Advances in spatial resolution and radiation dose reduction using super-resolution deep learning-based reconstruction for abdominal computed tomography: A phantom study.

RATIONALE AND OBJECTIVES: This study evaluated the performance of super-resolution deep learning-based reconstruction (SR-DLR) and compared with it that of hybrid iterative reconstruction (HIR) and normal-resolution DLR (NR-DLR) for enhancing image quality in computed tomography (CT) images across various field of view (FOV) sizes, radiation doses, and noise reduction strengths. MATERIALS AND METHODS: A Catphan phantom equipped with an external body ring was used. CT images were reconstructed using filtered back-projection (FBP), HIR, NR-DLR, and SR-DLR across three noise reduction strengths: mild, standard, and strong. The noise power spectrum (NPS) was obtained from the FBP, HIR, NR-DLR, and SR-DLR images at various FOVs, radiation doses, and noise reduction strengths. The noise magnitude ratio (NMR) and central frequency ratio (CFR) were calculated from the HIR, NR-DLR, and SR-DLR images relative to the FBP images using NPS. The high-contrast value was obtained from the amplitude values of the peaks and valleys of profile curve and the task-based transfer function were also analyzed. RESULTS: SR-DLR consistently demonstrated superior noise reduction capabilities, with NMR of 0.29-0.36 at reduced dose and 0.35-0.45 at standard dose, outperforming HIR and showing comparable efficiency to NR-DLR. The high-contrast values for SR-DLR were highest at mild and standard levels for both low and standard doses (0.610 and 0.726 at mild and 0.725 and 0.603 at standard levels). At the standard dose, the spatial resolution of SR-DLR was significantly improved, regardless of the noise reduction strength and FOV. CONCLUSION: SR-DLR images achieved more substantial noise reduction than HIR and similar noise reduction as NR-DLR reconstructions while also improving spatial resolution.

Non-contrast spectral CT vs chemical-shift MRI in discriminating lipid-poor adrenal lesions.

OBJECTIVES: To compare the diagnostic performance of conventional non-contrast CT, dual-energy spectral CT, and chemical-shift MRI (CS-MRI) in discriminating lipid-poor adenomas (> 10-HU on non-contrast CT) from non-adenomas. METHODS: A total of 110 patients (69 men; 41 women; mean age 66.5 ± 13.4 years) with 80 lipid-poor adenomas and 30 non-adenomas who underwent non-contrast dual-layer spectral CT and CS-MRI were retrospectively identified. For each lesion, non-contrast attenuation on conventional 120-kVp images, ΔHU-index ([attenuation difference between virtual monoenergetic 140-keV and 40-keV images]/conventional attenuation × 100), and signal intensity index (SI-index) were quantified. Each parameter was compared between adenomas and non-adenomas using the Mann-Whitney U-test. The area under the receiver operating characteristic curve (AUC) and sensitivity to achieve > 95% specificity for adenoma diagnosis were determined. RESULTS: Conventional non-contrast attenuation was lower in adenomas than in non-adenomas (22.4 ± 8.6 HU vs 32.8 ± 48.5 HU), whereas ΔHU-index (148.0 ± 103.2 vs 19.4 ± 25.8) and SI-index (41.6 ± 19.6 vs 4.2 ± 10.2) were higher in adenomas (all, p < 0.001). ΔHU-index showed superior performance to conventional non-contrast attenuation (AUC: 0.919 [95% CI: 0.852-0.963] vs 0.791 [95% CI: 0.703-0.863]; sensitivity: 75.0% [60/80] vs 27.5% [22/80], both p < 0.001), and near equivalent to SI-index (AUC: 0.952 [95% CI: 0.894-0.984], sensitivity 85.0% [68/80], both p > 0.05). Both the ΔHU-index and SI-index provided a sensitivity of 96.0% (48/50) for hypoattenuating adenomas (≤ 25 HU). For hyperattenuating (> 25 HU) adenomas, SI-index showed higher sensitivity than ΔHU-index (66.7% [20/30] vs 40.0% [12/30], p = 0.022). CONCLUSIONS: Non-contrast spectral CT and CS-MRI outperformed conventional non-contrast CT in distinguishing lipid-poor adenomas from non-adenomas. While CS-MRI demonstrated superior sensitivity for adenomas measuring > 25 HU, non-contrast spectral CT provided high discriminative values for adenomas measuring ≤ 25 HU. CLINICAL RELEVANCE STATEMENT: Spectral attenuation analysis improves the diagnostic performance of non-contrast CT in discriminating lipid-poor adrenal adenomas, potentially serving as an alternative to CS-MRI and obviating the necessity for additional diagnostic workup in indeterminate adrenal incidentalomas, particularly for lesions measuring ≤ 25 HU. KEY POINTS: Incidental adrenal lesion detection has increased as abdominal CT use has become more frequent. Non-contrast spectral CT and CS-MRI differentiated lipid-poor adenomas from non-adenomas better than conventional non-contrast CT. For lesions measuring ≤ 25 HU, spectral CT may obviate the need for additional evaluation.

