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.

A rare incidence of a hepatic artery pseudoaneurysm following plastic biliary stent insertion.

Hepatic artery pseudoaneurysms have been reported to occur in approximately 1% of cases after metal stenting for malignant biliary obstruction. In contrast, only a few cases have been reported as complications after plastic stenting for benign biliary disease. We report a 61-year-old man with cholangitis who presented with a rare complication of hemobilia after implantation of 7 Fr double pigtail plastic biliary stents. No bleeding was observed approximately one month after biliary stent tube removal. Contrast-enhanced CT scan revealed a circularly enhanced lesion (5 mm in diameter) in the arterial phase at the tip of the previously inserted plastic bile duct stent. Color Doppler ultrasonography enhanced the lesion and detected arterial blood flow inside. He was diagnosed with a hepatic artery pseudoaneurysm. However, he had no risk factors such as prolonged catheterization, severe cholangitis, liver abscess, or long-term steroid use. Superselective transarterial embolization using two metal microcoils was successfully completed without damage to the surrounding liver parenchyma. If hemobilia is suspected after insertion of a plastic bile duct stent, immediate monitoring using contrast-enhanced computed tomography or Doppler ultrasonography is recommended.

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 mm3) in addition to conventional MRA (spatial resolution, 0.39 × 0.89 × 1 mm3). 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.

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.

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.

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.

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.

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.

Durable Stable Disease by Atezolizumab/Bevacizumab Can Provide Long-term Survival of Patients With Hepatocellular Carcinoma Lung Metastases.

BACKGROUND: Multiple bilateral lung metastases secondary to hepatocellular carcinoma (HCC) are mainly treated with molecular therapy. Atezolizumab plus bevacizumab can provide excellent long-term survival for patients with a good response. CASE REPORT: A 67-year-old woman underwent right hepatectomy for a primary solitary HCC, 11 cm in diameter, after portal embolization. After 2 years, she developed bilateral lung metastases with >100 nodules, <1 cm in size. She had no viral hepatitis or liver cirrhosis, and the Child-Pugh Grade was A (5 points). Lenvatinib (12 mg daily) was administered as a first-line treatment and continued for 18 months. The best response was stable disease (SD). Subsequently, intravenous atezolizumab (1,200 mg) plus bevacizumab (15 mg/kg) was administered once every three weeks. The best response was SD, which continued for 26 months. After that, cabozantinib treatment was initiated and discontinued after one cycle. Subsequently, dual immune checkpoint inhibitor treatment (durvalumab + tremelimumab) was administered. She has had multiple, but lung-only, metastases over four years. She has been well as an outpatient with the Child-Pugh Grade of A and a performance status of 0. CONCLUSION: Even if atezolizumab plus bevacizumab does not induce a good response, a durable SD could prolong survival in patients with metastatic HCC while maintaining liver function and a good quality-of-life.

Improving image quality with super-resolution deep-learning-based reconstruction in coronary CT angiography.

OBJECTIVES: To evaluate the effect of super-resolution deep-learning-based reconstruction (SR-DLR) on the image quality of coronary CT angiography (CCTA). METHODS: Forty-one patients who underwent CCTA using a 320-row scanner were retrospectively included. Images were reconstructed with hybrid (HIR), model-based iterative reconstruction (MBIR), normal-resolution deep-learning-based reconstruction (NR-DLR), and SR-DLR algorithms. For each image series, image noise, and contrast-to-noise ratio (CNR) at the left main trunk, right coronary artery, left anterior descending artery, and left circumflex artery were quantified. Blooming artifacts from calcified plaques were measured. Image sharpness, noise magnitude, noise texture, edge smoothness, overall quality, and delineation of the coronary wall, calcified and noncalcified plaques, cardiac muscle, and valves were subjectively ranked on a 4-point scale (1, worst; 4, best). The quantitative parameters and subjective scores were compared among the four reconstructions. Task-based image quality was assessed with a physical evaluation phantom. The detectability index for the objects simulating the coronary lumen, calcified plaques, and noncalcified plaques was calculated from the noise power spectrum (NPS) and task-based transfer function (TTF). RESULTS: SR-DLR yielded significantly lower image noise and blooming artifacts with higher CNR than HIR, MBIR, and NR-DLR (all p < 0.001). The best subjective scores for all the evaluation criteria were attained with SR-DLR, with significant differences from all other reconstructions (p < 0.001). In the phantom study, SR-DLR provided the highest NPS average frequency, TTF50%, and detectability for all task objects. CONCLUSION: SR-DLR considerably improved the subjective and objective image qualities and object detectability of CCTA relative to HIR, MBIR, and NR-DLR algorithms. CLINICAL RELEVANCE STATEMENT: The novel SR-DLR algorithm has the potential to facilitate accurate assessment of coronary artery disease on CCTA by providing excellent image quality in terms of spatial resolution, noise characteristics, and object detectability. KEY POINTS: • SR-DLR designed for CCTA improved image sharpness, noise property, and delineation of cardiac structures with reduced blooming artifacts from calcified plaques relative to HIR, MBIR, and NR-DLR. • In the task-based image-quality assessments, SR-DLR yielded better spatial resolution, noise property, and detectability for objects simulating the coronary lumen, coronary calcifications, and noncalcified plaques than other reconstruction techniques. • The image reconstruction times of SR-DLR were shorter than those of MBIR, potentially serving as a novel standard-of-care reconstruction technique for CCTA performed on a 320-row CT scanner.

