Nihon Hoshasen Gijutsu Gakkai zasshi最新文献

Purpose: Osteosclerotic images are known as an image appearance of occult femoral neck fractures in X-ray images. The aim of this study is to investigate frequency enhancement processing that improves the visibility of the osteosclerotic image.

Methods: We acquired three sclerotic bone images with different thicknesses, and self-made bone equivalent phantoms were set up on a pelvic phantom. The frequency processing type and high-density enhancement coefficients were applied to the X-ray images taken at RF-A(1.0, 2.0), C(2.0, 4.0), D(1.0), and H(2.0, 4.0). For the physical index, we compared the difference in signal values between the sclerotic and background normal bone. We evaluated the preference using Scheffé's paired comparison methods for the visual index.

Results: For the physical index, RF-C(4.0) had the most significant signal value difference for all 3 bone stiffness images. For the visual index, RF-C(4.0) showed the highest preference.

Conclusion: Using frequency-enhanced processing, RF-C(4.0) was suggested to improve the visibility of the osteosclerosis image.

【Purpose】 Accurate control of X-ray units and dosimeters and analysis of the uncertainties associated with the accurate measurement of radiation doses are essential for the effective establishment and application of diagnostic reference levels. In this study, the uncertainty of the average glandular dose (AGD) in the quality control of mammography equipment was evaluated in detail, and recommendations were provided to improve the accuracy and safety of radiological practice. 【Methods】 In the uncertainty analysis of the AGD, the relative standard uncertainties in the measurements of the half-value layer, the incident air kerma, and the conversion factor were considered and finally expressed as expanded uncertainties, the intervals of which were clearly defined. 【Results】 From the AGD measurements using two types of dosimeters, it was found that the primary sources of uncertainty are the uncertainty of the calibration factors of the dosimeters and the uncertainty of the conversion factors.【Conclusion】 To reduce uncertainty, the use of regularly calibrated dosimeters is effective and reliable. Two types of dosimeters are commonly used; the results of this study may serve as a reference value for the uncertainty of AGD in quality control in medical facilities.

Purpose: To verify the usefulness of a deep learning model for determining the presence or absence of contrast-enhanced myocardium in late gadolinium-enhancement images in cardiac MRI.

Methods: We used 174 late gadolinium-enhancement myocardial short-axis images obtained from contrast-enhanced cardiac MRI performed using a 3.0T MRI system at the University of Tokyo Hospital. Of these, 144 images were used for training, extracting a region of interest targeting the heart, scaling signal intensity, and data augmentation were performed to obtain 3312 training images. The interpretation report of two cardiology specialists of our hospital was used as the correct label. A learning model was constructed using a convolutional neural network and applied to 30 test data. In all cases, the acquired mean age was 56.4±12.1 years, and the male-to-female ratio was 1 : 0.82.

Results: Before and after data augmentation, sensitivity remained consistent at 93.3%, specificity improved from 0.0% to 100.0%, and accuracy improved from 46.7% to 96.7%.

Conclusion: The prediction accuracy of the deep learning model developed in this research is high, suggesting its high usefulness.

In this study, we investigated the effects of various disinfectants used to prevent infectious diseases on medical images and medical equipment. First, we investigated the effect of residual disinfectant on medical images in CT, mammography (MMG), and general imaging systems. Acrylic discs with various disinfectants attached were photographed using each imaging device, and visual evaluation and changes in image signal values were evaluated. We also conducted a questionnaire survey of each manufacturer regarding cleaning methods for medical devices. With CT/MMG, residual disinfectant could be visually confirmed on the image. Although this could not be confirmed with the general imaging system, a significant difference was confirmed in the image signal values of the general imaging system through statistical analysis. This is thought to be largely due to the influence of nonlinearity in the short-time imaging range of general imaging equipment. In addition, from the responses to a questionnaire survey of each medical device manufacturer, we were able to understand detailed cleaning methods that are not covered in medical device instruction manuals.

Purpose: The aim of this study was to validate the potential of substituting an observer in a paired comparison with a deep-learning observer.

Methods: Phantom images were obtained using computed tomography. Imaging conditions included a standard setting of 120 kVp and 200 mA, with tube current variations ranging from 160 mA, 120 mA, 80 mA, 40 mA, and 20 mA, resulting in six different imaging conditions. Fourteen radiologic technologists with >10 years of experience conducted pairwise comparisons using Ura's method. After training, VGG16 and VGG19 models were combined to form deep learning models, which were then evaluated for accuracy, recall, precision, specificity, and F₁value. The validation results were used as the standard, and the results of the average degree of preference and significance tests between images were compared to the standard if the results of deep learning were incorporated.

Results: The average accuracy of the deep learning model was 82%, with a maximum difference of 0.13 from the standard regarding the average degree of preference, a minimum difference of 0, and an average difference of 0.05. Significant differences were observed in the test results when replacing human observers with AI counterparts for image pairs with tube currents of 160 mA vs. 120 mA and 200 mA vs. 160 mA.

Conclusion: In paired comparisons with a limited phantom (7-point noise evaluation), the potential use of deep learning was suggested as one of the observers.

Purpose: In Japan, radiologists perform qualitative visual classification to define four categories of mammary gland density. However, an objective estimation of mammary gland density is necessary. To address this, we developed an automatic classification software using image similarity.

