Radiological Physics and Technology最新文献

This study aims to compare and evaluate the potential benefits of using single DV-based, multiple DV-based physical cost function, and biological-based cost functions for organs at risk (OARs) sparing in IMRT as well as VMAT plans of head and neck cancer. Forty head and neck cancer patients treated with inverse plan optimization techniques were retrospectively enrolled for this study. Three different treatment plans were optimized by single DV-based, multiple DV-based physical cost functions, and biological-based cost functions on MONACO 6.1® TPS. All three optimized plans were normalized to deliver the same prescribed target dose. All 120 optimized plans were analyzed using dose evaluation parameters. For IMRT plans, the biological cost functions (BCF) were superior to both DV-based optimizations when it came to the mean dose of parallel organs. For VMAT plans, multiple DV-based physical cost function optimization resulted in a lower mean dose of parallel organs when compared with other two optimization. The biological cost function significantly reduced the mean dose of parallel organs, for which multiple DV-based cost functions were not used. In both IMRT and VMAT plans, the DV-based physical cost function significantly reduced the maximum dose of serial organs, with the exception of the mandible. Biological-based optimization made it more likely that the parallel OARs would be spared in IMRT plans, while multiple DV-based optimization made it more likely that the parallel OARs would be spared in VMAT plans. Both DV-based optimization in IMRT and VMAT plans effectively spared the maximum dose of the serial organ.

Magnetic resonance imaging (MRI) is time-consuming, posing challenges in capturing clear images of moving organs, such as cardiac structures, including complex structures such as the Valsalva sinus. This study evaluates a computed tomography (CT)-guided refinement approach for cardiac segmentation from MRI volumes, focused on preserving the detailed shape of the Valsalva sinus. Owing to the low spatial contrast around the Valsalva sinus in MRI, labels from separate computed tomography (CT) volumes are used to refine the segmentation. Deep learning techniques are employed to obtain initial segmentation from MRI volumes, followed by the detection of the ascending aorta's proximal point. This detected proximal point is then used to select the most similar label from CT volumes of other patients. Non-rigid registration is further applied to refine the segmentation. Experiments conducted on 20 MRI volumes with labels from 20 CT volumes exhibited a slight decrease in quantitative segmentation accuracy. The CT-guided method demonstrated the precision (0.908), recall (0.746), and Dice score (0.804) for the ascending aorta compared with those obtained by nnU-Net alone (0.903, 0.770, and 0.816, respectively). Although some outputs showed bulge-like structures near the Valsalva sinus, an improvement in quantitative segmentation accuracy could not be validated.

This study aims to develop a flexible Geant4 application capable of modeling all IEC 61267 defined radiation qualities for the HOPEWELL Designs 225 kV X-ray generator, while systematically analyze the impact of various environmental and systematic factors. Using Geant4, we replicated the experimental setup of the LEGEX laboratory and simulated all IEC 61267 radiation qualities by adjusting relevant beam parameters. The model was validated by comparing simulated HVLs and spectra, measured with a CdTe X-123 spectrometer against experimental data, SRS78 software results, and IEC reference values. The simulation demonstrated strong agreement with experimental measurements and published data, confirming the validity of our Geant4 application. We derived the function that characterizes the behavior of Kinetic Energy Released per unit Mass (KERMA) in response to variations in each influencing factor. Geometrical misalignment is the primary contributor to deviations, followed by aluminum purity and diaphragm movement, while environmental factors induced minor fluctuations. Additionally, we quantified backscattered radiation and applied corrective measures to eliminate its impact on measurements. The developed Geant4 application provides a reliable tool for simulating IEC 61267 radiation qualities and optimizing dosimetric accuracy. Our framework offers a cost-effective alternative to replicate different scenarios multiple times to identify and minimizes uncertainties.