Super-resolution deep learning reconstruction approach for enhanced visualization in lumbar spine MR bone imaging.

OBJECTIVES: This study aims to assess the effectiveness of super-resolution deep-learning-based reconstruction (SR-DLR), which leverages k-space data, on the image quality of lumbar spine magnetic resonance (MR) bone imaging using a 3D multi-echo in-phase sequence. MATERIALS AND METHODS: In this retrospective study, 29 patients who underwent lumbar spine MRI, including an MR bone imaging sequence between January and April 2023, were analyzed. Images were reconstructed with and without SR-DLR (Matrix sizes: 960 × 960 and 320 × 320, respectively). The signal-to-noise ratio (SNR) of the vertebral body and spinal canal and the contrast and contrast-to-noise ratio (CNR) between the vertebral body and spinal canal were quantitatively evaluated. Furthermore, the slope at half-peak points of the profile curve drawn across the posterior border of the vertebral body was calculated. Two radiologists independently assessed image noise, contrast, artifacts, sharpness, and overall image quality of both image types using a 4-point scale. Interobserver agreement was evaluated using weighted kappa coefficients, and quantitative and qualitative scores were compared via the Wilcoxon signed-rank test. RESULTS: SNRs of the vertebral body and spinal canal were notably improved in images with SR-DLR (p < 0.001). Contrast and CNR were significantly enhanced with SR-DLR compared to those without SR-DLR (p = 0.023 and p = 0.022, respectively). The slope of the profile curve at half-peak points across the posterior border of the vertebral body and spinal canal was markedly higher with SR-DLR (p < 0.001). Qualitative scores (noise: p < 0.001, contrast: p < 0.001, artifact p = 0.042, sharpness: p < 0.001, overall image quality: p < 0.001) were superior in images with SR-DLR compared to those without. Kappa analysis indicated moderate to good agreement (noise: κ = 0.56, contrast: κ = 0.51, artifact: κ = 0.46, sharpness: κ = 0.76, overall image quality: κ = 0.44). CONCLUSION: SR-DLR, which is based on k-space data, has the potential to enhance the image quality of lumbar spine MR bone imaging utilizing a 3D gradient echo in-phase sequence. CLINICAL RELEVANCE STATEMENT: The application of SR-DLR can lead to improvements in lumbar spine MR bone imaging quality.

Subclinical leaflet thrombus in patients with severe aortic stenosis and atrial fibrillation -ENRICH-AF TAVI study.

Subclinical leaflet thrombosis (SLT) can be one of the causes of transcatheter heart valve (THV) failure after transcatheter aortic valve implantation (TAVI). We sought to clarify the formation process of SLT and thrombogenicity during the perioperative period of TAVI. This multicenter, prospective, single-arm interventional study enrolled 26 patients treated with edoxaban for atrial fibrillation and who underwent TAVI for severe aortic stenosis between September 2018 and September 2022. We investigated changes in maximal leaflet thickness detected by contrast-enhanced computed tomography between 1 week and 3 months after TAVI in 18 patients and measured the thrombogenicity by Total Thrombus-formation Analysis System (T-TAS) and flow stagnation volume by computational fluid dynamics (CFD) (n = 11). SLT was observed in 16.7% (3/18) at 1 week, but decreased to 5.9% (1/17) at 3 months after TAVI. Patients with SLT at 1 week had a significantly decreased maximal leaflet thickness compared to those without SLT. Thrombogenicity assessed by T-TAS decreased markedly at 1 week and tended to increase at 3 months. The stagnation volume assessed by CFD was positively associated with a higher maximum leaflet thickness. This study showed the course of leaflet thrombus formation and visualization of stagnation in neo-sinus of THV in the acute phase after TAVI.