Accelerating TOF-MRA: The impact of the combined use of compressed sensitivity encoding and spiral imaging.

PURPOSE: To evaluate the image quality of the combined technique of compressed sensitivity encoding (CS) and spiral imaging in time-of-flight magnetic resonance angiography (TOF-MRA), which is approximately 2.5 times faster than conventional methods. METHODS: Twenty volunteers underwent four TOF-MRA sequences: sensitivity encoding (SENSE) with acceleration factor of 4 (acquisition time: 4:55 min), CS with acceleration factor of 10.9, and spiral and CS-spiral (both 1:55 min). A quantitative image analysis (signal-to-noise ratio [SNR], contrast, and full width at half maximum [FWHM] edge criterion measurements) was performed on four TOF sequences. For qualitative image analysis, two board-certified radiologists evaluated the overall depiction of the proximal, intermediate, and distal branches in CS, spiral, and CS-spiral images using SENSE as a reference. RESULTS: The SNR of BA in spiral and CS-spiral imaging was significantly lower than that in SENSE (p = 0.009). The contrasts of ACA and BA in CS-spiral were significantly higher and those in spiral were significantly lower than those in SENSE (p < 0.001). The FWHM in the CS image was significantly higher than that of SENSE; however, no significant differences were observed between the spiral or CS-spiral and SENSE. In qualitative analysis, the depiction of proximal vascular branches was significantly impaired in spiral than in others and that of distal vascular branches was significantly impaired in CS than in others (p < 0.001). CONCLUSIONS: In TOF-MRA, which is approximately 2.5 times faster than conventional methods, the combined use of CS and spiral imaging demonstrated an improvement in image quality compared to either CS or spiral imaging alone. SUMMARY STATEMENT: The image quality of Compressed SENSE and spiral imaging is particularly poor in the proximal and distal vascular branches, respectively at an extremely high acceleration factor; however, CS-spiral provided stable image quality in all regions as compared with the SENSE technique.

Noise power spectrum properties of deep learning-based reconstruction and iterative reconstruction algorithms: Phantom and clinical study.

PURPOSE: To compare the noise power spectrum (NPS) properties and perform a qualitative analysis of hybrid iterative reconstruction (IR), model-based IR (MBIR), and deep learning-based reconstruction (DLR) at a similar noise level in clinical study and compare these outcomes with those in phantom study. METHODS: A Catphan phantom with an external body ring was used in the phantom study. In the clinical study, computed tomography (CT) examination data of 34 patients were reviewed. NPS was calculated from DLR, hybrid IR, and MBIR images. The noise magnitude ratio (NMR) and the central frequency ratio (CFR) were calculated from DLR, hybrid IR, and MBIR images relative to filtered back-projection images using NPS. Clinical images were independently reviewed by two radiologists. RESULTS: In the phantom study, DLR with a mild level had a similar noise level as hybrid IR and MBIR with strong levels. In the clinical study, DLR with a mild level had a similar noise level as hybrid IR with standard and MBIR with strong levels. The NMR and CFR were 0.40 and 0.76 for DLR, 0.42 and 0.55 for hybrid IR, and 0.48 and 0.62 for MBIR. The visual inspection of the clinical DLR image was superior to that of the hybrid IR and MBIR images. CONCLUSION: Deep learning-based reconstruction improves overall image quality with substantial noise reduction while maintaining image noise texture compared with the CT reconstruction techniques.