The purpose of the study is to investigate the degree and performance in the differential diagnosis of bronchiectasis/airspace enlargement in an iodine map obtainable from CT pulmonary angiography compared with monochromatic images. This retrospective study included 62 patients with a lung nodule who underwent CT pulmonary angiography. The iodine map and monochromatic image (70 keV) were reconstructed. Three readers evaluated the degree of bronchiectasis/airspace enlargement with a 4-point scale. A reference standard was established in 39 patients, and the performance of bronchiectasis/airspace enlargement in the differential diagnosis was evaluated in them. The degree of bronchiectasis/airspace enlargement in the iodine map (median score = 1/2/1 for reader 1/2/3) was significantly more prominent than that in the monochromatic image (median score = 0/1/0 for reader 1/2/3) (p < 0.001 for all readers). Using bronchiectasis/airspace enlargement, primary lung carcinoma and malignant lymphoma could be differentiated from other diseases, excluding lung infarct, with an area under the receiver operating characteristic curve (AUC) (reader 1/2/3) of 0.718/0.867/0.803 in the combinations of iodine map plus monochromatic image and 0.496/0.828/0.450 in the monochromatic image (p ≤ 0.047 for two readers). Lung metastasis from colorectal carcinoma could be differentiated from other diseases with an AUC of 0.851/0.976/0.838 in the combinations of iodine map plus monochromatic image, which was significantly superior to the monochromatic image (0.378/0.780/0.459) (p ≤ 0.012 for all readers). Bronchiectasis/airspace enlargement was more prominently observed in the iodine map than in the monochromatic image. This image finding in the iodine map provided added value in the differential diagnosis of malignant lung nodules compared with monochromatic images alone.

This study evaluated the detection accuracy of the VOXELAN surface-guided radiation therapy (SGRT) system using CT reference body surfaces generated by different radiation treatment planning systems (RTPSs) under their respective default CT value threshold settings. Two phantoms were used to assess 6-axis position matching accuracy with 1 mm and 2 mm slice thicknesses. Contour variations of approximately 2 mm were observed in the ventral and dorsal directions due to differing RTPSs. VOXELAN generally achieved detection accuracy within 1 mm, though some RTPS combinations showed errors exceeding 1 mm in the longitudinal and lateral directions. Slice thickness differences had minimal impact on detection accuracy. Overall, VOXELAN's detection accuracy varied slightly depending on the RTPS used but remained within approximately 1 mm. From our results, using a consistent RTPS when performing SGRT is recommended, as detection errors associated with different RTPS combinations were complex and difficult to interpret.

Digital subtraction angiography (DSA) is used to visualize blood vessels by subtracting pre-contrast (mask) images from contrast images; sequential mask and contrast images are used to generate dynamic DSA images that allow observation of blood flow and organ movements. However, misalignment between mask and contrast images can cause motion artifacts, which not only obscure the appearance of enhanced structures but also lead to the misidentification of patterns as vascular structures. In this study, we proposed a new method for generating abdominal sequential DSA images using a patch-based phase-matching technique between mask and contrast images acquired under natural breathing conditions. Our method divides mask and contrast images into small patches and selects the mask image patch most structurally similar to each patch in the target contrast image. Furthermore, the selected mask image patch is refined by searching for the subpixel-level region that most closely matches the target contrast image patch. The proposed method was evaluated using 20 abdominal angiogram cases, and its performance was compared with an existing phase matching-based method. Our experimental results showed that the proposed method effectively reduced motion artifacts and outperformed the comparison method in all cases. We demonstrated that our method successfully identified the optimal mask image for each contrast image on a patch-by-patch basis, allowing it to suppress artifacts caused by physiological motions such as peristalsis and cardiac pulsation, thereby generating higher-quality DSA images.

The myelin sheath is a multilayered structure that surrounds the axons of nerve cells. It acts as an insulator to ensure rapid and accurate transmission of electrical signals in the nervous system. Myelin water fraction (MWF) serves as a biomarker for the myelin sheath. Several methods for determining the MWF have been proposed; however, the inconsistency of MWF values is a challenge. In this study, we attempted to derive the MWF using quantitative parameter mapping (QPM). QPM ensures reproducibility by maintaining consistent imaging conditions across different scanners, enabling stable acquisition of quantitative parameters. This is expected to improve the reliability of the MWF measurements. Additionally, a significant correlation between QPM-derived parameters and the MWF has been reported. Five healthy volunteers were included in this study. QPM-MRI was performed using a 3-Tesla MR scanner with a three-dimensional radio frequency-spoiled steady-state gradient-echo (3D-RSSG) method. Using the derived quantitative values, pseudo-intensity images were generated for arbitrary continuous echo time values. Subsequently, a model equation for the brain tissue was defined. The generated signals were fitted with triexponential curve to estimate the amplitudes of each tissue component. Finally, the MWF was calculated using the amplitude ratio of each tissue. The mean MWF values for white matter and gray matter were 8.20 ± 4.97% and 7.99 ± 3.45%, respectively. This method using QPM allows for 3D data collection within a scan time applicable to standard clinical examinations and provides high accuracy in relaxation time estimation, thereby enabling stable quantification of MWF and suggesting its potential for clinical implementation.

