Radiological Physics and Technology最新文献

Beam-matched linear accelerators (LA's) require accurate and precise dosimetry for fractionated stereotactic treatment. In this study, the beam data were validated by comparing the three-beam-matched LA's measured data and the vendor reference data. Upon its validation, the accuracy of the volumetric dose delivery for eighty patient-specific fractionated stereotactic treatment plans was evaluated. Measurements of the percentage depth dose (PDD), beam profiles, output factors (OFs), absolute output, and dynamic multi-leaf collimator (MLC) transmission factors for 6 MV and 10 MV flattening filter (FF) and flattening filter-free (FFF) photon beams were obtained from three-beam-matched LA's. The patient-specific quality assurance evaluation for all eighty plans was performed using PTW Octavius 1000 SRS™ array detectors for two-dimensional (2D) fluence measurement. The following 2D gamma passing criteria were used: 1%/1 mm, 2%/1 mm, 1%/2 mm, 2%/2 mm and 3%/2 mm. In all three LA's, gamma analysis for PDD and profile were above 97% with gamma criteria of 1%/1 mm. The differences OFs, absolute output, and dynamic MLC transmission factors were less than ± 1% of base value. For all eighty cases, the median passing rates on the three LA's were above 76%, 88%, 92%, 96%, and 98% for the above-mentioned gamma criteria of the three LA's. The beam-matched LA's showed good agreement between the measured and treatment planning system (TPS) calculated values for fractionated stereotactic VMAT plans with 6 MV and 10 MV (FF and FFF) photon beams. Patients can be shifted and treated on any beam-matched linac without the need of re-planning.

The aim of this study was to optimise the vessel angle as well as the stack number from the profiles of carbon dioxide digital subtraction angiography (CO₂-DSA) images of a water phantom containing an artificial vessel tilted at different angles which imitate arteries in the body. The artificial vessel was tilted at 0°, 15°, and 30° relative to the horizontal axis with its centre as the pivot point, and CO₂-DSA images were acquired at each vessel tilt angle. The maximum opacity method was used to stack up to four images of the next frame one by one. The signal-to-noise ratio (SNR) was determined from the profile curves. The Wilcoxon rank sum test was used to evaluate whether the profile curve and SNR differed depending on the vessel tilt angle or stack number, and a p-value of less than 0.05 was considered statistically significant. Images acquired at 0° had a significantly lower SNR than images acquired at 15° (p = 0.10). When the vessel angle was 30°, the profile curves were significantly improved (p < 0.05) when two or more images were stacked over the original image. Images with a good SNR were acquired at the vessel tilt angle of 15°, and the shape of the profile curve was improved when two or more images were stacked on the original image. This study demonstrates that the quality of images acquired using CO₂-DSA can be significantly improved through parameter optimisation for image acquisition and post-processing.

This study aimed to develop a novel internal target volume (ITV) definition for respiratory motion targets, considering target motion velocity and time. The proposed ITV was evaluated in respiratory-gated radiotherapy. An ITV modified with target motion velocity and time (ITVvt) was defined as an ITV that includes a target motion based on target motion velocity and time. The target motion velocity was calculated using four-dimensional computed tomography (4DCT) images. The ITVvts were created from phantom and clinical 4DCT images. The phantom 4DCT images were acquired using a solid phantom that moved with a sinusoidal waveform (peak-to-peak amplitudes of 10 and 20 mm and cycles of 2-6 s). The clinical 4DCT images were obtained from eight lung cancer cases. In respiratory-gated radiotherapy, the ITVvt was compared with conventional ITVs for beam times of 0.5-2 s within the gating window. The conventional ITV was created by adding a uniform margin as the maximum motion within the gating window. In the phantom images, the maximum volume difference between the ITVvt and conventional ITV was -81.9%. In the clinical images, the maximum volume difference was -53.6%. Shorter respiratory cycles and longer BTs resulted in smaller ITVvt compared with the conventional ITV. Therefore, the proposed ITVvt plan could be used to reduce treatment volumes and doses to normal tissues.