Super-resolution deep-learning reconstruction for cardiac CT: impact of radiation dose and focal spot size on task-based image quality.

This study aimed to evaluate the impact of radiation dose and focal spot size on the image quality of super-resolution deep-learning reconstruction (SR-DLR) in comparison with iterative reconstruction (IR) and normal-resolution DLR (NR-DLR) algorithms for cardiac CT. Catphan-700 phantom was scanned on a 320-row scanner at six radiation doses (small and large focal spots at 1.4-4.3 and 5.8-8.8 mGy, respectively). Images were reconstructed using hybrid-IR, model-based-IR, NR-DLR, and SR-DLR algorithms. Noise properties were evaluated through plotting noise power spectrum (NPS). Spatial resolution was quantified with task-based transfer function (TTF); Polystyrene, Delrin, and Bone-50% inserts were used for low-, intermediate, and high-contrast spatial resolution. The detectability index (d') was calculated. Image noise, noise texture, edge sharpness of low- and intermediate-contrast objects, delineation of fine high-contrast objects, and overall quality of four reconstructions were visually ranked. Results indicated that among four reconstructions, SR-DLR yielded the lowest noise magnitude and NPS peak, as well as the highest average NPS frequency, TTF50%, d' values, and visual rank at each radiation dose. For all reconstructions, the intermediate- to high-contrast spatial resolution was maximized at 4.3 mGy, while the lowest noise magnitude and highest d' were attained at 8.8 mGy. SR-DLR at 4.3 mGy exhibited superior noise performance, intermediate- to high-contrast spatial resolution, d' values, and visual rank compared to the other reconstructions at 8.8 mGy. Therefore, SR-DLR may yield superior diagnostic image quality and facilitate radiation dose reduction compared to the other reconstructions, particularly when combined with small focal spot scanning.

Improving Visualization of In-stent Lumen Using Prototype Photon-counting Detector Computed Tomography with High-resolution Plaque Kernel.

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.

A Novel Approach to Detecting Contrast Extravasation in Computed Tomography: Evaluating the Injection Pressure-to-Injection Rate Ratio.

OBJECTIVE: The purpose of this study was to evaluate the usefulness of the injection pressure-to-injection rate (IPIR) ratio for the early detection of contrast extravasation at the venipuncture site during contrast-enhanced computed tomography. METHODS: We retrospectively enrolled 57,528 patients who underwent contrast-enhanced computed tomography examinations in a single hospital. The power injector recorded the contrast injection pressure at 0.25-second intervals. We constructed logistic regression models using the IPIR ratio as the independent variable and extravasation occurrence as the dependent variable (IPIR ratio models) at 1, 2, 3, 4, 5, and 6 seconds after the start of contrast administration. Univariate logistic regression models in which injection pressure is used as an independent variable (injection pressure models) were also constructed as a reference baseline. The performance of the models was evaluated with the area under the receiver operating characteristic curves. RESULTS: Of the 57,528 cases, 46,022 were assigned to the training group and 11,506 were assigned to the test group, which included 112 extravasation cases (0.24%) in the training group and 28 (0.24%) in the test group. The area under the receiver operating characteristic curves for the IPIR ratio models and injection pressure models were 0.555 versus 0.563 at t = 1 (P = 0.270), 0.712 versus 0.678 at t = 2 (P = 0.305), 0.758 versus 0.693 at t = 3 (P = 0.032), 0.776 versus 0.688 at t = 4 (P = 0.005), 0.810 versus 0.699 at t = 5 (P = 0.002), and 0.811 versus 0.706 at t = 6 (P = 0.002). CONCLUSIONS: The IPIR ratio models perform better in detecting contrast extravasation at 3 to 6 seconds after the start of contrast administration than injection pressure models.

