity between pixels of the same class in a U-Net-based model. The edge detection was performed on the region of interest, and the detected edge was used as an aid in deriving the final segmentation result. And the morphological guided measurements were used to provide the information about the vessel area, lumen area, plaque burden and lesion length. Quantitative evaluation was conducted on four scales: Jaccard Measure (JM), Dice Similarity Coefficient (DSC), Hausdorff Distance (HD), and Percentage of Area Difference (PAD). And we compared the diagnostic accuracy to detect the vessel-plaque interface of the target lesion. Results. In the training sample (1,256 IVUS frames of target lesion), and the test samples (1,072 IVUS frames of target lesion) were used for the proposed model. The external elastic membrane area (EEM), lumen area, plaque area and thrombus were assessed with the proposed algorithm. The measurement of EEM was DSC 0.99, JM 0.98, HD 0.06, and PAD 0.01. And the lumen also shows excellent performance with DSC 0.98, JM 0.97, HD 0.08, and PAD 0.01. In addition, the proposed model was compared with ground-truth for Thrombus, and showed high values of 0.88 for DSC, 0.83 for JM, 0.21 for HD, and 0.09 for PAD. Conclusion. An advanced algorithm can accurately detect the vessel-plaque interface and estimated stent length of coronary artery disease with high reproducibility. F

Conventional computed tomography-magnetic resonance imaging (CT-MRI) fusion for head and neck radiotherapy is often limited by registration uncertainty from inconsistent positioning and immobilization between diagnostic MRI and CT simulation. This study evaluates the practicality of using MRI positioning plates for head and neck tumors through image registration. A total of 35 patients with head and neck tumors were retrospectively included. Two sets of MRI data were obtained, with and without the positioning plate. These scans were fused with simulated CT images using either rigid registration (RIR) or deformable registration, generating four image combinations: CT/MRI positioning plate_RIR, CT/MRI positioning plate_DIR, CT/MRI_RIR, and CT/MRI_DIR (referred to as SETS1-4). Gross tumor volume (GTV) and organs at risk (OARs) were delineated on each image set. GTVs, OARs, and dose distributions from SET1 were used as reference values, and the overlap of GTV with OAR and dose variations were compared with SETS2-4. Image registration accuracy was assessed using geometric evaluation metrics, including the dice similarity coefficient (DSC), Jaccard similarity coefficient (JSC), Hausdorff distance (HD), and mean distance to agreement (MDA). Compared with the reference registration method, all registration methods for GTVs and most OARs showed statistically significant differences in DSC, JSC, HD, and MDA (p&lt;0.05), except for the brainstem. Notably, compared with MRI images without the positioning plate, the GTV and OAR volumes in SETS1 and SETS2 demonstrated high three-dimensional overlap (DSC &gt; 0.7) and close margin agreement (MDA &lt; 2 mm). For head and neck tumors, MRI images obtained with the positioning plate in the treatment position showed substantial geometric similarity to simulated CT scans through RIR. This similarity enables the use of MRI for radiotherapy target delineation, providing a significant advantage for clinical practice in hospitals without MRI simulators.

The Effect of Contouring Variability on Dosimetric Parameters for Brain Metastases Treated With Stereotactic Radiosurgery

Purpose: The Gamma Knife Perfexion uses 192 Co‐60 sources divided into eight sectors. For each treatment isocenter, each sector can be either blocked or collimated using one of three collimator sizes, but the beam‐on time is identical for all sectors. We postulated that independently variable sector weighting might confer dosimetric advantages with regard to critical structure sparing. In this work, we investigate the potential advantages of independently varying the weighting of individual source sectors to create sector modulated Gamma Knife (SMoG) plans. Materials and Methods: We simulated independent sector weighting for a clinical Gamma Knife plan for a trigeminal neuralgia case. We first obtained dose matrices corresponding to the contribution of each sector. The sector weighting was varied in eight discrete levels and an optimum combination determined using an optimization algorithm. The resulting sector modulated Gamma Knife plan was then entered into the Gamma Knife treatment planning system by decomposing the weighted sectors into multiple shots at the same location with different weights and blocking. The process was repeated using four levels of sector weighting. Results: Our results showed that the optimized plan reduced the maximum dose to the organ at risk (the brainstem) from 18.6 Gy to 11.8 Gy (with eight levels) compared to the clinical plan. However, the beam‐on time for delivery increased from 32 minutes to 84 minutes. The SMoG plan with four sector weighting levels reduced the maximum brainstem dose to 12.6 Gy and only increased treatment beam‐on time to 42 minutes. Conclusions: We have demonstrated that independent weighting of the individual sectors in the Gamma Knife Perfexion can improve the quality of treatment plans, although at the cost of longer delivery times.