Urinary toxicities are one of the serious complications of radiotherapy for prostate cancer, and dose-volume histogram of prostatic urethra has been associated with such toxicities in previous reports. Previous research has focused on estimating the prostatic urethra, which is difficult to delineate in CT images; however, these studies, which are limited in number, mainly focused on cases undergoing brachytherapy uses low-dose-rate sources and do not involve external beam radiation therapy (EBRT). In this study, we aimed to develop a deep learning-based method of determining the position of the prostatic urethra in patients eligible for EBRT. We used contour data from 430 patients with localized prostate cancer. In all cases, a urethral catheter was placed when planning CT to identify the prostatic urethra. We used 2D and 3D U-Net segmentation models. The input images included the bladder and prostate, while the output images focused on the prostatic urethra. The 2D model determined the prostate's position based on results from both coronal and sagittal directions. Evaluation metrics included the average distance between centerlines. The average centerline distances for the 2D and 3D models were 2.07 ± 0.87 mm and 2.05 ± 0.92 mm, respectively. Increasing the number of cases while maintaining equivalent accuracy as we did in this study suggests the potential for high generalization performance and the feasibility of using deep learning technology for estimating the position of the prostatic urethra.

We aimed to evaluate the accuracy and repeatability of the T1, T2*, and proton density (PD) values obtained by quantitative parameter mapping (QPM) using the ISMRM/NIST MRI system phantom and compared them with computer simulations. We compared the relaxation times and PD obtained through QPM with the reference values of the ISMRM/NIST MRI system phantom and conventional methods. Furthermore, we evaluated the presence or absence of influences other than noise in T1 and T2* values obtained by QPM by comparing the obtained coefficient of variation (CV) with simulation results. The T1, T2*, and PD values by QPM showed a strong correlation with the measured values and the referenced values. The simulated CVs of QPM calculated for each sphere showed similar trends to those of the actual scans.

Deep-learning-based methods can improve robustness against individual variations in computed tomography (CT) images of the sternocleidomastoid muscle, which is a challenge when using conventional methods based on probabilistic atlases are used for automatic segmentation. Thus, this study proposes a novel multiclass learning approach for the joint segmentation of the sternocleidomastoid and skeletal muscles in CT images, and it employs a two-dimensional U-Net architecture. The proposed method concurrently learns and segmented segments the sternocleidomastoid muscle and the entire skeletal musculature. Consequently, three-dimensional segmentation results are generated for both muscle groups. Experiments conducted on a dataset of 30 body CT images demonstrated segmentation accuracies of 82.94% and 92.73% for the sternocleidomastoid muscle and entire skeletal muscle compartment, respectively. These results outperformed those of conventional methods, such as the single-region learning of a target muscle and multiclass learning of specific muscle pairs. Moreover, the multiclass learning paradigm facilitated a robust segmentation performance regardless of the input image range. This highlights the method's potential for cases that present muscle atrophy or reduced muscle strength. The proposed method exhibits promising capabilities for the high-accuracy joint segmentation of the sternocleidomastoid and skeletal muscles and is effective in recognizing skeletal muscles, thus, it holds promise for integration into computer-aided diagnostic systems for comprehensive musculoskeletal analysis. These findings are expected to enhance medical image analysis techniques and their applications in clinical decision support systems.

The advancement of irradiation technology has increased the demand for quality control of radiation therapy equipment. Consequently, the number of quality control items and required personnel have also increased. However, differences in the proportion of qualified personnel to irradiation techniques have caused bias in quality control systems among institutions. To standardize the quality across institutions, researchers should conduct mutual quality control by analyzing the quality control data of one institution at another institution and comparing the results with those of their own institutions. This study uses failure mode and effects analysis (FMEA) to identify potential risks in 12 radiation therapy institutions, compares the results before and after implementation of mutual quality control, and examines the utility of mutual quality control in risk reduction. Furthermore, a cost-effectiveness factor is introduced into FMEA to evaluate the utility of mutual quality control.