Diagnostic performance of unenhanced electrocardiogram-gated cardiac CT for detecting myocardial edema.

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 ±â€…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 ±â€…2.7 Hounsfield unit and 49.0 ±â€…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.

Clinical characteristics of patients with high extracellular volume fraction evaluated by cardiac computed tomography for coronary artery evaluation.

Aims: This study aims to evaluate the distribution of extracellular volume fraction detected via computed tomography, clinical characteristics of high extracellular volume fraction detected via computed tomography, and the rate of incidental detection of cardiac amyloidosis in patients undergoing cardiac computed tomography for coronary artery evaluation. Methods and results: This study included 874 consecutive patients (mean age, 74.4 ± 7.1 years; men, 65%), comprising men aged ≥60 years and women aged ≥70 years, who had undergone cardiac computed tomography between January 2020 and September 2022. The mean extracellular volume fraction detected via computed tomography was 29.7 ± 5.2%, and 108 patients (12.4%) had an extracellular volume fraction detected via computed tomography of ≥35%. Older age (75.9 ± 8.2 years vs. 74.2 ± 6.9 years; P = 0.042), male sex (75.9% vs. 63.0%; P = 0.007), impaired left ventricular ejection fraction, increased high-sensitivity cardiac troponin T and B-type natriuretic peptide levels, and increased left ventricular thickness showed significant associations with an extracellular volume fraction detected via computed tomography of ≥35%. Cardiac amyloidosis was diagnosed incidentally in 15 patients based on an increase in extracellular volume fraction detected via computed tomography. The prevalence of cardiac amyloidosis was 1.7% (15/874) and 14.3% (15/105) in the entire study population and in patients with an extracellular volume fraction detected via computed tomography of ≥35%, respectively. An increase in the extracellular volume fraction detected via computed tomography was suggestive of cardiac amyloidosis. Conclusion: Elevated extracellular volume fraction detected via computed tomography, associated with elevated cardiac biomarker levels and myocardial structural changes, may lead to the incidental diagnosis of cardiac amyloidosis.

Diagnostic performance of hepatic CT and chemical-shift MRI to discriminate lipid-poor adrenal adenomas from hepatocellular carcinoma metastases.

PURPOSE: To evaluate the diagnostic performance of multiphase hepatic CT parameters (non-contrast attenuation, absolute and relative washout ratios [APW and RPW, respectively], and relative enhancement ratio [RER]) and chemical-shift MRI (CS-MRI) for discriminating lipid-poor adrenal adenomas (with non-contrast CT attenuation > 10 HU) from metastases in patients with hepatocellular carcinoma (HCC). METHODS: This retrospective study included HCC patients with lipid-poor adrenal lesions who underwent multiphase hepatic CT between January 2010 and December 2021. For each adrenal lesion, non-contrast attenuation, APW, RPW, RER, and signal-intensity index (SI-index) were measured. Each parameter was compared between adenomas and metastases. The area under the receiver operating characteristic curves (AUCs) and sensitivities to achieve 100% specificity for adenoma diagnoses were determined. RESULTS: 104 HCC patients (78 men; mean age, 71.8 ± 9.6 years) with 63 adenomas and 48 metastases were identified; CS-MRI was performed in 66 patients with 49 adenomas and 21 metastases within one year of CT. Lipid-poor adenomas showed lower non-contrast attenuation (22.9 ± 7.1 vs. 37.9 ± 9.4 HU) and higher APW (40.5% ± 12.8% vs. 23.7% ± 17.4%), RPW (30.0% ± 10.2% vs. 12.4% ± 9.6%), RER (329% ± 152% vs. 111% ± 43.0%), and SI-index (43.3 ± 20.7 vs. 10.8 ± 13.4) than HCC metastases (all p < .001). AUC for non-contrast attenuation, APW, RPW, RER, and SI-index were 0.894, 0.786, 0.904, 0.969, and 0.902, respectively. The sensitivities to achieve 100% specificity were 7.9%, 25.4%, 30.2%, 63.5%, and 24.5%, respectively. Combined RER and APW achieved the highest sensitivity of 73.0%. CONCLUSION: Multiphase hepatic CT allows for better discrimination between lipid-poor adrenal adenomas and metastases relative to CS-MRI, especially when combined with RER and washout parameters.

