Journal of Applied Clinical Medical Physics最新文献

Purpose: The unique design of the MR-linac may restrict the use of effective immobilization devices, resulting in significant patient setup variations (PSVs). The purpose of this study is to analyze the PSVs on the Elekta Unity system and investigate their impact on adaptive planning.

Methods: The PSVs for 10 brain, 10 pancreas, five prostate, and five rectum patients previously treated on Elekta Unity were analyzed. The five prostate and five pancreas plans were selected to investigate the impact of PSVs on adaptive planning. The reference scans were shifted by 1, 2, and 3 cm in the left-right (LR) and superior-inferior (SI) directions to simulate PSVs. Both the adaptive-to-position (ATP) and adaptive-to-shape (ATS) workflows were executed. The adaptive planning time, number of monitor units (MUs), and dosimetric metrics quantifying target coverage and organ-at-risks (OARs) sparing were compared.

Results: For brain treatments, the average/maximum PSVs were -0.2 ± 0.3 cm/0.8 cm (LR), 0.3 ± 0.7 cm/1.8 cm (SI), and 0.8 ± 0.7 cm/1.8 cm in the anterior-posterior (AP) direction. For pancreas treatments, the PSVs are -0.1 ± 1.0 cm/3.8 cm (LR), -0.1 ± 0.8 cm/3.5 cm (SI), and 0.3 ± 0.3 cm/1.3 cm (AP). Pelvis treatments had similar PSVs as pancreas treatments. The ATS workflow took two to three times longer than the ATP workflow. The only trend observed was that the plan MUs increased slightly (< 10%) with PSVs in the ATP workflow for prostate patients. Both workflows effectively reproduced target coverage and OAR sparing, regardless of the magnitude of the PSVs.

Conclusions: Significant PSVs were observed on Elekta Unity due to suboptimal patient immobilization. Using prostate and pancreas treatments as examples, we demonstrated that adaptive planning can effectively accommodate such PSVs. Nevertheless, efforts should be made to minimize PSVs-particularly rotations-to mitigate intra-fraction motion and reduce treatment time.

Introduction: The American Association of Physicists in Medicine (AAPM) recently shared results and recommendations from its first Equity, Diversity, and Inclusion (EDI) Climate Survey, which was designed to assess the climate at the workplace, the AAPM organization, and the AAPM regional chapter level. This work further explores the status of EDI at the regional chapter level.

Methods: AAPM's EDI Survey was distributed to 5500 members and had a response rate of 25%. In the survey, three open-ended comment boxes were provided for feedback, including one for regional AAPM members. Sixty-four percent of respondents indicated they were part of a regional chapter, and 6% provided written responses to the regional chapter question. Responses were analyzed using a mixed methods approach with an exploratory sequential design. Two phases were conducted; the first relied on a Grounded Theory quantitative systemic approach, and the second applied qualitative analysis. Chapter member demographic data were collected to support findings.

Results: Survey respondents provided open comments and feedback on their regional chapter's climate. Data are summarized as five themes: positive experiences, negative experiences, challenges within chapters, diversity and inclusion, and changes observed. Experiences of regional chapters were rated positively by 75% of respondents. Respondents found their chapters were welcoming, and some noted their great chapter leadership. A number of incidents of sexual harassment, bullying, and discrimination incidences were also shared. Other respondents observed exclusion based on their gender, race, highest degree, and medical physics specialty. Chapter leadership data aligned with these claims, with most leaders to-date being white males, doctoral degree holders, and/or specializing in radiation therapy.

Conclusion: AAPM chapters provide rewarding professional opportunities. This study has highlighted positive and negative experiences reported by its members. The major themes identified can guide chapter leaders to continue to cultivate welcoming communities for regional AAPM members.

This study aimed to compare the treatment plans of x-ray SBRT and scanning carbon ion radiation therapy (CIRT) for localized lung tumors, and to evaluate the dose dependence of tumor size tumor-to-heart distance. For phantom verification, we used a chest phantom with a spherical simulated tumor. Treatment plans for 3-dimensional conformal radiation therapy (3D-CRT), volumetric modulated arc therapy (VMAT), and CIRT were created. GTVs were created in sizes ranging from 0.5 to 5 cm in diameter, and the dependence of the lung dose on GTV diameter was evaluated for each treatment plan. For patient validation, 30 cases of localized lung tumors were analyzed. 3D-CRT, VMAT, and CIRT treatment plans were developed, and DVH parameters were evaluated for each GTV size and GTV-to-heart distance. In both phantom and patient validations, the OAR doses were the lowest for CIRT. The lung dose increased with increasing GTV diameter for all three treatment plans. CIRT had the smallest ratio of lung dose increase to GTV diameter increase among the three treatment plans. Heart dose in CIRT was independent of GTV size and GTV-to-heart distance Conclusions: The results of the present study suggested that the use of scanning CIRT can reduce the OAR dose while guaranteeing the tumor dose compared to x-ray SBRT. In addition, it was suggested that CIRT can treat patients with large GTV sizes while maintaining low lung and heart dose.