Coronary Stent Evaluation by CTA: Image Quality Comparison Between Super-Resolution Deep Learning Reconstruction and Other Reconstruction Algorithms.

BACKGROUND. A super-resolution deep learning reconstruction (SR-DLR) algorithm may provide better image sharpness than earlier reconstruction algorithms and thereby improve coronary stent assessment on coronary CTA. OBJECTIVE. The purpose of our study was to compare SR-DLR and other reconstruction algorithms in terms of image quality measures related to coronary stent evaluation in patients undergoing coronary CTA. METHODS. This retrospective study included patients with at least one coronary artery stent who underwent coronary CTA between January 2020 and December 2020. Examinations were performed using a 320-row normal-resolution scanner and were reconstructed with hybrid iterative reconstruction (HIR), model-based iterative reconstruction (MBIR), normal-resolution deep learning reconstruction (NR-DLR), and SR-DLR algorithms. Quantitative image quality measures were determined. Two radiologists independently reviewed images to rank the four reconstructions (4-point scale: 1 = worst reconstruction, 4 = best reconstruction) for qualitative measures and to score diagnostic confidence (5-point scale: score ≥ 3 indicating an assessable stent). The assessability rate was calculated for stents with a diameter of 3.0 mm or less. RESULTS. The sample included 24 patients (18 men, six women; mean age, 72.5 ± 9.8 [SD] years), with 51 stents. SR-DLR, in comparison with the other reconstructions, yielded lower stent-related blooming artifacts (median, 40.3 vs 53.4-58.2), stent-induced attenuation increase ratio (0.17 vs 0.27-0.31), and quantitative image noise (18.1 vs 20.9-30.4 HU) and higher in-stent lumen diameter (2.4 vs 1.7-1.9 mm), stent strut sharpness (327 vs 147-210 ΔHU/mm), and CNR (30.0 vs 16.0-25.6) (all p < .001). For both observers, all ranked measures (image sharpness; image noise; noise texture; delineation of stent strut, in-stent lumen, coronary artery wall, and calcified plaque surrounding the stent) and diagnostic confidence showed a higher score for SR-DLR (median, 4.0 for all features) than for the other reconstructions (range, 1.0-3.0) (all p < .001). The assessability rate for stents with a diameter of 3.0 mm or less (n = 37) was higher for SR-DLR (86.5% for observer 1 and 89.2% for observer 2) than for HIR (35.1% and 43.2%), MBIR (59.5% and 62.2%), and NR-DLR (62.2% and 64.9%) (all p < .05). CONCLUSION. SR-DLR yielded improved delineation of the stent strut and in-stent lumen, with better image sharpness and less image noise and blooming artifacts, in comparison with HIR, MBIR, and NR-DLR. CLINICAL IMPACT. SR-DLR may facilitate coronary stent assessment on a 320-row normal-resolution scanner, particularly for small-diameter stents.

Can Machine Learning Identify the Intravenous Contrast Dose and Injection Rate Needed for Optimal Enhancement on Dynamic Liver Computed Tomography?

OBJECTIVES: This study aimed to investigate whether machine learning (ML) is useful for predicting the contrast material (CM) dose required to obtain a clinically optimal contrast enhancement in hepatic dynamic computed tomography (CT). METHODS: We trained and evaluated ensemble ML regressors to predict the CM doses needed for optimal enhancement in hepatic dynamic CT using 236 patients for a training data set and 94 patients for a test data set. After the ML training, we randomly divided using the ML-based (n = 100) and the body weight (BW)-based protocols (n = 100) by the prospective trial. The BW protocol was performed using routine protocol (600 mg/kg of iodine) by the prospective trial. The CT numbers of the abdominal aorta and hepatic parenchyma, CM dose, and injection rate were compared between each protocol using the paired t test. Equivalence tests were performed with equivalent margins of 100 and 20 Hounsfield units for the aorta and liver, respectively. RESULTS: The CM dose and injection rate for the ML and BW protocols were 112.3 mL and 3.7 mL/s, and 118.0 mL and 3.9 mL/s ( P < 0.05). There were no significant differences in the CT numbers of the abdominal aorta and hepatic parenchyma between the 2 protocols ( P = 0.20 and 0.45). The 95% confidence interval for the difference in the CT number of the abdominal aorta and hepatic parenchyma between 2 protocols was within the range of predetermined equivalence margins. CONCLUSIONS: Machine learning is useful for predicting the CM dose and injection rate required to obtain the optimal clinical contrast enhancement for hepatic dynamic CT without reducing the CT number of the abdominal aorta and hepatic parenchyma.