Uncertainties in the steps of external beam radiotherapy (EBRT) affect patient outcomes. However, few studies have investigated major contributors to these uncertainties. This study investigated factors contributing to reducing uncertainty in delivering a dose to a target volume. The EBRT process was classified into four steps: reference dosimetry, relative dosimetry [percentage depth doses (PDDs) and off-center ratios (OCRs)], dose calculations (PDDs and OCRs in a virtual water phantom), and patient setup using an image-guided radiation therapy system. We evaluated the uncertainties for these steps in conventionally fractionated EBRT for intracranial disease using 4-, 6-, and 10-MV flattened photon beams generated from clinical linear accelerators following the Guide to the Expression of Uncertainty in Measurement and an uncertainty evaluation method with uncorrected deflection. The following were the major contributors to these uncertainties: beam quality conversion factors for reference dosimetry; charge measurements, chamber depth, source-to-surface distance, water evaporation, and field size for relative dosimetry; dose calculation accuracy for the dose calculations; image registration, radiation-imaging isocenter coincidence, variation in radiation isocenter due to gantry and couch rotation, and intrafractional motion for the patient setup. Among the four steps, the relative dosimetry and dose calculation (namely, both penumbral OCRs) steps involved an uncertainty of more than 5% with a coverage factor of 1. In the EBRT process evaluated herein, the uncertainties in the relative dosimetry and dose calculations must be reduced.

While layer-stacking irradiation provides a conformal dose distribution, it is vulnerable to respiratory motion. Considering that the motion tolerance has not yet been demonstrated, this study aimed to determine the tolerance level for the amount of target motion. Dose distributions considering motion were simulated for a numerical water phantom using in-house software. Comparisons with measured and simulated physical dose distributions confirmed the validity of the simulation, with gamma analysis showing almost 90% or greater agreement under all conditions with a criterion of 3%/3 mm. The variation in physical dose from static conditions followed a similar trend. Based on the evaluation of the simulated clinical dose uniformity, motion tolerance was derived. The acceptable motion amounts in the lateral direction were 11 mm in respiratory-ungated condition and at least 20 mm with 30% lateral gating at 4 Gy (RBE). In the longitudinal (beam upstream) direction, the acceptable target motion amounts were 3 mm without gating and 6 mm with gating. These results employed worst-case scenarios considering multiple respiratory cycles. In both lateral and longitudinal directions, the motion amounts of 3 mm for non-gating and 5 mm for gating were acceptable. The acceptable target motion amounts improved by 1-9 mm with gating and increased prescribed doses. The dose uniformity and motion tolerance under multiple conditions, although based on a simple system, may be useful for treatment involving target motion in layer-stacking irradiation.

Recently, a novel wireless flat-panel detector with auto-exposure control has become available. This study aimed to elucidate the potential advantages of the new detector over conventional detectors through a comprehensive analysis of the physical image quality characteristics. Measurements were conducted on two models: new (720C) and conventional (710C) versions; this assessment was performed by assuming the beam quality for bedside chest radiography, utilizing a portable device for X-ray exposure. The detective quantum efficiency (DQE) was computed based on the presampled modulation transfer function (MTF) and normalized noise power spectrum. The validity of the DQE results was verified through the visualization of the analog blurring components and a detailed analysis of the noise components. The spatial frequency at which the presampled MTF value reached 10% was 5.2 cycles/mm for 720C and 3.9 cycles/mm for 710C. The full width at half-maximum of the spatial spreading of analog components was estimated at 0.09 mm for 720C and 0.14 mm for 710C by the visualization. Regarding the DQE, 720C was superior under low-dose conditions despite no significant differences being observed under high-dose conditions. The new detector demonstrated superior resolution characteristics compared with the conventional detector and an improvement in the DQE under low-dose conditions. However, similar to the conventional detector, a significant dose dependence caused by a structural factor was confirmed for the DQE. These results suggest the existence of an appropriate dose range for maximizing detector performance and provide insights crucial for optimization tasks in the X-ray imaging.