Radiological Physics and Technology最新文献

The estimation of peripheral dose (PD) is vital in cancer patients with long life expectancy. Assessment of PD to radiosensitive organs is important to determine the possible risk of late effects. An attempt has been made to assess the peripheral dose using optically stimulated luminescence dosimeter (OSLD) with megavoltage photon beams as a function of field size, depth, energy, and distance from the field edge. The PD measurements were carried out at 13 locations starting from 1.5 cm to 20.8 cm from radiation field edge for three different field sizes at three different depths with 6 and 18 MV photon beams. In addition, the measurements were carried out to analyze the response in PD due to the presence of wedge. The %PD decreases gradually with an increase in distance from the radiation field edge. The %PD at surface for 10 × 10cm² with 6MV photon beams was 6.77 ± 0.32% and 1.0 ± 0.04% at 1.5 cm and 20.8 cm away from field edge. For 20 × 20 cm² field, %PD was found to be much higher at surface than at 5 cm depth for all distances from field edge. This study demonstrates the suitability of OSLD for PD assessment in megavoltage photon beams. The PD increases as field size increases, primarily due to greater amount of out-of-field scatter generated by larger surface area of the collimator defining the larger field size. An enhancement in PD was observed with wedge when the thick end was oriented towards the OSLDs. This study assessed PD that would be a risk factor of the normal tissue complication and secondary cancer induction.

The purpose of this study is to clarify the influence of acquiring medical record information and laboratory data on the sensitivity of detecting imaging findings among Japanese radiological technologists (RTs). RTs were presented with patient's information in three distinct sequences for detecting imaging findings. True positives (TP) were identified and categorized into three groups: Group 1 (image + chief complaint), Group 2 (image + chief complaint + medical record), and Group 3 (image + chief complaint + medical record + laboratory data). Compared with Group 1, Groups 2 and 3 exhibited up to 8.3% and 16.7% higher sensitivity, respectively. Notably, Group 3 demonstrated up to 13.3% higher sensitivity than that of Group 2. Obtaining medical record information and laboratory data improved the sensitivity of imaging findings detected by RTs. This study provides valuable insights for optimizing diagnostic protocols and improving patient outcomes by leveraging supplementary clinical data to augment diagnostic accuracy.

This study evaluates the dosimetric impact of arc simulation angular resolution in VMAT-based Single Isocentre Multiple Target (SIMT) SRS, focusing on their dependence on target size, isocentre distance, number of arcs, and arc type. A phantom study analysed angular resolution (0.5°, 1°, 2°) effects on dosimetric accuracy for PTVs of 0.5 cm, 1 cm, and 2 cm at distances of 2.5 cm, 5 cm, and 7.5 cm from the isocentre using conformal arc and VMAT plans. Clinical validation involved 32 patients with 2-8 brain metastases, comparing plans recalculated at 1° and 2° resolutions. Dosimetric parameters included: D_near-Min, D_near-Max, D_mean, D_median, TV_PIV, CI_Paddick, GI, and Brain-GTV 12 Gy. For the 0.5 cm diameter target located at 7.5 cm distance from isocentre, phantom results showed TV_PIV, D_mean, and GI deviations of 7.91%, 1.8%, and 0.85 for single-conformal arcs, which decreased to 4.84%, 1.3%, and 0.77 with 4-conformal arcs, and 3.4%, 0.96%, and 0.5 for 4-arc VMAT. Deviations varied based on target size, isocentre distance, number of arcs, and arc type. Clinical results mirrored the phantom study, with maximum TV_PIV and GI deviations of 2.76% and 0.65 for the smallest target (0.6 cm) located at 7.5 cm distance for four-arc VMAT. Other dosimetric parameters showed minimal variations (< 1%). Correlation analysis revealed strong associations between dosimetric differences, target size, and distance (r = 0.6-0.78 for small targets). MANOVA identified TV_PIV as the only significant parameter (p = 0.01). A 1° angular resolution significantly improves dosimetric accuracy for small, distally located targets in SIMT SRS.