Methods: We prepared 741 cases of mediolateral oblique images (MLO) for evaluation, and they were diagnosed as normal among the mammography images taken at our hospital. Image matching was performed using the evaluation images and an image database for breast density determination. In this study, the image similarity used zero normalized cross-correlation (ZNCC) as an index. In addition, if the breast thickness is less than 30 mm and each breast density category ZNNC has the same value, the category is evaluated on the fat side. We compared the results of qualitative visual classification and automatic classification methods to assess consistency.

Results: The agreement with the subjective breast composition classification was 78.5%, and the weighted kappa coefficient was 0.98. One mismatched case was evaluated on the higher density side with the same ZNCC value between categories and a breast thickness greater than 30 mm.

Conclusion: Image similarity provides an excellent estimation of quantification of breast density. This system could contribute to improving the efficiency of the mammography screening system.

Purpose: To verify the optimal imaging conditions for coronary computed tomography angiography (CCTA) examinations when using high-definition (HD) mode and deep learning image reconstruction (DLIR) in combination.

Method: A chest phantom and an in-house phantom using 3D printer were scanned with a 256-row detector CT scanner. The scan parameters were as follows - acquisition mode: ON (HD mode) and OFF (normal resolution [NR] mode), rotation time: 0.28 s/rotation, beam coverage width: 160 mm, and the radiation dose was adjusted based on CT-AEC. Image reconstruction was performed using ASiR-V (Hybrid-IR), TrueFidelity Image (DLIR), and HD-Standard (HD mode) and Standard (NR mode) reconstruction kernels. The task-based transfer function (TTF) and noise power spectrum (NPS) were measured for image evaluation, and the detectability index (d') was calculated. Visual evaluation was also performed on an in-house coronary phantom.

Result: The in-plane TTF was better for the HD mode than for the NR mode, while the z-axis TTF was lower for DLIR than for Hybrid-IR. The NPS values in the high-frequency region were higher for the HD mode compared to those for the NR mode, and the NPS was lower for DLIR than for Hybrid-IR. The combination of HD mode and DLIR showed the best value for in-plane d', whereas the combination of NR mode and DLIR showed the best value for z-axis d'. In the visual evaluation, the combination of NR mode and DLIR showed the best values from a noise index of 45 HU.

Conclusion: The optimal combination of HD mode and DLIR depends on the image noise level, and the combination of NR mode and DLIR was the best imaging condition under noisy conditions.

Purpose: During computed tomography pulmonary angiography (CTPA), a decrease in the CT value of the pulmonary artery may be observed due to poor contrast enhancement, even though the imaging is performed at the optimum timing while continuously injecting a contrast medium. This study focused on the increase in blood flow in the superior and inferior vena cava during inspiration that affects the decrease in the CT value of the pulmonary artery and investigated a radiography method in which a delay time was set after inspiration in clinical cases.

Methods: A total of 50 patients who underwent CTPA for suspected pulmonary thromboembolism were included. Using the bolus tracking method, we monitored the pulmonary arteries before and after inspiration, and investigated the CT value changes.

Results: A decrease in the CT value of the pulmonary artery after inspiration was observed in approximately 30% of cases. By setting the delay time, the contrast enhancement effect before and after inspiration became equivalent.

Conclusion: As a result of this study, avoiding a decrease in the CT value of the pulmonary artery is possible by setting a delay time after inspiration, which is considered useful during CTPA.

Purpose: It is very difficult for a radiologist to correctly detect small lesions and lesions hidden on dense breast tissue on a mammogram. Therefore, recently, computer-aided detection (CAD) systems have been widely used to assist radiologists in interpreting images. Thus, in this study, we aimed to segment mass on the mammogram with high accuracy by using focus images obtained from an eye-tracking device.

Methods: We obtained focus images for two mammography expert radiologists and 19 mammography technologists on 8 abnormal and 8 normal mammograms published by the DDSM. Next, the auto-encoder, Pix2Pix, and UNIT learned the relationship between the actual mammogram and the focus image, and generated the focus image for the unknown mammogram. Finally, we segmented regions of mass on mammogram using the U-Net for each focus image generated by the auto-encoder, Pix2Pix, and UNIT.

Results: The dice coefficient in the UNIT was 0.64±0.14. The dice coefficient in the UNIT was higher than that in the auto-encoder and Pix2Pix, and there was a statistically significant difference (p<0.05). The dice coefficient of the proposed method, which combines the focus images generated by the UNIT and the original mammogram, was 0.66±0.15, which is equivalent to the method using the original mammogram.

Conclusion: In the future, it will be necessary to increase the number of cases and further improve the segmentation.

Purpose: In X-ray computed tomography (CT), noise distribution within images is nonuniform and thought to vary with imaging conditions. This study aimed to evaluate noise nonuniformity by altering specific imaging conditions, such as tube voltage, bow-tie filter (BTF), and phantom size.

Methods: Using four tube voltages (80, 100, 120, and 135 kV), two BTF types (L and M), and circular water phantoms with diameters of 240, 320, and 400 mm, we employed filtered back projection (FBP) for reconstruction. Noise nonuniformity was assessed by defining six regions of interest (ROI) from the image center to the periphery, and the noise nonuniformity index (NNI) was calculated based on the standard deviation (SD) values within these ROIs.

Results: Results showed consistently larger noise SD values in the central region compared to the peripheral region under all imaging conditions, with the maximum NNI reaching 32.1%. Variations in NNI were observed, reaching up to 5.5 points for tube voltage, 7.8 points for BTF, and 8.2 points for phantom size.

Conclusion: In conclusion, our quantitative assessment revealed moderate dependence of noise nonuniformity on imaging conditions in CT images.