Optimizing High-Resolution MR Angiography: The Synergistic Effects of 3D Wheel Sampling and Deep Learning-Based Reconstruction.

OBJECTIVE: The aim of this study was to assess the utility of the combined use of 3D wheel sampling and deep learning-based reconstruction (DLR) for intracranial high-resolution (HR)-time-of-flight (TOF)-magnetic resonance angiography (MRA) at 3 T. METHODS: This prospective study enrolled 20 patients who underwent head MRI at 3 T, including TOF-MRA. We used 3D wheel sampling called "fast 3D" and DLR for HR-TOF-MRA (spatial resolution, 0.39 × 0.59 × 0.5 mm 3 ) in addition to conventional MRA (spatial resolution, 0.39 × 0.89 × 1 mm 3 ). We compared contrast and contrast-to-noise ratio between the blood vessels (basilar artery and anterior cerebral artery) and brain parenchyma, full width at half maximum in the P3 segment of the posterior cerebral artery among 3 protocols. Two board-certified radiologists evaluated noise, contrast, sharpness, artifact, and overall image quality of 3 protocols. RESULTS: The contrast and contrast-to-noise ratio of fast 3D-HR-MRA with DLR are comparable or higher than those of conventional MRA and fast 3D-HR-MRA without DLR. The full width at half maximum was significantly lower in fast 3D-MRA with and without DLR than in conventional MRA ( P = 0.006, P < 0.001). In qualitative evaluation, fast 3D-MRA with DLR had significantly higher sharpness and overall image quality than conventional MRA and fast 3D-MRA without DLR (sharpness: P = 0.021, P = 0.001; overall image quality: P = 0.029, P < 0.001). CONCLUSIONS: The combination of 3D wheel sampling and DLR can improve visualization of arteries in intracranial TOF-MRA.

Cardiac MRI T1 and T2 Mapping as a Quantitative Imaging Biomarker in Transthyretin Amyloid Cardiomyopathy.

RATIONALE AND OBJECTIVES: This study aimed to assess the utility of cardiac magnetic resonance imaging (MRI) T1 and T2 mapping as quantitative imaging biomarkers in transthyretin amyloid cardiomyopathy (ATTR-CM). MATERIALS AND METHODS: This study retrospectively evaluated 74 patients with confirmed wild-type ATTR-CM who underwent cardiac MRI, 99mTc-labeled pyrophosphate (99mTc-PYP) scintigraphy, and echocardiography. We assessed the quantitative disease parameters, for example, left ventricular ejection fraction (LVEF), and global longitudinal strain (GLS) by echocardiography, native T1, extracellular volume fraction (ECV), and native T2 value by cardiac MRI, heart-to-contralateral ratio (H/CL) by 99mTc-PYP, and high-sensitive cardiac troponin T. Myocardial native T2 of ≥50 ms was defined as myocardial edema. Correlations between the disease's quantitative parameters were evaluated, and the ECV was compared to other parameters in ATTR-CM with/without myocardial edema. RESULTS: ECV in all patients with ATTR-CM revealed a strong correlation with native T1 (r = 0.62), a moderate correlation with hs-TnT (r = 0.59), LVEF (r = -0.48), GLS (r = 0.58), and H/CL (r = 0.48). Correlations between ECV and other quantitative parameters decreased in ATTR-CM with myocardial edema except for H/CL. Meanwhile, the correlations increased in ATTR-CM without myocardial edema. CONCLUSION: The presence of myocardial edema affected the interpretation of ECV assessment, although ECV can be a comprehensive imaging biomarker for ATTR-CM. ECV showed a significant correlation with various quantitative disease parameters and can be a reliable disease monitoring marker in patients with ATTR-CM when myocardial edema was excluded.

