Radiological Physics and Technology最新文献

In Gd-EOB-DTPA-enhanced MRI, cardiac pulsation artifacts in the left lobe often hinder diagnosis, the image quality need to improve. This study aimed to reduce cardiac pulsation artifacts in Gd-EOB-DTPA-enhanced three-dimensional (3D) T1-weighted turbo-field echo (3D-T1TFE) using compressed sensitivity encoding (CS).For phantom evaluation, the cardiac phantom was manually operated using a metronome-synchronized apparatus, comprising a bag-valve mask, a breathing circuit, and a Jackson-Rees system. Transverse images of a liver phantom were acquired using enhanced T1 high-resolution isotropic volumetric excitation with CS (CS-eTHRIVE) and sensitivity encoding (S-eTHRIVE). For evaluation, images obtained during cardiac phantom operation were subtracted from those obtained when the phantom was stationary. Standard deviation (SD) of the difference images was used as the evaluation metric, and assessments were conducted based on changes in heart rate and TFE factor. For clinical image evaluation, artifacts in hepatobiliary phase images acquired 15 min after Gd-EOB-DTPA injection in the order of S-eTHRIVE and CS-eTHRIVE were visually evaluated at four levels. In heart-rate evaluation (40, 60, and 80 beats/min), CS-eTHRIVE revealed significantly lower SD values compared to S-eTHRIVE across all heart rates (P < 0.01), with no significant differences between heart rates. For TFE factor evaluation, CS-eTHRIVE with a factor of 35 exhibited the lowest SD, which was significantly different from all other groups (P < 0.01). In clinical image evaluation, CS-eTHRIVE achieved higher visual scores (mean ± SD: 3.72 ± 0.46) compared with S-eTHRIVE (2.72 ± 0.98, P < 0.01).CS reduced pulsation artifacts in Gd-EOB-DTPA-enhanced 3D-T1TFE.

The air-gap method is a technique employed to control dose distribution and radiation scattering in medical imaging. By introducing a layer of air between the radiation source and the object, this method effectively reduces the impact of scattered radiation. The purpose of this study was to investigate the suitability of the air-gap method for radiation dose reduction in pediatric patients during computed tomography (CT) examinations. Only one type of neonate phantom is used with 64 detector-row CT scanner while helical scanning the chest. The distance between the CT table and the subject was 0 mm at the conventional method and 150 mm at the air-gap method. The values of the real-time skin dosimeter on the dorsal surface of the body, and on the left and right mammary glands and image noise are measured and compared for each method. Compared with the conventional method, it was possible to reduce the exposure dose and image noise by approximately 10% and 15%, respectively, using the air-gap method (p < 0.05). The air-gap method was useful for reducing the radiation dose during pediatric CT examinations compared with the conventional method.

3D-digital subtraction angiography (3D-DSA) is essential for understanding the anatomical structure of cerebral veins, crucial in brain tumor surgery. 3D-DSA produces three-dimensional images of veins by adjusting the X-ray delay time after contrast agent injection, but the delineation of veins varies with the delay in X-ray timing. Our study aimed to refine the delay time using time-enhancement curve (TEC) analysis from 2D-DSA conducted before 3D-DSA imaging. We retrospectively reviewed 26 meningioma patients who underwent cerebral angiography from March 2020 to August 2021. Using 2D-DSA, we analyzed arterial and venous TECs to determine the contrast agent's peak time and estimated the optimal imaging timing. Cases performed near this optimal time were in Group A, and others in Group B, with cerebral venous pixel values compared between them. TEC analysis identified peak times: internal carotid artery: 2.8 ± 0.7 s, middle cerebral artery (M4): 4.1 ± 0.9 s, superior sagittal sinus: 8.3 ± 1.1 s, sigmoid sinus: 9.5 ± 1.3 s, and venous structures near tumors: 7.3 ± 1.0 s. We observed several veins peaking immediately after arterial contrast passage, suggesting the optimal X-ray delay should incorporate the arterial contrast agent's transit time. Statistical analysis revealed that Group A, with imaging timed to reflect the contrast agent transit time, demonstrated significantly better contrast effects than Group B. The X-ray delay time for 3D-DSA imaging of cerebral veins can be optimized in angiography systems by incorporating the contrast agent transit time, calculated from TEC analysis of cerebral 2D-DSA images.