Background: Abdominal multi-organ segmentation remains a challenging task. Semi-supervised domain adaptation (SSDA) has emerged as an innovative solution. However, SSDA frameworks based on UNet struggle to capture multi-scale and global information.

Purpose: Our work aimed to propose a novel SSDA method to achieve more accurate abdominal multi-organ segmentation with limited labeled target domain data, which has a superior ability to capture the multi-scale features and integrate local and global information effectively.

Methods: The proposed network is based on UNet. In the encoder part, a scale-aware with domain-specific batch normalization (SAD) module is integrated to adaptively extract multi-scale features and to get better generalization across source and target domains. In the bottleneck part, a global-local fusion (GLF) module is utilized for capturing and integrating both local and global information. They are integrated into the framework of self-ensembling mean-teacher (SE-MT) to enhance the model's capability to learn common features across source and target domains.

Results: To validate the performance of the proposed model, we evaluated it on the public CHAOS and BTCV datasets. For CHAOS, the proposed method obtains an average DSC of 88.97% and ASD of 1.12 mm with only 20% labeled target data. For BTCV, it achieves an average DSC of 88.95% and ASD of 1.13 mm with 20% labeled target data. Compared with the state-of-the-art methods, DSC and ASD increased by at least 0.72% and 0.33 mm on CHAOS, 1.29% and 0.06 mm on BTCV, respectively. Ablation studies were also conducted to verify the contribution of each component of the model. The proposed method achieves a DSC improvement of 3.17% over the baseline with 20% labeled target data.

Conclusion: The proposed SSDA method for abdominal multi-organ segmentation has a powerful ability to extract multi-scale and more global features, significantly improving segmentation accuracy and robustness.

Disability and accessibility remain under-addressed topics despite the increasing prevalence of disabled people in the workforce. In the field of medical physics, there is growing evidence that the proportion of people with one or more disabilities mirrors that of the US population. Addressing disability and accessibility is a crucial facet of the American Association of Physicists in Medicine's 2018 strategic goal to "champion equity, diversity, and inclusion in the field of medical physics." This review aims to provide guidance on disability-related topics in the context of the medical physics profession. An overview of current knowledge and recommendations is provided on essential topics such as how to comply with federal law, handle accommodation requests, and discuss disability using appropriate language. To that end, background information such as definitions, models, and classifications of disability is included. Beyond the essentials, this review also applies disability-related concepts to improve overall efficiency and productivity, attract diverse talent, and demonstrate leadership as an individual or organization.

Purpose: Intra- and inter-observer contour uncertainty is a continuous challenge in treatment planning for radiotherapy. Our proposed solution to address this challenge is the use of partial contours for treatment planning, focusing on uninvolved or non-overlapping portions of the organs-at-risk (OARs) with the planning target volume (PTV).

Methods: The partial contours systematically eliminate overlapping regions. The partial contours were evaluated against fully contoured OARs. We incorporated advanced tools like knowledge-based planning (KBP) to create treatment plans and artificial intelligence (AI) to create auto-segmented contours. We developed two models, Rapid Plan (RP) and Rapid Plan partial uninvolved (RP_Part_Un), using 70 previous clinically approved volumetric arc therapy (VMAT) plans each prescribed with 70 Gy/28 fractions. From these models, we created three plans, RP, RP_Part_Un, and MIM AI_Part_Un. In this retrospective study, 60 prostate patients were analyzed using the three plans. For determining OAR sparing, D_max and D_mean along the percent volume receiving a dose over a range (V₁₀ Gy V₇₀ Gy) between each plan were compared. Geometric evaluations, dice similarity coefficient (DSC), and overlay index (OI) between the OAR contours from partial-contoured manual structure sets and partial-contoured AI structure sets were analyzed.

Results: When comparing the DSC and OI for full contours to the partial contours, in both groups, all comparisons were significantly increased for both organs. This indicated the partial contours had a higher degree of concordance. In patients with SpaceOAR, RP_Part_Un plans exhibited significantly reduced bladder D_max and D_mean compared to RP plans, while rectum D_max and D_mean showed no significant differences. For patients without SpaceOAR, RP_Part_Un significantly lowered rectum D_mean. MIM AI_Part_Un plans demonstrated lower rectum D_max in both patient groups.

Conclusions: Partial contours, defined at a specified distance from the PTV, yielded dosimetry comparable to fully contoured plans, highlighting their potential efficacy in treatment planning.

Background: Inverse planning typically utilizes fast, less accurate dose calculation algorithms during the iterative optimization process, thus leading to dose calculation errors (DCEs) and suboptimal plans that often require dose normalization and/or plan re-optimization.

Purpose: A graphic processing unit (GPU) accelerated Fourier transform dose calculation (FTDC) was recently commissioned at our institution during the Eclipse treatment planning system (Varian Medical Systems) v18.0 upgrade. We hypothesize that FTDC could reduce DCEs and planning failure rates (PFRs) compared to its predecessor, multi-resolution dose calculation (MRDC), while improving efficiency through utilization of GPUs.