Lung function assessment is essential for determining the optimal treatment strategy for radiation therapy in patients with lung tumors. This study aimed to develop radiomics and dosiomics approaches to estimate pulmonary function test (PFT) results in post-stereotactic body radiation therapy (SBRT). Sixty-four patients with lung tumors who underwent SBRT were included. Models were created to estimate the PFT results at 0-6 months (Cohort 1) and 6-24 months (Cohort 2) after SBRT. Radiomics and dosiomics features were extracted from the computed tomography (CT) images and dose distributions, respectively. To estimate the PFT results, Models A (dose-volume histogram [DVH] + radiomics features) and B (DVH + radiomics + dosiomics features) were created. In the PFT results, the forced expiratory volume in one second (FEV1) and forced vital capacity (FVC) were estimated using each model, and the ratio of FEV1 to FVC (FEV1/FVC) was calculated. The Pearson's correlation coefficient (Pearson r) and area under the curve (AUC) for FEV1/FVC (< 70%) were calculated. The models were evaluated by comparing them with the conventional calculation formulae (Conventional). The Pearson r (FEV1/FVC) values were 0.30, 0.64, and 0.69 for Conventional and Models A and B (Cohort 2), respectively, and the AUC (FEV1/FVC < 70%) values were 0.63, 0.80, and 0.78, respectively. This study demonstrates the possibility of estimating lung function after SBRT using radiomics and dosiomics features based on planning CT images and dose distributions.

In plain radiography, scattered X-ray correction processing (Virtual Grid: VG) is used to estimate and correct scattered rays in images. We developed an objective evaluation system for bedside chest X-ray images using VG and investigated its usefulness. First, we trained the blind/referenceless image spatial quality evaluator (BRISQUE) on 200 images obtained by portable chest radiography. We then evaluated optimal chest phantom VG images as well as those that deviated from the VG setting conditions using BRISQUE. Furthermore, we conducted a subjective evaluation using the mean opinion score (MOS) and established an objective evaluation system for VG images. Finally, the degree of agreement between the MOS subjectively evaluated by 14 radiological technologists and that determined by the objective evaluation system for 100 clinical images obtained by portable chest radiography was calculated using Cohen's kappa coefficient. The correlation coefficient between the BRISQUE score and MOS for chest phantom images was - 0.96 (p < 0.05). The two scores showed a very high linear correlation, indicating the potential of the BRISQUE score as an alternative to MOS. The Cohen's kappa coefficient for the objective evaluation system using the optimal conversion table was 0.42. Conversely, there was a very high detection rate of 82.86% for poor-quality images. An objective evaluation system for bedside chest X-ray images using VG that uses no-reference image quality evaluation helps provide proper image quality. Furthermore, such a system can be constructed with a small amount of training data, which increases the possibility of introducing it to a variety of facilities.

Liver and spleen volume measurements are important for early detection and monitoring of liver disease. However, alterations in liver and spleen volumes with postural changes, i.e., the different effects of gravity, remain unclear. This study aims to evaluate the effects of posture on the liver and spleen in the supine and upright positions with an original magnetic resonance imaging (MRI) system capable of imaging in any posture (multiposture MRI). The liver and spleen volumes were assessed in ten healthy volunteers (age range: 20-24 years) in the supine and upright positions with multiposture MRI (0.4 T) and compared between postures. The liver and spleen volumes were significantly smaller in the upright position than in the supine position (P < 0.05 for both). Multiposture MRI offers more detailed information on liver and spleen volumes.

Accuracies of measuring the artifact index (AI), a quantitative artifact evaluation index in X-ray CT images, were investigated. The AI is calculated based not only on the standard deviation (SD) of the artifact area in the image, but also on the SD of noise components for considering the noise influence. However, conventional measurement methods may not follow this consideration, for example the non-uniformity of the noise distribution is not taken into account, resulting in reducing the accuracy of AI. To address this problem, this study aims to clarify the impact of noise SD measuring (NSDM) error on AI accuracy and improve the accuracy by reducing the NSDM error. Experimental results demonstrated that the conventional noise measurement methods reduced the accuracy of the AI. Specifically, AI inaccuracy due to the NSDM error is severe in the case of weak artifacts and under high noise conditions. Furthermore, the AI accuracy can be improved by reducing the influence of the NSDM error through image smoothing or by correcting NSDM through noise distribution estimation. These results showed that AI can be affected by NSDM errors practically even though it is robust against noise in principle. The impact of NSDM errors must be avoided for reliable artifact evaluation.

