Radiological Physics and Technology最新文献

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.

The purpose of the study is to investigate the variation in Hounsfield unit (HU) values calculated using dual-energy computed tomography (DECT) scanners. A tissue characterization phantom inserting 16 reference materials were scanned three times using DECT scanners [dual-layer CT (DLCT), dual-source CT (DSCT), and fast kilovoltage switching CT (FKSCT)] changing scanning conditions. The single-energy CT images (120 or 140 kVp), and virtual monochromatic images at 70 keV (VMI₇₀) and 140 keV (VMI₁₄₀) were reconstructed, and the HU values of each reference material were measured. The difference in HU values was larger when the phantom was scanned using the half dose with wrapping with rubber (strong beam-hardening effect) compared with the full dose without the rubber (reference condition), and the difference was larger as the electron density increased. For SECT, the difference in HU values against the reference condition measured by the DSCT (3.2 ± 5.0 HU) was significantly smaller (p < 0.05) than that using DLCT with 120 kVp (22.4 ± 23.8 HU), DLCT with 140 kVp (11.4 ± 12.8 HU), and FKSCT (13.4 ± 14.3 HU). The respective difference in HU values in the VMI₇₀ and VMI₁₄₀ measured using the DSCT (10.8 ± 17.1 and 3.5 ± 4.1 HU) and FKSCT (11.5 ± 21.8 and 5.5 ± 10.4 HU) were significantly smaller than those measured using the DLCT₁₂₀ (23.1 ± 27.5 and 12.4 ± 9.4 HU) and DLCT₁₄₀ (22.3 ± 28.6 and 13.1 ± 11.4 HU). The HU values and the susceptibility to beam-hardening effects varied widely depending on the DECT scanners.

This study proposes the use of the inversion recovery T₁-weighted turbo field echo (IR-T₁TFE) sequence for myocardial T₁ mapping and compares the results obtained with those of the modified Look-Locker inversion recovery (MOLLI) method for accuracy, precision, and reproducibility. A phantom containing seven vials with different T₁ values was imaged, thereby comparing the T₁ measurements between the inversion recovery spin-echo (IR-SE) technique, MOLLI, and the IR-T₁TFE. The accuracy, precision, and reproducibility of the T₁-mapping sequences were analyzed in a phantom study. Fifteen healthy subjects were recruited for the in vivo comparison of native myocardial T₁ mapping using MOLLI and IR-T₁TFE sequences. After myocardium segmentation, the T₁ value of the entire myocardium was calculated. In the phantom study, excellent accuracy was achieved using IR-T₁TFE for all T₁ ranges. MOLLI displayed lower accuracy than IR-T₁TFE (p =0.016), substantially underestimating T₁ at large T₁ values (> 1000 ms). In the in vivo study, the first mean myocardial T₁ values ± SD using MOLLI and IR-T₁TFE were 1306 ± 70 ms and 1484 ± 28 ms, respectively, and the second were 1297 ± 68 ms and 1474 ± 43 ms, respectively. The native myocardial T₁ obtained with MOLLI was lower than that of IR-T₁TFE (p < 0.001). The reproducibility of native myocardial T₁ mapping within the same sequence was not statistically significant (p = 0.11). This study demonstrates the utility and validity of myocardial T₁ mapping using IR-T₁TFE, which is a common sequence. This method was found to have high accuracy and reproducibility.

Measurement-based verification is impossible for the patient-specific quality assurance (QA) of online adaptive magnetic resonance imaging-guided radiotherapy (oMRgRT) because the patient remains on the couch throughout the session. We assessed a deep learning (DL) system for oMRgRT to predict the gamma passing rate (GPR). This study collected 125 verification plans [reference plan (RP), 100; adapted plan (AP), 25] from patients with prostate cancer treated using Elekta Unity. Based on our previous study, we employed a convolutional neural network that predicted the GPRs of nine pairs of gamma criteria from 1%/1 mm to 3%/3 mm. First, we trained and tested the DL model using RPs (n = 75 and n = 25 for training and testing, respectively) for its optimization. Second, we tested the GPR prediction accuracy using APs to determine whether the DL model could be applied to APs. The mean absolute error (MAE) and correlation coefficient (r) of the RPs were 1.22 ± 0.27% and 0.29 ± 0.10 in 3%/2 mm, 1.35 ± 0.16% and 0.37 ± 0.15 in 2%/2 mm, and 3.62 ± 0.55% and 0.32 ± 0.14 in 1%/1 mm, respectively. The MAE and r of the APs were 1.13 ± 0.33% and 0.35 ± 0.22 in 3%/2 mm, 1.68 ± 0.47% and 0.30 ± 0.11 in 2%/2 mm, and 5.08 ± 0.29% and 0.15 ± 0.10 in 1%/1 mm, respectively. The time cost was within 3 s for the prediction. The results suggest the DL-based model has the potential for rapid GPR prediction in Elekta Unity.

The purpose of this study was to validate an electronic portal imaging device (EPID) based 3-dimensional (3D) dosimetry system for the commissioning of volumetric modulated arc therapy (VMAT) delivery for flattening filter (FF) and flattening filter free (FFF) modalities based on test suites developed according to American Association of Physicists in Medicine Task Group 119 (AAPM TG 119) and pre-treatment patient specific quality assurance (PSQA).With ionisation chamber, multiple-point measurement in various planes becomes extremely difficult and time-consuming, necessitating repeated exposure of the plan. The average agreement between measured and planned doses for TG plans is recommended to be within 3%, and both the ionisation chamber and PerFRACTION™ measurement were well within this prescribed limit. Both point dose differences with the planned dose and gamma passing rates are comparable with TG reported multi-institution results. From our study, we found that no significant differences were found between FF and FFF beams for measurements using PerFRACTION™ and ion chamber. Overall, PerFRACTION™ produces acceptable results to be used for commissioning and validating VMAT and for performing PSQA. The findings support the feasibility of integrating PerFRACTION™ into routine quality assurance procedures for VMAT delivery. Further multi-institutional studies are recommended to establish global baseline values and enhance the understanding of PerFRACTION^™'s capabilities in diverse clinical settings.

This review focuses on positron emission tomography (PET) imaging algorithms and traces the evolution of PET image reconstruction methods. First, we provide an overview of conventional PET image reconstruction methods from filtered backprojection through to recent iterative PET image reconstruction algorithms, and then review deep learning methods for PET data up to the latest innovations within three main categories. The first category involves post-processing methods for PET image denoising. The second category comprises direct image reconstruction methods that learn mappings from sinograms to the reconstructed images in an end-to-end manner. The third category comprises iterative reconstruction methods that combine conventional iterative image reconstruction with neural-network enhancement. We discuss future perspectives on PET imaging and deep learning technology.