This study aims to develop an anomaly-detection scheme for lesions in CT images. Our database consists of lung CT images obtained from 1500 examinees. It includes 1200 normal and 300 abnormal cases. In this study, SVDD (Support Vector Data Description) mapping the normal latent variables into a hypersphere as small as possible on the latent space is introduced to VQ-VAE (Vector Quantized-Variational Auto-Encoder). VQ-VAE with SVDD is constructed from two encoders, two decoders, and an embedding space. The first encoder compresses the input image into the latent-variable map, whereas the second encoder maps the normal latent variables into a hypersphere as small as possible. The first decoder then up-samples the mapped latent variables into a latent-variable map with the original size. The second decoder finally reconstructs the input image from the latent-variable map replaced by the embedding representations. The data of each examinee is classified as abnormal or normal based on the anomaly score defined as the combination of the difference between the input image and the reconstructed image and the distance between the latent variables and the center of the hypersphere. The area under the ROC curve for VQ-VAE with SVDD was 0.76, showing an improvement when compared with the conventional VAE (0.63, p < .001). VQ-VAE with SVDD developed in this study can yield higher anomaly-detection accuracy than the conventional VAE. The proposed method is expected to be useful for identifying examinees with lesions and reducing interpretation time in CT screening.

We aimed to evaluate the image quality and diagnostic performance of chronic lung allograft dysfunction (CLAD) with lung ventilation single-photon emission computed tomography (SPECT) images acquired briefly using a convolutional neural network (CNN) in patients after lung transplantation and to explore the feasibility of short acquisition times. We retrospectively identified 93 consecutive lung-transplant recipients who underwent ventilation SPECT/computed tomography (CT). We employed a CNN to distinguish the images acquired in full time from those acquired in a short time. The image quality was evaluated using the structural similarity index (SSIM) loss and normalized mean square error (NMSE). The correlation between functional volume/morphological volume (F/M) ratios of full-time SPECT images and predicted SPECT images was evaluated. Differences in the F/M ratio were evaluated using Bland-Altman plots, and the diagnostic performance was compared using the area under the curve (AUC). The learning curve, obtained using MSE, converged within 100 epochs. The NMSE was significantly lower (P < 0.001) and the SSIM was significantly higher (P < 0.001) for the CNN-predicted SPECT images compared to the short-time SPECT images. The F/M ratio of full-time SPECT images and predicted SPECT images showed a significant correlation (r = 0.955, P < 0.0001). The Bland-Altman plot revealed a bias of -7.90% in the F/M ratio. The AUC values were 0.942 for full-time SPECT images, 0.934 for predicted SPECT images and 0.872 for short-time SPECT images. Our findings suggest that a deep-learning-based approach can significantly curtail the acquisition time of ventilation SPECT, while preserving the image quality and diagnostic accuracy for CLAD.

To visualize the X-ray fields, overlaps, and over-beaming on the skin surface during spiral head CT scanning. The measured pitch factors were determined by measuring 3 rotation times, 11 table-feed speeds, and an X-ray beam width. The X-ray fields, overlaps, and over-beaming on the skin surface were calculated via computer-aided design-based X-ray beam modeling, and the values obtained using the nominal pitch and measured pitch factors were compared. The X-ray fields with measured pitch factors exceeded those with nominal pitch factors. The overlaps increased with a decrease in the nominal pitch and measured pitch factors and were observed even at a nominal pitch factor of 1.0. The most stretched over-beaming field was observed with the measured pitch factor of 0.670. The technique can show the overlaps of the X-ray fields and may determine the adequate start angle to prevent overlaps to the eye lens.