This study aimed to investigate the cause of susceptibility underestimation in body quantitative susceptibility mapping (QSM) and propose a water/fat separate reconstruction to address this issue. A numerical simulation was conducted using conventional QSM with/without body masking. The conventional method with body masking underestimated the susceptibility across all regions, whereas the method without body masking estimated an equivalent value to the ground truth. Additional numerical simulations and human experiments were conducted to compare the water/fat separate reconstruction, which separately reconstructs water and fat susceptibility maps based on the water/fat separation, with conventional QSM with body masking. The proposed method improved susceptibility estimation specifically in only the water tissue. The results of the human experiments were consistent with those of the numerical simulations. The lack of phase information outside the body contributed to susceptibility underestimation in conventional QSM. The developed method addressed susceptibility underestimation only in water tissue in body QSM.

Self-supervised learning (SSL) has gained attention in the medical field as a deep learning approach utilizing unlabeled data. The Jigsaw puzzle task in SSL enables models to learn both features of images and the positional relationships within images. In breast cancer diagnosis, radiologists evaluate not only lesion-specific features but also the surrounding breast structures. However, deep learning models that adopt a diagnostic approach similar to human radiologists are still limited. This study aims to evaluate the effectiveness of the Jigsaw puzzle task in characterizing breast tissue structures for breast cancer classification on mammographic images. Using the Chinese Mammography Database (CMMD), we compared four pre-training pipelines: (1) IN-Jig, pre-trained with both the ImageNet classification task and the Jigsaw puzzle task, (2) Scratch-Jig, pre-trained only with the Jigsaw puzzle task, (3) IN, pre-trained only with the ImageNet classification task, and (4) Scratch, that is trained from random initialization without any pre-training tasks. All pipelines were fine-tuned using binary classification to distinguish between the presence or absence of breast cancer. Performance was evaluated based on the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. Additionally, detailed analysis was conducted for performance across different radiological findings, breast density, and regions of interest were visualized using gradient-weighted class activation mapping (Grad-CAM). The AUC for the four models were 0.925, 0.921, 0.918, 0.909, respectively. Our results suggest the Jigsaw puzzle task is an effective pre-training method for breast cancer classification, with the potential to enhance diagnostic accuracy with limited data.

This study aims to develop a computerized classification method for significant coronary artery stenosis on whole-heart coronary magnetic resonance angiography (WHCMRA) images using a 3D convolutional neural network (3D-CNN) with attention mechanisms. The dataset included 951 segments from WHCMRA images of 75 patients who underwent both WHCMRA and invasive coronary angiography (ICA). Forty-two segments with significant stenosis (luminal diameter reduction $\ge$ 75%) on ICA were annotated on WHCMRA images by an experienced radiologist, whereas 909 segments without it were annotated at representative sites. Volumes of interest (VOIs) of 21 × 21 × 21 voxels centered on annotated points were extracted. The network comprises two feature extractors, two attention mechanisms (for the coronary artery and annotated points), and a classifier. The feature extractors first extracted the feature maps from the VOI. The two attention mechanisms weighted the feature maps of the coronary artery and those the neighborhood of the annotated point, respectively. The classifier finally classified the VOIs into those with and without significant coronary artery stenosis. Using fivefold cross-validation, the classification accuracy, sensitivity, specificity, and AUROC (area under the receiver operating characteristic curve) were 0.875, 0.905, 0.873, and 0.944, respectively. The proposed method showed high classification performance for significant coronary artery stenosis and appears to have a substantial impact on the interpretation of WHCMRA images.