Radiological Physics and Technology最新文献

In this study, we developed a method for generating quasi-material decomposition (quasi-MD) images from single-energy computed tomography (SECT) images using a deep convolutional neural network (DCNN). Our aim was to improve the detection of cholesterol gallstones and to determine the clinical utility of quasi-MD images. Four thousand pairs of virtual monochromatic images (70 keV) and MD images (fat/water) of the same section, obtained via dual-energy computed tomography (DECT), were used to train the DCNN. The trained DCNN can automatically generate quasi-MD images from the SECT images. Additional SECT images were obtained from 70 patients (40 with and 30 without cholesterol gallstones) to generate quasi-MD images for testing. The presence of gallstones in this dataset was confirmed by ultrasonography. We conducted a receiver operating characteristic (ROC) observer study with three radiologists to validate the clinical utility of the quasi-MD images for detecting cholesterol gallstones. The mean area under the ROC curve for the detection of cholesterol gallstones improved from 0.867 to 0.921 (p = 0.001) when quasi-MD images were added to SECT images. The clinical utility of quasi-MD imaging for detecting cholesterol gallstones was showed. This study demonstrated that the lesion detection capability of images obtained from SECT can be improved using a DCNN trained with DECT images obtained using high-end computed tomography systems.

The work investigates the implementation of personalized radiotherapy boluses by means of additive manufacturing technologies. Boluses materials that are currently used need an excessive amount of human intervention which leads to reduced repeatability in terms of dosimetry. Additive manufacturing can solve this problem by eliminating the human factor in the process of fabrication. Planar boluses with fixed geometry and personalized boluses printed starting from a computed tomography scan of a radiotherapy phantom were produced. First, a dosimetric characterization study on planar bolus designs to quantify the effects of print parameters such as infill density and geometry on the radiation beam was made. Secondly, a volumetric quantification of air gap between the bolus and the skin of the patient as well as dosimetric analyses were performed. The optimization process according to the obtained dosimetric and airgap results allowed us to find a combination of parameters to have the 3D-printed bolus performing similarly to that in conventional use. These preliminary results confirm those in the relevant literature, with 3D-printed boluses showing a dosimetric performance similar to conventional boluses with the additional advantage of being perfectly conformed to the patient geometry.

We proposed a new deep learning (DL) model for accurate scatter correction in digital radiography. The proposed network featured a pixel-wise water equivalent path length (WEPL) map of subjects with diverse sizes and 3D inner structures. The proposed U-Net model comprises two concatenated modules: one for generating a WEPL map and the other for predicting scatter using the WEPL map as auxiliary information. First, 3D CT images were used as numerical phantoms for training and validation, generating observed and scattered images by Monte Carlo simulation, and WEPL maps using Siddon's algorithm. Then, we optimised the model without overfitting. Next, we validated the proposed model's performance by comparing it with other DL models. The proposed model obtained scatter-corrected images with a peak signal-to-noise ratio of 44.24 ± 2.89 dB and a structural similarity index measure of 0.9987 ± 0.0004, which were higher than other DL models. Finally, scatter fractions (SFs) were compared with other DL models using an actual phantom to confirm practicality. Among DL models, the proposed model showed the smallest deviation from measured SF values. Furthermore, using an actual radiograph containing an acrylic object, the contrast-to-noise ratio (CNR) of the proposed model and the anti-scatter grid were compared. The CNR of the images corrected using the proposed model are 16% and 82% higher than those of the raw and grid-applied images, respectively. The advantage of the proposed method is that no actual radiography system is required for collecting training dataset, as the dataset is created from CT images using Monte Carlo simulation.