Patient respiration is characterized by respiratory parameters, such as cycle, amplitude, and baseline drift. In treatment planning using four-dimensional computed tomography (4DCT) images, the target dose may be affected by variations in image reconstruction techniques and respiratory parameters. This study aimed to optimize 4DCT image reconstruction techniques for the treatment planning of lung stereotactic body radiotherapy (SBRT) based on respiratory parameters using respiratory motion phantom. We quantified respiratory parameters using 30 respiratory motion datasets. The 4DCT images were acquired, and the phase- and amplitude-based reconstruction images (RI) were created. The target dose was calculated based on these reconstructed images. Statistical analysis was performed using Pearson's correlation coefficient (r) to determine the relationship between respiratory parameters and target dose in each reconstructed technique and respiratory region. In the inhalation region of phase-based RI, r of the target dose and baseline drift was -0.52. In particular, the target dose was significantly reduced for respiratory parameters with a baseline drift of 0.8 mm/s and above. No other respiratory parameters or respiratory regions were significantly correlated with target dose in phase-based RI. In amplitude-based RI, there were no significant differences in the correlation between all respiratory parameters and target dose in the exhalation or inhalation regions. These results showed that the target dose of the amplitude-based RI did not depend on changes in respiratory parameters or respiratory regions, compared to the phase-based RI. However, it is possible to guarantee the target dose by considering respiratory parameters during the inhalation region of the phase-based RI.

Proton dose calculation in media other than water may be of interest for either research purposes or clinical practice. Current study aims to quantify the required parameters for analytical proton dosimetry in muscle, bone, and PMMA. Required analytical dosimetry parameters were extracted from ICRU-49 report and Janni study. Geant4 Toolkit was also used for Bragg curve simulation inside the investigated media at different proton energies. Calculated and simulated dosimetry data were compared using gamma analysis. Simulated and calculated Bragg curves are consistent, a fact that confirms the validity of reported parameters for analytical proton dosimetry inside considered media. Furthermore, derived analytical parameters for these media are different from those of water. Listed parameters can be reliably utilized for analytical proton dosimetry inside muscle, bone, and PMMA. Furthermore, accurate proton dosimetry inside each medium demands dedicated analytical parameters and one is not allowed to use the water coefficients for non-water media.

This study investigates the influence of calculation accuracy in peripheral low-dose regions on the gamma pass rate (GPR), utilizing the Acuros XB (AXB) algorithm and ArcCHECK™ measurement. The effects of varying small field sizes, dose grid sizes, and split-arc techniques on GPR were analyzed. Various small field sizes were employed. Thirty-two single-arc plans with dose grid sizes of 2 mm and 1 mm and prescribed doses of 2, 5, 10, and 20 Gy were calculated using the AXB algorithm. In total, 128 GPR plans were examined. These plans were categorized into three sub-fields (3SF), four sub-fields (4SF), and six sub-fields (6SF). The GPR results deteriorated with smaller target sizes and a 2 mm dose grid size in a single arc. A similar degradation in GPR was observed with smaller target sizes and a 1 mm dose grid size. However, the 1 mm dose grid size generally resulted in better GPR compared with the 2 mm dose grid size for the same target sizes. The GPR improved with finer split angles and a 2 mm dose grid size in the split-arc method. However, no statistically significant improvement was observed with finer split angles and a 1 mm dose grid size. This study demonstrates that coarser dose grid sizes result in lower GPRs in peripheral low-dose regions as calculated by AXB with ArcCHECK™ measurement. To enhance GPR, employing split-arc methods and finer dose grid sizes could be beneficial.

Cerebral computed tomography perfusion (CTP) imaging requires complete acquisition of contrast bolus inflow and washout in the brain parenchyma; however, time truncation undoubtedly occurs in clinical practice. To overcome this issue, we proposed a three-dimensional (two-dimensional + time) convolutional neural network (CNN)-based approach to predict missing CTP image frames at the end of the series from earlier acquired image frames. Moreover, we evaluated three strategies for predicting multiple time points. Seventy-two CTP scans with 89 frames and eight slices from a publicly available dataset were used to train and test the CNN models capable of predicting the last 10 image frames. The prediction strategies were single-shot prediction, recursive multi-step prediction, and direct-recursive hybrid prediction.Single-shot prediction predicted all frames simultaneously, while recursive multi-step prediction used prior predictions as input for subsequent steps, and direct-recursive hybrid prediction employed separate models for each step with prior predictions as input for the next step. The accuracies of the predicted image frames were evaluated in terms of image quality, bolus shape, and clinical perfusion parameters. We found that the image quality metrics were superior when multiple CTP images were predicted simultaneously rather than recursively. The bolus shape also showed the highest correlation (r = 0.990, p < 0.001) and the lowest variance (95% confidence interval, -453.26-445.53) in the single-shot prediction. For all perfusion parameters, the single-shot prediction had the smallest absolute differences from ground truth. Our proposed approach can potentially minimize time truncation errors and support the accurate quantification of ischemic stroke.