Journal of Applied Clinical Medical Physics最新文献

Background: Formulating a clinically acceptable plan within the time-constrained clinical setting of brachytherapy poses challenges to clinicians. Deep learning based dose prediction methods have shown favorable solutions for enhancing efficiency, but development has primarily been on external beam radiation therapy. Thus, there is a need for translation to brachytherapy.

Purpose: This study proposes a dose prediction model utilizing an attention-gating mechanism and a 3D UNET for cervical cancer high-dose-rate intracavitary brachytherapy treatment planning with tandem-and-ovoid/ring applicators.

Methods: A multi-institutional data set consisting of 77 retrospective clinical brachytherapy plans was utilized in this study. The data were preprocessed and augmented to increase the number of plans to 252. A 3D UNET architecture with attention gates was constructed and trained for mapping the contour information to dose distribution. The trained model was evaluated on a testing data set using various metrics, including dose statistics and dose-volume indices. We also trained a baseline UNET model for a fair comparison.

Results: The attention-gated 3D UNET model exhibited competitive accuracy in predicting dose distributions similar to the ground truth. The average values of the mean absolute errors were 0.46 ± 11.71 Gy (vs. 0.47 ± 9.16 Gy for a baseline UNET) in CTV_HR, 0.55 ± 0.67 Gy (vs. 0.70 ± 1.54 Gy for a baseline UNET) in bladder, 0.42 ± 0.46 Gy (vs. 0.49 ± 1.34 Gy for a baseline UNET) in rectum, and 0.31 ± 0.65 Gy (vs. 0.20 ± 3.76 Gy for a baseline UNET) in sigmoid. Our results showed that the mean individual differences in ΔD_2cc for bladder, rectum, and sigmoid were 0.38 ± 1.19 (p = 0.50), 0.43 ± 0.71 (p = 0.41), and -0.47 ± 0.79 (p = 0.30) Gy, respectively. Similarly, the mean individual differences in ΔD_1cc for bladder, rectum, and sigmoid were 0.09 ± 1.21 (p = 0.36), 0.20 ± 0.95 (p = 0.24), and -0.21 ± 0.59 (p = 0.30) Gy. The mean individual differences for ΔD₉₀, ΔV_100%, ΔV_150%, and ΔV_200% of the CTV_HR were -0.45 ± 2.42 (p = 0.26) Gy, 0.55 ± 9.42% (p = 0.78), 0.82 ± 4.21% (p = 0.81), and -0.80 ± 10.48% (p = 0.36), respectively. The model requires less than 5 s to predict a full 3D dose distribution for a new patient plan.

Conclusion: Attention-gated 3D UNET revealed a promising capability in predicting voxel-wise dose distributions compared to 3D UNET. This model could be deployed for clinical use to predict 3D dose distributions for near real-time decision-making before planning, quality assurance, and guiding future automated planning, making the current workflow more efficient.

Objective: The routine patient arm position differs between coronary CT angiography (CTA) and craniocervical CTA protocols. To investigate the clinical feasibility of supported bridge position (SBP) in combined coronary and craniocervical CTA.

Methods: Prospective enrollment included patients with suspected coronary artery disease (CAD) or craniocervical artery disease (CCAD) from February 2022 to November 2022. Patients were divided into three groups: coronary or craniocervical CTA according to CAD or CCAD using standard position (group 1), combined CTA with naturally arm-down position (group 2) and SBP (group 3). Statistical analysis of objective image quality, such as noise and contrast-to-noise ratio (CNR), subjective image quality, patient position and radiation dose was performed among the three groups.

Results: Two hundred and one patients (median age, 67 years; 138 men) were included. In terms of CNR for cardiac segment, group 1 and group 3 had no statistical difference, both significantly higher than group 2 (group 1, 12.56 ± 2.05; group 2, 10.4 ± 2.43; group 3, 11.94 ± 2.22; P < 0.05). Subjective image evaluation revealed no statistically significant differences among the three groups of coronary arteries (P > 0.05). Additionally, the lateral project value of scout images at the heart level indicated a significant difference (119.48 ± 12.19, 182.34 ± 25.09, and 140.58 ± 19.68 of patients, for group 1, group 2, and group 3, respectively, P < 0.05). No statistical differences were observed in ${\mathrm{CTDI}}_{\mathrm{vol}}$ between group 1 and group 3 (cardiac scan, 15.77 [15.07-16.37] mGy vs. 14.88 [12.19-18.81] mGy; craniocervical scan, 7.85 [7.69-8.01] mGy vs. 7.88 [7.88-7.89] mGy; all P > 0.05). However, group 2 had a higher dose (19.54 [16.86-22.85] mGy and 10.87 [10.86-10.87] mGy, for cardiac and craniocervical scans, respectively).

Conclusions: In comparison with a naturally arm-down position, SBP, which aligns the humerus bones with the spinal column, can provide diagnostic image quality at routine dose level of standard position CTA.

Objective: We investigated the feasibility of deep learning-based ultra-low dose kV-fan-beam computed tomography (kV-FBCT) image enhancement algorithm for clinical application in abdominal and pelvic tumor radiotherapy.