Using numerical indices and visual evaluation, we evaluated the dependence of coronary-artery depictability on the denoising parameter in compressed sensing magnetic resonance angiography (CS-MRA). This study was conducted to clarify the acceleration factor (AF) and denoising factor (DF) dependence of CS-MRA image quality. Vascular phantoms and clinical images were acquired using three-dimensional CS-MRA on a clinical 1.5 T system. For the phantom measurements, we compared the full width at half maximum (FWHM), sharpness, and contrast ratio of the vascular profile curves for various AFs and DFs. In the clinical cases, the FWHM, sharpness, contrast ratio, signal-to-noise ratio, noise level values, and visual evaluation results were compared for various DFs. Phantom image analyses demonstrated that the respective measurements of the FWHM, sharpness, and contrast ratios did not significantly change with an increase in AF. The FWHM and sharpness measurements slightly changed with the DF level. However, the contrast ratio tended to increase with an increase in the DF level. In the clinical cases, the FWHM and sharpness showed no significant differences, even when the DF level was changed. However, the contrast ratio tended to decrease as the DF level increased. When the DF levels of the clinical cases increased, the background signals of the myocardium, fat, and noise levels decreased. We investigated the dependence of the coronary-artery depictability on AF and DF using CS-MRA. Analysis of the coronary-artery profile curves indicated that a better image quality was achieved with a stronger DF on coronary CS-MRA.

The projection data generated via the forward projection of a computed tomography (CT) image (FP-data) have useful potentials in cases where only image data are available. However, there is a question of whether the FP-data generated from an image severely corrupted by metal artifacts can be used for the metal artifact reduction (MAR). The aim of this study was to investigate the feasibility of a MAR technique using FP-data by comparing its performance with that of a conventional robust MAR using projection data normalization (NMARconv). The NMAR_conv was modified to make use of FP-data (FPNMAR). A graphics processing unit was used to reduce the time required to generate FP-data and subsequent processes. The performances of FPNMAR and NMAR_conv were quantitatively compared using a normalized artifact index (AI_n) for two cases each of hip prosthesis and dental fillings. Several clinical CT images with metal artifacts were processed by FPNMAR. The AI_n values of FPNMAR and NMAR_conv were not significantly different from each other, showing almost the same performance between these two techniques. For all the clinical cases tested, FPNMAR significantly reduced the metal artifacts; thereby, the images of the soft tissues and bones obscured by the artifacts were notably recovered. The computation time per image was ~ 56 ms. FPNMAR, which can be applied to CT images without accessing the projection data, exhibited almost the same performance as that of NMAR_conv, while consuming significantly shorter processing time. This capability testifies the potential of FPNMAR for wider use in clinical settings.

A few reports have discussed the influence of inter-fractional position error and intra-fractional motion on dose distribution, particularly regarding a spread-out Bragg peak. We investigated inter-fractional and intra-fractional prostate position error by monitoring fiducial marker positions. In 2020, data from 15 patients with prostate cancer who received carbon-ion beam radiotherapy (CIRT) with gold markers were investigated. We checked marker positions before and during irradiation to calculate the inter-fractional positioning and intra-fractional movement and evaluated the CIRT dose distribution by adjusting the planning beam isocenter and clinical target volume (CTV) position. We compared the CTV dose coverages (CTV receiving 95% [V95%] or 98% [V98%] of the prescribed dose) between skeletal and fiducial matching irradiation on the treatment planning system. For inter-fractional error, the mean distance between the marker position in the planning images and that in a patient starting irradiation with skeletal matching was 1.49 ± 1.11 mm (95th percentile = 1.85 mm). The 95th percentile (maximum) values of the intra-fractional movement were 0.79 mm (2.31 mm), 1.17 mm (2.48 mm), 1.88 mm (4.01 mm), 1.23 mm (3.00 mm), and 2.09 mm (8.46 mm) along the lateral, inferior, superior, dorsal, and ventral axes, respectively. The mean V95% and V98% were 98.2% and 96.2% for the skeletal matching plan and 99.5% and 96.8% for the fiducial matching plan, respectively. Fiducial matching irradiation improved the CTV dose coverage compared with skeletal matching irradiation for CIRT for prostate cancer.

This study aimed to assess the subjective and objective image quality of low-dose computed tomography (CT) images processed using a self-supervised denoising algorithm with deep learning. We trained the self-supervised denoising model using low-dose CT images of 40 patients and applied this model to CT images of another 30 patients. Image quality, in terms of noise and edge sharpness, was rated on a 5-point scale by two radiologists. The coefficient of variation, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) were calculated. The values for the self-supervised denoising model were compared with those for the original low-dose CT images and CT images processed using other conventional denoising algorithms (non-local means, block-matching and 3D filtering, and total variation minimization-based algorithms). The mean (standard deviation) scores of local and overall noise levels for the self-supervised denoising algorithm were 3.90 (0.40) and 3.93 (0.51), respectively, outperforming the original image and other algorithms. Similarly, the mean scores of local and overall edge sharpness for the self-supervised denoising algorithm were 3.90 (0.40) and 3.75 (0.47), respectively, surpassing the scores of the original image and other algorithms. The CNR and SNR for the self-supervised denoising algorithm were higher than those for the original images but slightly lower than those for the other algorithms. Our findings indicate the potential clinical applicability of the self-supervised denoising algorithm for low-dose CT images in clinical settings.

