Yoshinori Funama

Objectives: The objective of the study is to determine the contrast-specific conversion factor (cCF) based on the dose differences between non-enhanced computed tomography (NECT) and contrast-enhanced CT (CECT) in abdominal imaging, with the aim of improving the accuracy of radiation dose estimation in contrast-enhanced examinations. Material and Methods: The study included 33 adult patients who underwent both NECT and CECT. CECT image acquisition commenced 80 s after the start of iodinated contrast medium injection. A Monte Carlo simulation tool was used to estimate the radiation dose delivered to each patient during NECT and CECT. Organ doses for the liver, spleen, and both kidneys were measured using 120-kVp images. The cCF was calculated as the ratio of the organ dose in CECT to that in NECT. The cCF values are dependent on the scan protocol-, CT vendor-, and X-ray photon energy. Results: The mean organ doses during NECT were 10.45 mGy for the liver, 11.19 mGy for the spleen, and 11.47 mGy for both kidneys. During CECT, these values increased to 15.83 mGy, 17.56 mGy, and 20.75 mGy, respectively. The mean cCFs of CECT relative to NECT were 1.52 for the liver, 1.83 for the spleen, and 1.87 for both kidneys. Conclusion: Applying the cCF to NECT-based dose estimates enables more accurate assessment of radiation exposure in CECT examinations.

To assess the diagnostic performance of unenhanced electrocardiogram (ECG)-gated cardiac computed tomography (CT) for detecting myocardial edema, using MRI T2 mapping as the reference standard. This retrospective study protocol was approved by our institutional review board, which waived the requirement for written informed consent. Between December 2017 to February 2019, consecutive patients who had undergone T2 mapping for myocardial tissue characterization were identified. We excluded patients who did not undergo unenhanced ECG-gated cardiac CT within 3 months from MRI T2 mapping or who had poor CT image quality. All patients underwent unenhanced ECG-gated cardiac CT with an axial scan using a third-generation, 320 × 0.5 mm detector-row CT unit. Two radiologists together drew regions of interest (ROIs) in the interventricular septum on the unenhanced ECG-gated cardiac CT images. Using T2 mapping as the reference standard, the diagnostic performance of unenhanced cardiac CT for detecting myocardial edema was evaluated by using the area under the receiver operating characteristic curve with sensitivity and specificity. Youden index was used to find an optimal sensitivity-specificity cutoff point. A cardiovascular radiologist independently performed the measurements, and interobserver reliability was assessed using intraclass correlation coefficients for CT value measurements. A P value of <.05 was considered statistically significant. We included 257 patients who had undergone MRI T2 mapping. Of the 257 patients, 35 patients underwent unenhanced ECG-gated cardiac CT. One patient was excluded from the study because of poor CT image quality. Finally, 34 patients (23 men; age 64.7 ± 14.6 years) comprised our study group. Using T2 mapping, we identified myocardial edema in 19 patients. Mean CT and T2 values for 34 patients were 46.3 ± 2.7 Hounsfield unit and 49.0 ± 4.9 ms, respectively. Mean CT values moderately correlated with mean T2 values (Rho = –0.41; P < .05). Mean CT values provided a sensitivity of 63.2% and a specificity of 93.3% for detecting myocardial edema, with a cutoff value of ≤45.0 Hounsfield unit (area under the receiver operating characteristic curve = 0.77; P < .01). Inter-observer reproducibility in measuring mean CT values was excellent (intraclass correlation coefficient = 0.93; [95% confidence interval: 0.86, 0.96]). Myocardial edema could be detected by CT value of myocardium in unenhanced ECG-gated cardiac CT.

The study aimed to compare the performance of photon-counting detector computed tomography (PCD CT) with high-resolution (HR)-plaque kernel with that of the energy-integrating detector CT (EID CT) in terms of the visualization of the lumen size and the in-stent stenotic portion at different coronary vessel angles. The lumen sizes in PCD CT and EID CT images were 2.13 and 1.80 mm at 0°, 2.20 and 1.77 mm at 45°, and 2.27 mm and 1.67 mm at 90°, respectively. The lumen sizes in PCD CT with HR-plaque kernel were wider than those in EID CT. The mean degree of the in-stent stenotic portion at 50% was 69.7% for PCD CT and 90.4% for EID CT. PCD CT images with HR-plaque kernel enable improved visualization of lumen size and accurate measurements of the in-stent stenotic portion compared to conventional EID CT images regardless of the stent direction.

Purpose: In this preliminary study, we aimed to evaluate the potential of the generative pre-trained transformer (GPT) series for generating radiology reports from concise imaging findings and compare its performance with radiologist-generated reports. Methods: This retrospective study involved 28 patients who underwent computed tomography (CT) scans and had a diagnosed disease with typical imaging findings. Radiology reports were generated using GPT-2, GPT-3.5, and GPT-4 based on the patient’s age, gender, disease site, and imaging findings. We calculated the top-1, top-5 accuracy, and mean average precision (MAP) of differential diagnoses for GPT-2, GPT-3.5, GPT-4, and radiologists. Two board-certified radiologists evaluated the grammar and readability, image findings, impression, differential diagnosis, and overall quality of all reports using a 4-point scale. Results: Top-1 and Top-5 accuracies for the different diagnoses were highest for radiologists, followed by GPT-4, GPT-3.5, and GPT-2, in that order (Top-1: 1.00, 0.54, 0.54, and 0.21, respectively; Top-5: 1.00, 0.96, 0.89, and 0.54, respectively). There were no significant differences in qualitative scores about grammar and readability, image findings, and overall quality between radiologists and GPT-3.5 or GPT-4 (p > 0.05). However, qualitative scores of the GPT series in impression and differential diagnosis scores were significantly lower than those of radiologists (p < 0.05). Conclusions: Our preliminary study suggests that GPT-3.5 and GPT-4 have the possibility to generate radiology reports with high readability and reasonable image findings from very short keywords; however, concerns persist regarding the accuracy of impressions and differential diagnoses, thereby requiring verification by radiologists.

Background: A multi detector computed tomography (CT) scanner with wide-area coverage enables whole-brain volumetric scanning in a single rotation. Purpose: To investigate variations in image-quality characteristics in the longitudinal direction for different image-reconstruction algorithms and strengths with phantoms. Material and methods: Single-rotation volume scans were performed on a 320-row multidetector CT volume scanner using three types of phantoms. Tube current was set to 200 mA (standard dose) and 50 mA (low dose). All images were reconstructed with filtered back projection (FBP), mild and strong levels with hybrid iterative reconstruction (HIR), and model-based IR (MBIR). Computed tomography numbers, image noise, noise power spectrum (NPS), task-based transfer function (TTF), and visual spatial resolution were used to evaluate uniformity of image quality in the longitudinal direction ( Z-axis). Results: The MBIR images showed smaller variation in CT numbers in the Z-axis. The difference in the highest and lowest CT numbers was smaller (5.0 Hounsfield units [HU]) for MBIR than for FBP (6.6 HU) and HIR (6.8 HU). The variations in image noise were the smallest for strong MBIR and the largest for FBP. The low-frequency component at NPS _0.2 was lower for strong MBIR than for other algorithms. The high-frequency component at NPS _0.8 was low in all reconstructions. For MBIR, the image resolution and TTFs were higher in the outer portion than in the center. Conclusion: Model-based IR is the optimal image-reconstruction algorithm for single-volume scan of spherical subjects owing to its high in-plane resolution and uniformity of CT numbers, image noise, and NPS in the Z-axis.