Cardiac MRI-derived Extracellular Volume Fraction versus Myocardium-to-Lumen R1 Ratio at Postcontrast T1 Mapping for Detecting Cardiac Amyloidosis.

Purpose: To evaluate the diagnostic performance of myocardium-to-lumen R1 (1/T1) ratio on postcontrast T1 maps for the detection of cardiac amyloidosis in a large patient sample. Materials and Methods: This retrospective study included consecutive patients who underwent MRI-derived extracellular volume fraction (MRI ECV) analysis between March 2017 and July 2021 because of known or suspected heart failure or cardiomyopathy. Pre- and postcontrast T1 maps were generated using the modified Look-Locker inversion recovery sequence. Diagnostic performances of MRI ECV and myocardium-to-lumen R1 ratio on postcontrast T1 maps (a simplified index not requiring a native T1 map and hematocrit level data) for detecting cardiac amyloidosis were evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. Results: Of 352 patients (mean age, 63 years ± 16 [SD]; 235 men), 136 had cardiac amyloidosis. MRI ECV showed 89.0% (121 of 136; 95% CI: 82%, 94%) sensitivity and 98.6% (213 of 216; 95% CI: 96%, 100%) specificity for helping detect cardiac amyloidosis (cutoff value of 40% [AUC, 0.99 {95% CI: 0.97, 1.00}; P < .001]). Postcontrast myocardium-to-lumen R1 ratio showed 92.6% (126 of 136; 95% CI: 89%, 96%) sensitivity and 93.1% (201 of 216; 95% CI: 89%, 96%) specificity (cutoff value of 0.84 [AUC, 0.98 {95% CI: 0.95, 0.99}; P < .001]). There was no evidence of a difference in AUCs for each parameter (P = .10). Conclusion: Postcontrast myocardium-to-lumen R1 ratio showed excellent diagnostic performance comparable to that of MRI ECV in the detection of cardiac amyloidosis.Keywords: MR Imaging, Cardiac, Heart, Cardiomyopathies Supplemental material is available for this article. © RSNA, 2023.

Deep learning-based reconstruction and 3D hybrid profile order technique for MRCP at 3T: evaluation of image quality and acquisition time.

OBJECTIVES: To evaluate the image quality of the 3D hybrid profile order technique and deep-learning-based reconstruction (DLR) for 3D magnetic resonance cholangiopancreatography (MRCP) within a single breath-hold (BH) at 3 T magnetic resonance imaging (MRI). METHODS: This retrospective study included 32 patients with biliary and pancreatic disorders. BH images were reconstructed with and without DLR. The signal-to-noise ratio (SNR), contrast, contrast-to-noise ratio (CNR) between the common bile duct (CBD) and periductal tissues, and full width at half maximum (FWHM) of CBD on 3D-MRCP were evaluated quantitatively. Two radiologists scored image noise, contrast, artifacts, blur, and overall image quality of the three image types using a 4-point scale. Quantitative and qualitative scores were compared using the Friedman test and post hoc Nemenyi test. RESULTS: The SNR and CNR were not significantly different when under respiratory gating- and BH-MRCP without DLR. However, they were significantly higher under BH with DLR than under respiratory gating (SNR, p = 0.013; CNR, p = 0.027). The contrast and FWHM of MRCP under BH with and without DLR were lower than those under respiratory gating (contrast, p < 0.001; FWHM, p = 0.015). Qualitative scores for noise, blur, and overall image quality were higher under BH with DLR than those under respiratory gating (blur, p = 0.003; overall, p = 0.008). CONCLUSIONS: The combination of the 3D hybrid profile order technique and DLR is useful for MRCP within a single BH and does not lead to the deterioration of image quality and space resolution at 3 T MRI. CLINICAL RELEVANCE STATEMENT: Considering its advantages, this sequence might become the standard protocol for MRCP in clinical practice, at least at 3.0 T. KEY POINTS: • The 3D hybrid profile order can achieve MRCP within a single breath-hold without a decrease in spatial resolution. • The DLR significantly improved the CNR and SNR of BH-MRCP. • The 3D hybrid profile order technique with DLR reduces the deterioration of image quality in MRCP within a single breath-hold.