This study investigates the feasibility of estimating the radioactivity of radiopharmaceuticals using shielded syringes. The radioactivities of ^99mTc-MDP, ^99mTc-HMDP, ^99mTc-ECD, ^99mTc-MAG₃, and ¹²³I-IMP were measured using a dose calibrator. Correlation coefficients and regression equations were obtained from the radioactivity in the shielded and unshielded syringes. ^99mTc-MDP was also measured for residual radioactivity after the administration. The correlation coefficients of ^99mTc-MDP, ^99mTc-HMDP, ^99mTc-ECD, ^99mTc-MAG₃, and ¹²³I-IMP were r_s = 0.9998, r_s = 0.9997, r_s = 0.9999, r_s = 0.9998, and r_s = 0.9888, respectively. The regression equations were y = 0.0364x + 0.0913, y = 0.0349x + 0.0273, y = 0.0343x - 0.0018, y = 0.0522x + 0.1215, and y = 0.0383x + 0.0058, respectively. The correlation coefficient for the residual radioactivity of ^99mTc-MDP was r_s = 0.9887 and the regression equation was y = 0.1505x + 0.0853. The radioactivity of ^99mTc- and ¹²³I-labeled radiopharmaceuticals in shielded syringes was accurately measured. It was suggested that the measuring shielded syringes could provide an estimate of the actual radioactivity.

To investigate the geometric accuracy of the radiation focal point (RFP) and cone-beam computed tomography (CBCT) over long-term periods for the ICON Leksell Gamma Knife radiosurgery system. This phantom study utilized the ICON quality assurance tool plus, and the phantom was manually set on the patient position system before the implementation of treatment for patients. The deviation of the RFP position from the unit center point (UCP) and the positions of the four ball bearings (BBs) in the CBCT from the reference position were automatically analyzed. During 544 days, a total of 269 analyses were performed on different days. The mean ± standard deviation (SD) of the deviation between measured RFP and UCP was 0.01 ± 0.03, 0.01 ± 0.03, and -0.01 ± 0.01 mm in the X, Y, and Z directions, respectively. The deviations with offset values after the cobalt-60 source replacement (0.00 ± 0.03, -0.01 ± 0.01, and -0.01 ± 0.01 mm in the X, Y, and Z directions, respectively) were significantly (p = 0.001) smaller than those before the replacement (0.02 ± 0.03, 0.02 ± 0.01, and -0.02 ± 0.01 mm in the X, Y, and Z directions, respectively). The overall mean ± SD of four BBs was -0.03 ± 0.03, -0.01 ± 0.05, and 0.01 ± 0.03 mm in the X, Y, and Z directions, respectively. Geometric positional accuracy was ensured to be within 0.1 mm on most days over a long-term period of more than 500 days.

This study analyse setup time (ST) and frequency of on-board imaging for stereotactic abdomen (liver, stomach), lung, and spine radiotherapy in the absence of automatic rotational correction. Total 53 stereotactic body radiotherapy (SBRT) patients, 28 of abdomen, 19 lung, and 6 spine treated for 230 sessions in O-ring gantry accelerator were evaluated for ST analysis. The mean setup time for all patients, abdomen, lung, and spine cases were 7.7 ± 7.4 min, 9.2 ± 9.2 min, 6.3 ± 4.1 min, and 5.5 ± 3.3 min, respectively. Median number CBCT was 2. 96% of cases had a CBCT between 1 and 3, and 9 (4%) had ≥ 4 CBCTs. Overall, 38.1%, 35.5%, 22.1%, 2.2%, and 2.2% of setup time fall into window of 0-5 min, 5-10 min, 10-20 min, 20-30 min, and > 30 min. Most difficult challenge is to negotiate with unknown rotational errors. It will be easy to dealt with them without automatic rotational correction if values are known.