Methods: A total of 76 patients of abdominal and pelvic tumors were prospectively selected. The Catphan504 was acquired with the same conditions as the standard phantom test set. We used a CycleGAN-based model for image enhancement. Normal dose CT (NDCT), ultra-low dose CT (LDCT) and deep learning enhanced CT (DLR) were evaluated by subjective and objective analyses in terms of imaging quality, HU accuracy, and image signal-to-noise ratio (SNR).

Results: The image noise of DLR was significantly reduced, and the contrast-to-noise ratio (CNR) was significantly improved compared to the LDCT. The most significant improvement was the acrylic which represented soft tissue in CNR from 1.89 to 3.37, improving by 76%, nearly approaching the NDCT, and in low-density resolution from 7.64 to 12.6, improving by 64%. The spatial frequencies of MTF10 and MTF50 in DLR were 4.28 and 2.35 cycles/mm in DLR, respectively, which are higher than LDCT 3.87 and 2.12 cycles/mm, and even slightly higher than NDCT 4.15 and 2.28 cycles/mm. The accuracy and stability of HU values of DLR were similar to NDCT. The image quality evaluation of the two doctors agreed well with DLR and NDCT. A two-by-two comparison between groups showed that the differences in image scores of LDCT compared with NDCT and DLR were all statistically significant (p < 0.05), and the subjective scores of DLR were close to NDCT.

Conclusion: The image quality of DLR was close to NDCT with reduced radiation dose, which can fully meet the needs of conventional image-guided adaptive radiotherapy (ART) and achieve the quality requirements of clinical radiotherapy. The proposed method provided a technical basis for LDCT-guided ART.

Purpose: MRI-guided adaptive radiotherapy can directly monitor the anatomical positioning of the intended target during treatment with no additional imaging dose. Elekta has recently released its comprehensive motion management (CMM) solution that enables automatic radiation beam-gating on the Unity MR-Linac. Easily visualized targets that are distinct from the surrounding anatomy can be used to drive automatic gating decisions from the MRI cine imaging. However, poorly visualized targets can compromise the tracking and gating capabilities and may require surrogate tracking structures. This work presents strategies to generate robust tracking surrogates for a variety of treatment sites, enabling a wider application of CMM.

Methods: Surrogate tracking strategies were developed from a cohort of patients treated using the CMM system on the Unity MR-Linac for treatment sites of the lung, pancreas, liver, and prostate. These sites posed challenging visualization or tracking of the primary target thereby compromising the tracking accuracy. Surrogate structures were developed using site-specific strategies to improve the imaging textured detail within the tracking volume while avoiding the dynamic overwhelming hypo- or hyper-intense anatomical structures. These surrogate volumes were applied within the anatomical positioning monitoring system as a proxy that drove the CMM gating decisions on the treatment unit.

Results: Robust site-specific surrogate structures were developed. Surrogate tracking structures for centrally located thoracic targets were created by expanding the target peripherally away from the heart and great vessels and into the lung. Pancreas surrogates required a vertically expanded column intersecting with the inferior liver edge. For the liver and prostate, surrogate structures consisted of a uniform expansion of the target, with liver surrogates intersecting the proximal liver edge or diaphragm while avoiding nearby ribs.

Conclusion: These surrogate strategies have enabled the gating of complex moving targets among different treatment sites at our institution.

Purpose: This study investigates potential failure modes and conducts failure mode and effects analysis (FMEA) and fault tree analysis (FTA) on the administration of ${}^{177}\mathrm{Lu}$ DOTATATE (LUTATHERA) and ${}^{177}\mathrm{Lu}$ PSMA-617 (PLUVICTO). The quality management (QM) process in radiopharmaceutical therapies (RPTs) requires collaboration between nuclear medicine (NM) and radiation oncology (RO) departments. As part of a multi-institutional study, we surveyed various departments to identify and analyze failure modes, leading to a proposed comprehensive QM program. RPT teams in RO or NM clinics can benefit from this study by continually improving their practice.

Methods: We reviewed the literature to investigate the administration of Pluvicto and Lutathera, focusing on prospective procedural failures and potential failure modes (PFMs) and their outcomes. We distributed an FMEA survey to multiple experienced centers in ${}^{177}\mathrm{Lu}$ -based RPTs and calculated risk priority number (RPN) for various PFM. We conducted an FTA using this information to pinpoint the root causes of potential failures.

Results: The findings from the literature review and survey responses on the prospective study have identified several critical areas at risk of failure. These areas include non-optimized treatment delivery, inadequate patient monitoring, and lack of safety training, leading to radiation contamination from the dose excreted by the patients after treatment administration. A segmented FTA was created based on the FMEA results, focusing on radiation contamination with a high RPN value.

Conclusion: By identifying the root causes of failures and proposing targeted improvements to the existing QM measures, this analysis enhances safety in treatment delivery of ${}^{177}\mathrm{Lu}$ -based RPTs. Given the limited number of prospective risk analysis studies in RPTs, our research addresses the necessity for more such studies and recommends methods to apply this study to other RPTs.