Radiological Physics and Technology最新文献

Purpose: To investigate the dosimetric characteristics of intracavitary/interstitial brachytherapy (IC/ISBT) plans created via the hybrid inverse planning optimization (HIPO) algorithm with the dwell time Lock function.

Materials and methods: Sixteen patients with locally advanced cervical cancer treated with high-dose-rate IC/ISBT were evaluated. Based on the clinical plan data, five plans were retrospectively created: Manchester-based HIPO for needles, HIPO for all applicators, HIPO for needles after HIPO for tandem/ovoid, HIPO for ovoid after HIPO for tandem/needle, and HIPO for ovoid/needle after HIPO for tandem. The target coverage, organs at risk (OARs) doses, therapeutic ratios, and the dwell time contributions of the needles were analyzed to evaluate the plan quality. Dice similarity coefficients (DSCs) between clinical plan and each created plan were calculated to evaluate the similarity of the shape of the dose distribution.

Results: All plans created using HIPO had a sufficient target coverage, while the OAR dose for the Manchester-based HIPO plans was considerably higher than the other plans. The plan with HIPO for all applicators and with HIPO for the ovoid applicator after HIPO for tandem/needles had comparable or superior therapeutic ratios than those of the clinical plan while the dwell time contributions of the needle were much larger. For DSCs, an intermediate to low correlation was observed between the clinical plan and all HIPO plans.

Conclusions: The HIPO algorithm could create high-quality IC/ISBT plans, although the dosimetric consequences were affected by the locking function.

This study aimed to propose a deep learning-based segmentation framework to delineate prostate lesions across multiple MRI acquisitions and derived parametric maps, including apparent diffusion coefficient (ADC) map, diffusion kurtosis imaging (DKI)-derived parameter maps (D map and K map), T2-weighted imaging (T2WI), and T2*-weighted imaging-derived parameter maps (T2* map and R2* map). Then, a comparison was conducted among the model's segmentation performance across MRI-derived images to identify those that provide the most discriminative information for accurate lesion identification. 51 patients underwent multiparametric MRI sequences, which included T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and T2*-weighted images. Three expert radiologists conducted manual lesion annotations. All images were preprocessed, labeled, and augmented before training the U-Net++ model. The segmentation model's performance was evaluated using Dice similarity coefficient, Intersection over Union (IoU), sensitivity, and specificity metrics. The IoU values for the ADC map, D map, K map, T2WI, T2* map, and R2* map were 0.8907, 0.8559, 0.9504, 0.9250, 0.9441, and 0.8781, respectively. The corresponding Dice coefficient scores were 0.9416, 0.9211, 0.9744, 0.9604, 0.9709, and 0.9342. These results indicate a significant degree of overlap between the predicted and ground truth segmentation masks. These findings emphasize the complementary value of combining optimized deep learning architectures with advanced MRI-derived images, which could enhance diagnostic precision and facilitate more informed clinical decision-making.

Intraprocedural visualization of the iceball boundary is often limited at the fat-ice interface, where frozen fat-despite increased computed tomography (CT) values-remains within the negative range, thus yielding limited contrast with non-frozen fat. This limitation is relevant in CT-guided renal cryoablation involving perirenal fat. We evaluated a stepwise CT post-processing method of subtraction and scaled addition with probabilistically adjusted thresholding, using an in situ fat-muscle phantom. This two-step process involved fixed zero-threshold subtraction (Step 1: post-freezing image minus pre-freezing image) and kernel density estimation-based threshold subtraction (Step 2: Step 1 output minus post-freezing image), based on pixel-wise fat-attenuation distributions. Contrast-to-noise ratio improved in both fat and non-fat tissues. In fat tissue, boundary contrast selectively increased by reducing CT values in non-frozen regions, whereas in non-fat tissue, by reducing them in frozen regions. Iceball boundaries aligned with magnetic resonance imaging. This approach may improve iceball demarcation and warrants validation in clinical practice.

Abdominal computed tomography (CT) is normally performed with patients raising their arms over abdominal region to prevent arm-induced artifacts that degrade image quality. This study aimed to evaluate the effects of deep learning-based image reconstruction (DLIR) on arm-induced artifacts and image quality in abdominal CT with arms-down positioning, compared to adaptive statistical iterative reconstruction-Veo (ASIR-V) and filtered-backprojection (FBP). A liver nodule phantom with arms from a PBU-60 phantom was scanned in three arms-down positions: alongside the torso, across the abdomen, and crossed over the pelvis. Abdominal CT images of 10 patients in arms-alongside-torso position were also included. Images were reconstructed using DLIRs (L-low, M-medium, and H-high), ASIR-Vs (50% and 100%), and FBP. Phantom images were assessed for artifact strength (location parameter of the Gumbel distribution and standard deviation), signal-to-noise ratio, and contrast-to-noise ratio. Two radiologists qualitatively evaluated patient images for noise, artifacts, sharpness, and overall quality. DLIR-H significantly reduced streak artifacts by 37% in location parameters and by 43% in SD, while improving SNR by 28% and CNR by 29% compared to ASIR-V50%. DLIR-M performed significantly better than ASIR-V50% in all quantitative metrics, except in the arms-alongside-torso position. FBP performed worst, although sharpness was comparable. DLIR-H received the best qualitative scores (low noise and artifacts, minimal blurring, and excellent overall image quality), although ASIR-V100% had lower subjective noise. DLIR outperformed ASIR-V and FBP in arm-induced artifact reduction and image quality and is a preferable reconstruction method for arms-down abdominal CT.