Combination Use of Compressed Sensing and Deep Learning for Shoulder Magnetic Resonance Imaging With Various Sequences.

OBJECTIVE: For compressed sensing (CS) to become widely used in routine magnetic resonance imaging (MRI), it is essential to improve image quality. This study aimed to evaluate the usefulness of combining CS and deep learning-based reconstruction (DLR) for various sequences in shoulder MRI. METHODS: This retrospective study included 37 consecutive patients who underwent undersampled shoulder MRIs, including T1-weighted (T1WI), T2-weighted (T2WI), and fat-saturation T2-weighted (FS-T2WI) images. Images were reconstructed using the conventional wavelet-based denoising method (wavelet method) and a combination of wavelet and DLR-based denoising methods (hybrid-DLR method) for each sequence. The signal-to-noise ratio and contrast-to-noise ratio of the bone, muscle, and fat and the full width at half maximum of the shoulder joint were compared between the 2 image types. In addition, 2 board-certified radiologists scored the image noise, contrast, sharpness, artifacts, and overall image quality of the 2 image types on a 4-point scale. RESULTS: The signal-to-noise ratios and contrast-to-noise ratios of the bone, muscle, and fat in T1WI, T2WI, and FS-T2WI obtained from the hybrid-DLR method were significantly higher than those of the conventional wavelet method ( P < 0.001). However, there were no significant differences in the full width at half maximum of the shoulder joint in any of the sequences ( P > 0.05). Furthermore, in all sequences, the mean scores of the image noise, sharpness, artifacts, and overall image quality were significantly higher in the hybrid-DLR method than in the wavelet method ( P < 0.001), but there were no significant differences in contrast among the sequences ( P > 0.05). CONCLUSIONS: The DLR denoising method can improve the image quality of CS in T1-weighted images, T2-weighted images, and fat-saturation T2-weighted images of the shoulder compared with the wavelet denoising method alone.

Deep learning-based reconstruction can improve the image quality of low radiation dose head CT.

OBJECTIVES: To evaluate the image quality of deep learning-based reconstruction (DLR), model-based (MBIR), and hybrid iterative reconstruction (HIR) algorithms for lower-dose (LD) unenhanced head CT and compare it with those of standard-dose (STD) HIR images. METHODS: This retrospective study included 114 patients who underwent unenhanced head CT using the STD (n = 57) or LD (n = 57) protocol on a 320-row CT. STD images were reconstructed with HIR; LD images were reconstructed with HIR (LD-HIR), MBIR (LD-MBIR), and DLR (LD-DLR). The image noise, gray and white matter (GM-WM) contrast, and contrast-to-noise ratio (CNR) at the basal ganglia and posterior fossa levels were quantified. The noise magnitude, noise texture, GM-WM contrast, image sharpness, streak artifact, and subjective acceptability were independently scored by three radiologists (1 = worst, 5 = best). The lesion conspicuity of LD-HIR, LD-MBIR, and LD-DLR was ranked through side-by-side assessments (1 = worst, 3 = best). Reconstruction times of three algorithms were measured. RESULTS: The effective dose of LD was 25% lower than that of STD. Lower image noise, higher GM-WM contrast, and higher CNR were observed in LD-DLR and LD-MBIR than those in STD (all, p ≤ 0.035). Compared with STD, the noise texture, image sharpness, and subjective acceptability were inferior for LD-MBIR and superior for LD-DLR (all, p < 0.001). The lesion conspicuity of LD-DLR (2.9 ± 0.2) was higher than that of HIR (1.2 ± 0.3) and MBIR (1.8 ± 0.4) (all, p < 0.001). Reconstruction times of HIR, MBIR, and DLR were 11 ± 1, 319 ± 17, and 24 ± 1 s, respectively. CONCLUSION: DLR can enhance the image quality of head CT while preserving low radiation dose level and short reconstruction time. KEY POINTS: • For unenhanced head CT, DLR reduced the image noise and improved the GM-WM contrast and lesion delineation without sacrificing the natural noise texture and image sharpness relative to HIR. • The subjective and objective image quality of DLR was better than that of HIR even at 25% reduced dose without considerably increasing the image reconstruction times (24 s vs. 11 s). • Despite the strong noise reduction and improved GM-WM contrast performance, MBIR degraded the noise texture, sharpness, and subjective acceptance with prolonged reconstruction times relative to HIR, potentially hampering its feasibility.