Methods: Fifty lung SBRT plans were optimized with MRDC and FTDC dose calculation algorithms. Acuros XB (AXB) was then used for final dose calculations. DCEs for target and organ-at-risk (OAR) were calculated as the percent difference between AXB and dose calculated at the final optimization step. Plan quality was assessed using an in-house planning scorecard where PFRs were calculated as the percentage of plans that had a plan score less than 90% with optimal plans scored at 100%.

Results: FTDC showed excellent agreement with AXB in terms of planning target volume (PTV) coverage, as PTV D95% DCE_FTDC averaged 0.8% ± 0.9%, compared to DCE_MRDC's -2.5% ± 3.2%. DCEs for thoracic OARs were reduced with less variation when optimizing with FTDC as compared to MRDC. FTDC had a PFR of 10% (5 out of 50) versus MRDC's 32% (16 out of 50). The subsequent re-optimization rate resulted from a plan normalization of 3% or greater was 4% for FTDC compared to MRDC's 38%. FTDC with GPU acceleration reduced optimization time by 75% on average compared to MRDC without GPU acceleration.

Conclusions: FTDC shows more accurate dose calculation accuracy compared to MRDC. Its use during the optimization process improved planning quality and efficiency assisted with GPUs.

In recent years, complex re-irradiations and stereotactic treatments have triggered the use of non-coplanar treatments for better dose conformality, entailing risks of collision between the machine and the patient, couch, or immobilization device. To ensure the plans deliverability without collisions, time-consuming actions are typically performed, including dry runs, in-room couch rotations, and beam configuration tests during planning. To overcome these challenges, a new tool called MapRT (VisionRT Ltd., London, UK) was developed. MapRT predicts a clearance map based on a patients' 3D model (acquired with dedicated cameras at the CT simulation) and pre-established machine models. This work evaluates the accuracy of MapRT using a 30 × 35 × 40 cm³ phantom and 64 gantry/couch collision coordinates on a Truebeam Linac (Varian, Palo Alto, USA). Collision coordinates were recorded for gantry and couch rotations. The agreement of real collision coordinates and MapRT's predictions was evaluated for different buffer margins around the couch/patient models customizable in MapRT. Results of the first clinical implementation of MapRT were also reported. With no buffer margin, MapRT's predictions and experimental collision coordinates showed small average differences but with large standard deviations for gantry (-0.5°±6.2°) and couch (-0.1°±4.8°) collision coordinates. When excluding the kV imaging components, these values were of -0.8°±3.5° for gantry and 0.4°±4.4° for couch. Finally, a 3 cm buffer margin allows for 100% accurate predictions by MapRT of gantry-to-phantom and gantry-to-couch collisions. Among the ∼900 treatment plans checked with MapRT, 22 collisions could be avoided while another 6 plans still incurred a collision but these are mainly due to users' oversights. MapRT easily predict collisions in complex treatment planning. This work demonstrated its reliability using a 3 cm buffer margin. MapRT is a promising tool for increasing security, time saving and workflow improvement.

Background: With the introduction of a new multi-leaf collimator (MLC) enhanced leaf model (ELM) in the Varian Eclipse™ treatment planning system, there is currently limited data regarding the dosimetric sensitivity to real-world variation in the ELM parameters, and its clinical relevance.

Purpose: To characterize the variation in ELM parameters across a large department with ten linear accelerators and investigate the feasibility of using a single machine-averaged ELM for treatment planning. This could achieve time and resource savings from reduced quality assurance, while allowing easy transfer of patients between machines.

Methods: Clinical plans of a range of sites (head and neck, prostate, breast, lung, and brain), techniques (VMAT, IMRT, SBRT, and SRS), and energies (6 MV, 6 MV FFF, 10 MV, and 10 MV FFF) were recalculated on Varian TrueBeam™ (120 MLC) and Varian EDGE™ (HD120 MLC), with machine-specific ELM beam models, an averaged machine and an outlier machine model. A range of clinically relevant metrics relating to target coverage (e.g. PTV D_98%, D_50%, D_2%) and OAR doses (dosimetric, volumetric, conformity, and gradient indices) were evaluated.

Results: For the target metrics, the maximum percentage deviation from the mean was 0.422%, 0.157%, and 1.956% for the cases of the individual machines, the averaged machine and the outlier machine correspondingly, while the maximum absolute dose differences were 0.28 Gy, 0.07 Gy, and 0.38 Gy. For the OAR metrics, the maximum deviation from the mean was 1.833%, 0.204%, and 5.722% for the individual, averaged, and outlier machines, while the maximum absolute dose differences were 0.41 Gy, 0.10 Gy, and 0.97 Gy.

Conclusions: For machines that are well matched in terms of dosimetry for transmission and sweeping gap fields, the use of an averaged machine model is unlikely to introduce clinically significant dosimetric differences to treatment plans.