Myocardial extracellular volume quantification in cardiac amyloidosis: a comparative study between cardiac computed tomography and magnetic resonance imaging.

OBJECTIVES: Myocardial extracellular volume (ECV) on computed tomography (CT), an alternative to cardiac magnetic resonance (CMR), has significant practical clinical advantages. However, the consistency between ECVs quantified via CT and CMR in cardiac amyloidosis (CA) has not been investigated sufficiently. Therefore, the current study investigated the application of CT-ECV in CA with CMR-ECV as the reference standard. METHODS: We retrospectively evaluated 31 patients with CA who underwent cardiac CT and CMR. Pearson correlation analysis was performed to investigate correlations between CT-ECV and CMR-ECV at each segment. Further, correlations between ECV and clinical parameters were assessed. RESULTS: There were no significant differences in the mean global ECVs between CT scan and CMR (51.3% ± 10.2% vs 50.0% ± 10.5%). CT-ECV was correlated with CMR-ECV at the septal (r = 0.88), lateral (r = 0.80), inferior (r = 0.79), anterior (r = 0.77) segments, and global (r = 0.87). In both CT and CMR, the ECV had a weak to strong correlation with high-sensitivity cardiac troponin T level, a moderate correlation with global longitudinal strain, and an inverse correlation with left ventricular ejection fraction. Further, the septal ECV and global ECV had a slightly higher correlation with the clinical parameters. CONCLUSIONS: Cardiac CT can quantify myocardial ECV and yield results comparable to CMR in patients with CA. Moreover, a significant correlation between CT-ECV and clinical parameters was observed. Thus, CT-ECV can be an imaging biomarker and alternative to CMR-ECV. CLINICAL RELEVANCE STATEMENT: Cardiac CT can quantify myocardial ECV and yield results comparable to CMR in patients with CA, and CT-ECV can be used clinically as an imaging biomarker and alternative to CMR-ECV. KEY POINTS: • A significant correlation was found between CT myocardial extracellular volume and cardiac MR myocardial extracellular volume in patients with cardiac amyloidosis. • In CT and cardiac MR, the myocardial extracellular volume correlated well with high-sensitivity cardiac troponin T level, global longitudinal strain, and left ventricular ejection fraction. • CT myocardial extracellular volume can be an imaging biomarker and alternative to cardiac MR myocardial extracellular volume.

Preliminary assessment of automated radiology report generation with generative pre-trained transformers: comparing results to radiologist-generated reports.

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.

The Feasibility of Using a Deep Learning-Based Model to Determine Cardiac Computed Tomographic Contrast Dose.

PURPOSE: This study aimed to predict contrast effects in cardiac computed tomography (CT) from CT localizer radiographs using a deep learning (DL) model and to compare the prediction performance of the DL model with that of conventional models based on patients' physical size. METHODS: This retrospective study included 473 (256 men and 217 women) cardiac CT scans between May 2014 and August 2017. We developed and evaluated DL models that predict milligrams of iodine per enhancement of the aorta from CT localizer radiographs. To assess the model performance, we calculated and compared Pearson correlation coefficient ( r ) between the actual iodine dose that was necessary to obtain a contrast effect of 1 HU (iodine dose per contrast effect [IDCE]) and IDCE predicted by DL, body weight, lean body weight, and body surface area of patients. RESULTS: The model was tested on 52 cases for the male group (mean [SD] age, 63.7 ± 11.4) and 44 cases for the female group (mean [SD] age, 69.8 ± 11.6). Correlation coefficients between the actual and predicted IDCE were 0.607 for the male group and 0.412 for the female group, which were higher than the correlation coefficients between the actual IDCE and body weight (0.539 for male, 0.290 for female), lean body weight (0.563 for male, 0.352 for female), and body surface area (0.587 for male, 0.349 for female). CONCLUSIONS: The performance for predicting contrast effects by analyzing CT localizer radiographs with the DL model was at least comparable with conventional methods using the patient's body size, notwithstanding that no additional measurements other than CT localizer radiographs were required.

Pulmonary arterial hypertension associated with portal hypertension: Noninvasive comprehensive assessment using computed tomography.

