Cross-vendor multiparametric mapping of the human brain using 3D-QALAS: A multicenter and multivendor study.

PURPOSE: To evaluate a vendor-agnostic multiparametric mapping scheme based on 3D quantification using an interleaved Look-Locker acquisition sequence with a T2 preparation pulse (3D-QALAS) for whole-brain T1, T2, and proton density (PD) mapping. METHODS: This prospective, multi-institutional study was conducted between September 2021 and February 2022 using five different 3T systems from four prominent MRI vendors. The accuracy of this technique was evaluated using a standardized MRI system phantom. Intra-scanner repeatability and inter-vendor reproducibility of T1, T2, and PD values were evaluated in 10 healthy volunteers (6 men; mean age ± SD, 28.0 ± 5.6 y) who underwent scan-rescan sessions on each scanner (total scans = 100). To evaluate the feasibility of 3D-QALAS, nine patients with multiple sclerosis (nine women; mean age ± SD, 48.2 ± 11.5 y) underwent imaging examination on two 3T MRI systems from different manufacturers. RESULTS: Quantitative maps obtained with 3D-QALAS showed high linearity (R2 = 0.998 and 0.998 for T1 and T2, respectively) with respect to reference measurements. The mean intra-scanner coefficients of variation for each scanner and structure ranged from 0.4% to 2.6%. The mean structure-wise test-retest repeatabilities were 1.6%, 1.1%, and 0.7% for T1, T2, and PD, respectively. Overall, high inter-vendor reproducibility was observed for all parameter maps and all structure measurements, including white matter lesions in patients with multiple sclerosis. CONCLUSION: The vendor-agnostic multiparametric mapping technique 3D-QALAS provided reproducible measurements of T1, T2, and PD for human tissues within a typical physiological range using 3T scanners from four different MRI manufacturers.

Development of a rotational set-up correction device for stereotactic head radiation therapy: A performance evaluation.

We developed a new head supporting device to provide accurate correction of rotational setup during image-guided radiation therapy (IGRT), evaluating its correction performance and the efficacy of dose distribution in stereotactic radiotherapy (SRT) using a helical tomotherapy (HT) system. The accuracy of rotational motion was measured using an electronic inclinometer; we compared device angles and measurement values from 0.0° to 3.0°. The correction accuracy was investigated based on the distance between rotational centers in the device and on megavoltage computed tomography (MVCT); the correction values were compared using distances in the range of 0.0-9.0 cm using a head phantom with a rotational error of 1.5°. For an SRT with a simultaneous integrated boost plan and a rotational error of 3.0° in yaw angle using a head phantom, and for a single-isocenter SRT for multiple brain metastases in the data of three patients, dosimetric efficacy of the HT unit was evaluated for calculated dose distributions with MVCT after rotational correction. This device can correct pitch and yaw angles within 0.3° and can be corrected to within 0.5° for each rotational angle according to the result of MVCT correction regardless of the rotational center position. In the head phantom study, the device had a beneficial impact on rotational correction; D99% for the target improved by approximately 10% with rotational correction. Using patient data with the device, the mean difference based on the treatment planning data was 0.3% for D99% and -0.1% for coverage index to the target. Our rotational setup correction device has high efficacy, and can be used for IGRT.

[Detection method for blurred regions in radiographs].

In this paper, we propose a detection method for blurred regions in radiographs. The method involves edge detection using a Sobel filter, manually determining the region of interest (ROI), feature calculation, and classification using a support vector machine. We applied our method to 14 phantom images (7 normal images, 7 blurred images) and 14 clinical images (12 normal images, 2 blurred images). As a result, the average classification accuracies of ROIs with blurring and ROIs without blurring were 98% and 90% for phantom images and clinical images, respectively.