ObjectiveHigh precision and accuracy are expected in gamma knife radiosurgery treatment. Because of the requirement of clinically applying complex radiation and dose gradients together with a rapid radiation decline, a dedicated quality assurance program is required to maintain the radiation dosimetry and geometric accuracy and to reduce all associated risk factors. This study investigates the validity of Leksell Gamma plan (LGP)10.1.1 system of 5th generation Gamma Knife Perfexion as modified variable ellipsoid modeling technique (VEMT) method.MethodsTo verify LGP10.1.1 system, we compare the treatment plan program system of the Gamma Knife Perfexion, that is, the LGP, with the calculated value of the proposed modified VEMT program. To verify a modified VEMT method, we compare the distributions of the dose of Gamma Knife Perfexion measured by Gafchromic EBT3 and EBT-XD films. For verification, the center of an 80 mm radius solid water phantom is placed in the center of all sectors positioned at 16 mm, 4 mm and 8 mm; that is, the dose distribution is similar to the method used in the x, y, and z directions by the VEMT. The dose distribution in the axial direction is compared and analyzed based on Full-Width-of-Half-Maximum (FWHM) evaluation.ResultsThe dose profile distribution was evaluated by FWHM, and it showed an average difference of 0.104 mm for the LGP value and 0.130 mm for the EBT-XD film.ConclusionThe modified VEMT yielded consistent results in the two processes. The use of the modified VEMT as a verification tool can enable the system to stably test and operate the Gamma Knife Perfexion treatment planning system.

ScLNet: A cornea with scleral lens OCT layers segmentation dataset and new multi-task model

Manual segmentation in lumbar paraspinal muscle studies is time-consuming, subject to variability, and limits comprehensive evaluation. To solve the problems, we aim to develop a deep learning algorithm for the automatic segmentation and multi-parameter quantification of eight paraspinal muscles in lumbar CT. We collected CT images covering the range from the superior endplate of the L1 vertebra to the inferior endplate of the S1 vertebra. The dataset was partitioned into training, validation, and test sets in a ratio of 7:1:2. Six convolutional neural networks were employed to automatically segment the bilateral psoas major, quadratus lumborum, erector spinae, and multifidus across all slices. Model performance was evaluated using the Dice Similarity Coefficient (DSC), Hausdorff Distance (HD), and mean Intersection over Union (mIoU). Muscle parameters including cross-sectional area, muscle volume, fat infiltration, CT density, and paraspinal muscle index were calculated. The Intraclass Correlation Coefficient [ICC(3,1)] was used to assess agreement between model's segmentations and manual ground truth. 100 lumbar CT scans (age, 55.02 ± 16.2; 62 female) were used for model development. The mean DSC across all six models ranged from 0.754 to 0.903. TransUNet demonstrated the best overall (DSC, 0.903; 95% CI: 0.902, 0.913) (HD, 9.934mm; 95% CI: 9.815, 11.902) (mIoU, 0.841; 95% CI: 0.839, 0.850). For individual muscles segmented by TransUNet, the highest DSC was 0.936 (95% CI: 0.934-0.938) for the right multifidus. The mean ICC for all parameters was 0.931. The highest ICC value was 0.999 (p < 0.001). The deep learning tool enables automatic and accurate segmentation of eight lumbar paraspinal muscles. Muscle parameter measurements derived from model's segmentations and manual ground truth exhibit high agreement. Accurate segmentation and quantification of paraspinal muscle parameters facilitates large-scale epidemiological studies of paraspinal muscles and spine-related diseases, overcoming the limitations in manual methods.