Nihon Hoshasen Gijutsu Gakkai zasshi最新文献

Purpose: To investigate the accuracy of proton density fat fraction (PDFF) measurement using chemical shift-encoded MRI (CSE-MRI) with fast imaging techniques in a phantom.

Methods: A 1.5T imaging system (Prodiva; Philips Healthcare) and PDFF phantom (Fat Fraction Phantom Model 300; Calimetrix) were used in this study. The acquisitions without fast imaging techniques (conventional acquisition), with parallel imaging in phase-encode direction (SENSE acquisition), with compressed sensing (CS-SENSE acquisition), and with parallel imaging in both phase-encode and slice-encode direction (Dual-SENSE acquisition) were performed. The following acceleration factors in SENSE and CS-SENSE acquisition were used: 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, and 8.0. For Dual-SENSE acquisition, the same acceleration factors (1.5, 2.0, 3.0, 4.0, 5.0) were set in each of the two directions. The relationships between reference PDFF values and PDFF measurements obtained using each acquisition were assessed using linear regression analysis and Bland-Altman analysis.

Results: According to the linear regression analysis, the slopes and intercepts of regression lines were from 0.87 to 1.02 and from 0.06% to 3.55%, respectively. According to Bland-Altman analysis, there were fixed bias between reference PDFF values and PDFF measurements obtained using SENSE acquisition with reduction factor 8.0 and Dual-SENSE acquisition with reduction factor 5.0. For CS-SENSE acquisition with reduction factor from 7.0 to 8.0, SENSE acquisition with reduction factor from 3.0 to 8.0, and Dual-SENSE acquisition with reduction factor from 2.0 to 5.0, some vials had ±1.5% or more errors between the reference PDFF values and PDFF measurements in the range of 0% to 50% PDFF.

Conclusion: In CS-SENSE acquisition, the accuracy of PDFF measurement was maintained within 1.5% up to a reduction factor 6.0. The accuracy of PDFF measurement was maintained within 1.5% up to a reduction factor 2.0 in SENSE acquisition and a reduction factor 1.5 in Dual-SENSE acquisition.

Purpose: There are attempts to assess tumor heterogeneity by texture analysis. However, the ordered subsets-expectation maximization (OSEM) reconstruction method has problems depicting heterogeneities. The aim of this study was to identify image reconstruction parameters that improve the ability to depict internal tumor necrosis using a self-made phantom that simulates internal necrosis.

Methods: Self-made phantoms were prepared using polypropylene cylinders with inner diameters of 18.0 mm and 6.0 mm. The concentration ratios of the simulated tumor : tumor interior were 4 : 0 and 4 : 1. For each reconstruction method, the iteration for OSEM and OSEM+point spread function (PSF) were 1 to 25 and the subset was 12. The β values for block sequential regularized expectation maximization (BSREM) were set between 10 and 400. We evaluated the features of the profile curve, contrast-to-noise ratio, and grey-level co-occurrence matrix (GLCM).

Results: In the phantom study, OSEM and OSEM+PSF showed a better delineation of the differences between the inside and outside of the cylinder as iteration was increased and BSREM showed a better delineation as β was decreased. The highest value for each feature, both 4 : 0 and 4 : 1, was BSREM β 10 for angular second moment (ASM) and inverse differential moment (IDM), OSEM iteration 25 for contrast and entropy.

Conclusion: We have identified image reconstruction parameters that improve the ability to visualize internal tumor necrosis. The parameter was BRSEM β 10.

Purpose: This study developed a deep learning-based artificial intelligence (AI) observer to address the shortage of skilled human observers and evaluated the impact of substituting human observers with AI.

Methods: We used a CT system (Aquilion Prime SP; Canon Medical Systems, Tochigi) and modules CTP682 and CTP712 to scan the phantom (Catphan 700; Toyo Medic, Tokyo). The imaging conditions were set to a tube voltage of 120 kV and tube currents of 200, 160, 120, 80, 40, and 20 mA. Each condition was scanned twice, resulting in a total of 24 images. After the paired comparison experiment with 5 observers, deep learning models based on VGG19 and VGG16 were trained. We evaluated the variance, including both human and AI observers, and examined the impact of replacing humans with AI on the average degree of preference and statistical significance. These evaluations were conducted both when the training and assessments were from the same module and when they were from different modules.