This study aimed to generate myocardial perfusion images from coronary angiography (CAG) using Patlak plot analysis and evaluate their effectiveness in detecting ischemia. Data from 29 patients were analyzed. Electrocardiogram-synchronized CAG images of the left coronary artery were registered and processed for pixel-wise Patlak analysis. Image generation succeeded in 18 cases (62%) and failed in 11 due to motion artifacts caused by irregular heartbeats, table panning, or deep breathing. The resulting images clearly distinguished ischemic from normal regions. Perfusion values were significantly lower in ischemic regions compared to normal regions (p < 0.001). Despite technical challenges and variability in patient conditions, this method enabled consistent identification of perfusion deficits. Enhancing image processing increases the success rate. This approach allows ischemia assessment directly from CAG data and supports timely treatment planning, contributing to improved diagnostic precision and clinical decision-making in selected cases.

The verification of chest X-ray images involves several checkpoints, including orientation and reversal. To address the challenges of manual verification, this study developed an artificial intelligence (AI)-based system using a deep convolutional neural network (DCNN) to automatically verify the consistency between the imaging direction and examination orders. The system classified the chest X-ray images into four categories: anteroposterior (AP), posteroanterior (PA), flipped AP, and flipped PA. To evaluate the impact of internal and external datasets on the classification accuracy, the DCNN was trained using multiple publicly available chest X-ray datasets and tested on both internal and external data. The results demonstrated that the DCNN accurately classified the imaging directions and detected image reversal. However, the classification accuracy was strongly influenced by the training dataset. When trained exclusively on NIH data, the network achieved an accuracy of 98.9% on the same dataset; however, this reduced to 87.8% when evaluated with PADChest data. When trained on a mixed dataset, the accuracy improved to 96.4%; however, it decreased to 76.0% when tested on an external COVID-CXNet dataset. Further, using Grad-CAM, we visualized the decision-making process of the network, highlighting the areas of influence, such as the cardiac silhouette and arm positioning, depending on the imaging direction. Thus, this study demonstrated the potential of AI in assisting in automating the verification of imaging direction and positioning in chest X-rays. However, the network must be fine-tuned to local data characteristics to achieve optimal performance.

Glioblastoma multiforme is the most malignant brain tumor and is resistant to conventional radiotherapy. Proton radiotherapy utilizes accelerated proton beams to irradiate deep-seated tumors with minimum ionization in the entrance channel, thanks to its inverted dose profile. This work aims to investigate the response of human glioma (U87) cells cultured in a 3D culture after X-ray and proton irradiation. U87 cells have been cultured in 3D bio-phantom where cells were grown in Matrigel matrix inside a 96-well plate. The morphology of U87 cells in 3D culture has been observed for 48 h, and cells have grown in their natural shape. The response of cells in 3D bio-phantom was evaluated by exposing the cells to 6 MV X-ray and 70 MeV monoenergetic proton beams. Post-irradiation, the surviving cells were determined by a colony formation assay, and the survival curve of cells in 3D culture was compared with the cells grown in 2D monolayer culture. The response of cells in the 3D bio-phantom following X-ray and proton radiation demonstrated an increased survival fraction in the high-dose region than those in 2D monolayer. However, U87 cells showed more sensitivity towards proton irradiation compared to X-rays, regardless of the culture setup. Finally, we obtained the RBE $_{10\%}^{}$ value of 1.15 for cells in 3D bio-phantom and 1.29 for cells in 2D monolayer. Therefore, U87 cells grown in our 3D culture setup demonstrate radio-resistant behavior and exhibit higher sensitivity towards proton irradiation compared to X-ray irradiation in our clonogenic assay.