Pulmonary arterial hypertension associated with portal hypertension, known as portopulmonary hypertension (PoPH) is one of the important and serious pulmonary complications in patients with portal hypertension. Although there are a large number of patients with portal hypertension due to mainly liver cirrhosis, the number of cases diagnosed with PoPH are far fewer because the causes of dyspnea in patients with cirrhosis are diverse and the disease entity of PoPH is poorly recognized by clinicians. We report here the case with PoPH suggested and assessed comprehensively by dual energy computed tomography (CT) including high-resolution pulmonary CT angiography, pulmonary perfusion imaging, myocardial late iodine enhancement imaging, and myocardial extracellular volume analysis. This refined CT imaging protocol can be used in conjunction with standard chest evaluation and offers a practical and useful approach for the noninvasive "one-stop shop" evaluation of PoPH.

Exploring the impact of super-resolution deep learning on MR angiography image quality.

PURPOSE: The aim of this study is to assess the effect of super-resolution deep learning-based reconstruction (SR-DLR), which uses k-space properties, on image quality of intracranial time-of-flight (TOF) magnetic resonance angiography (MRA) at 3 T. METHODS: This retrospective study involved 35 patients who underwent intracranial TOF-MRA using a 3-T MRI system with SR-DLR based on k-space properties in October and November 2022. We reconstructed MRA with SR-DLR (matrix = 1008 × 1008) and MRA without SR-DLR (matrix = 336 × 336). We measured the signal-to-noise ratio (SNR), contrast, and contrast-to-noise ratio (CNR) in the basilar artery (BA) and the anterior cerebral artery (ACA) and the sharpness of the posterior cerebral artery (PCA) using the slope of the signal intensity profile curve at the half-peak points. Two radiologists evaluated image noise, artifacts, contrast, sharpness, and overall image quality of the two image types using a 4-point scale. We compared quantitative and qualitative scores between images with and without SR-DLR using the Wilcoxon signed-rank test. RESULTS: The SNRs, contrasts, and CNRs were all significantly higher in images with SR-DLR than those without SR-DLR (p < 0.001). The slope was significantly greater in images with SR-DLR than those without SR-DLR (p < 0.001). The qualitative scores in MRAs with SR-DLR were all significantly higher than MRAs without SR-DLR (p < 0.001). CONCLUSION: SR-DLR with k-space properties can offer the benefits of increased spatial resolution without the associated drawbacks of longer scan times and reduced SNR and CNR in intracranial MRA.

Feasibility study of super-resolution deep learning-based reconstruction using k-space data in brain diffusion-weighted images.

PURPOSE: The purpose of this study is to evaluate the influence of super-resolution deep learning-based reconstruction (SR-DLR), which utilizes k-space data, on the quality of images and the quantitation of the apparent diffusion coefficient (ADC) for diffusion-weighted images (DWI) in brain magnetic resonance imaging (MRI). METHODS: A retrospective analysis was performed on 34 patients who had undergone DWI using a 3 T MRI system with SR-DLR reconstruction based on k-space data in August 2022. DWI was reconstructed with SR-DLR (Matrix = 684 × 684) and without SR-DLR (Matrix = 228 × 228). Measurements were made of the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) in white matter (WM) and grey matter (GM), and the full width at half maximum (FWHM) of the septum pellucidum. Two radiologists assessed image noise, contrast, artifacts, blur, and the overall quality of three image types using a four-point scale. Quantitative and qualitative scores between images with and without SR-DLR were compared using the Wilcoxon signed-rank test. RESULTS: Images with SR-DLR showed significantly higher SNRs and CNRs than those without SR-DLR (p < 0.001). No statistically significant variances were found in the apparent diffusion coefficients (ADCs) in WM and GM between images with and without SR-DLR (ADC in WM, p = 0.945; ADC in GM, p = 0.235). Moreover, the FWHM without SR-DLR was notably lower compared to that with SR-DLR (p < 0.001). CONCLUSION: SR-DLR has the potential to augment the quality of DWI in DL MRI scans without significantly impacting ADC quantitation.