Results: Variance ranged from 0.085 to 0.177 (mean: 0.124). Despite using different modules for training and evaluation, the variance remained consistent, indicating that the results are independent of the training data. The average degree of preference and image rankings were nearly identical. Between 200 mA and 160 mA, AI results differed from human results in terms of statistical significance, though the difference was minimal. The discrepancy arose from differences in observations between humans and AI, yet it fell within the expected range of variation typically observed among human observers.

Conclusion: Our results suggest that replacing human observers with AI has a minimal impact and may help alleviate observer shortages. The main limitation is the inability to modify evaluation criteria or stages with the trained models.

Purpose: The purpose of this study was to propose a method for measuring the maximum leaf velocity (V_max) of the multileaf collimator (MLC) in a dynamic MLC irradiation.

Methods: The irradiation was carried out with a plan in which the MLC leaves were constantly and gradually accelerated. Based on this plan, it was assumed that the velocity of each leaf v(t) (t is the elapsed time) would initially increase but plateau once it reached its maximum velocity. In the proposed method, v(t) was calculated from the log file data during irradiation, and fitted by a piecewise linear function consisting of 2 linear segments (constant acceleration and constant velocity segments); V_max was determined as the velocity in the constant velocity segments. The V_max values in each accelerator were obtained periodically for 7 months (20 measurements in total).

Results: In all measurements, the constant acceleration and constant velocity segments in v(t) were clearly distinguished by the piecewise linear approximation, and the V_max was determined. The mean V_max value of each leaf ranged from 3.63 to 4.32 cm/s with standard deviations (SD) less than 0.04 cm/s.

Conclusion: The proposed method made it possible to confirm the long-term stability of the V_max easily.

Purpose: StarGuide (GE HealthCare, Haifa, Israel) is a full-ring SPECT/CT system based on Cadmium Zinc Telluride (CZT) technology. In this study, we aimed to compare the image quality of this CZT-based SPECT/CT to a conventional Anger-type SPECT/CT system (NM/CT 870 DR, 870DR; GE HealthCare).

Methods: Tomographic sensitivity was calculated by recording the total number of counts detected during tomographic acquisition for a point source. We evaluated spatial resolution and image uniformity on each system using the full width half maximum (FWHM) of line sources and root mean square uniformity (%RMSU) of pool phantom, respectively. The voxel size of the StarGuide SPECT images was 2.46×2.46×2.46 mm³, compared to 4.42×4.42×4.42 mm³ on 870DR. These projection data were reconstructed using 3D-OSEM with a resolution recovery technique (RR). We compared 3 different algorithms: non-correction (NCRR), scatter correction (SCRR), and attenuation correction and scatter correction (ACSCRR).

Results: Tomographic sensitivity of StarGuide and 870DR were estimated at 200.0 counts・s^-1・MBq^-1 and 193.3 counts・s^-1・MBq^-1, respectively. Spatial resolution at the center of the FOV was estimated at 2.6 mm for StarGuide and 5.4 mm for 870DR with ACSCRR. Likewise, the %RMSU was 21.7 for StarGuide and 24.6 for 870DR.

Conclusion: The full-ring CZT SPECT/CT system has a superior spatial resolution and better image uniformity than the conventional Anger-type SPECT instrument, whereas tomographic sensitivity remains similar.

Purpose: The purpose of this study was to evaluate the accuracy of dose information obtained from the digital imaging and communications in medicine (DICOM) using the study description for dose management in nuclear medicine (nuclear medicine SD), and to investigate the feasibility of nuclear medicine SD.

Methods: Single-photon emission nuclear medicine examinations and radionuclide therapy from June 1 to June 30 in 2021 (our hospital master period) and 2023 (nuclear medicine SD period) were included. The dose information in the radioisotope administration record was taken as the true value, and the agreement rate of the examination type, radiopharmaceutical, and dose in the dose management system and the error rate of the dose in the nuclear medicine SD period were calculated.

Results: The agreement rate of examination type and radiopharmaceutical was improved from 37.5% to 97.0% by using nuclear medicine SD, and the agreement rate of dose was 54.0%.

Conclusion: The use of nuclear medicine SD has remarkably improved the integrity of dose information. Dose consistency can be improved by unifying the checking system and the input method of dose information. The feasibility of nuclear medicine SD seems to be high in many facilities, and it may contribute to information collaboration among multi-center facilities and enable centralization of dose management systems.