Hybrid deep-learning-based denoising method for compressed sensing in pituitary MRI: comparison with the conventional wavelet-based denoising method.

OBJECTIVES: This study aimed to evaluate the efficacy of a combined wavelet and deep-learning reconstruction (DLR) method for under-sampled pituitary MRI. METHODS: This retrospective study included 28 consecutive patients who underwent under-sampled pituitary T2-weighted images (T2WI). Images were reconstructed using either the conventional wavelet denoising method (wavelet method) or the wavelet and DLR methods combined (hybrid DLR method) at five denoising levels. The signal-to-noise ratio (SNR) of the CSF, hypothalamic, and pituitary images and the contrast between structures were compared between the two image types. Noise quality, contrast, sharpness, artifacts, and overall image quality were evaluated by two board-certified radiologists. The quantitative and the qualitative analyses were performed with robust two-way repeated analyses of variance. RESULTS: Using the hybrid DLR method, the SNR of the CSF progressively increased as denoising levels increased. By contrast, with the wavelet method, the SNR of the CSF, hypothalamus, and pituitary did not increase at higher denoising levels. There was a significant main effect of denoising methods (p < 0.001) and denoising levels (p < 0.001), and an interaction between denoising methods and denoising levels (p < 0.001). For all five qualitative scores, there was a significant main effect of denoising methods (p < 0.001) and an interaction between denoising methods and denoising levels (p < 0.001). CONCLUSIONS: The hybrid DLR method can provide higher image quality for T2WI of the pituitary with compressed sensing (CS) than the wavelet method alone, especially at higher denoising levels. KEY POINTS: • The signal-to-noise ratios of cerebrospinal fluid progressively increased with the hybrid DLR method, with an increase in the denoising level for cerebrospinal fluid in pituitary T2WI with CS. • The signal-to-noise ratios of cerebrospinal fluid using the conventional wavelet method did not increase at higher denoising levels. • All qualitative scores of hybrid deep-learning reconstructions at all denoising levels were higher than those for the wavelet denoising method.

Evaluating of the Quality of Hepatic Diffusion Weighted Imaging Using Multiband Imaging With Variable-Rate Selective Excitation.

OBJECTIVE: To assess the image quality of diffusion-weighted imaging (DWI) using multiband (MB) imaging with variable-rate selective excitation (VERSE) and compare it to conventional DWI. METHODS: We retrospectively evaluated hepatic DWI images of patients (n = 76) according to either the conventional method (SENSE, acceleration factor = 2) (n = 38) or fast scanning method (MB imaging with VERSE, acceleration factor = 2 × 2) (n = 38). We also conducted a volunteer study (n = 15) for those scanning methods. During quantitative analysis, the signal-to-noise ratio (SNR), apparent diffusion coefficient values, and contrast in the liver, spleen, and spinal cord were compared between the 2 groups. During qualitative analysis, all images were independently and blindly evaluated by 2 board-certified radiologists. The image contrast, noise, artifacts, and sharpness were assessed, and the performance of classification was measured using receiver operating characteristic curve analysis. RESULTS: In the retrospective study, the SNRs of the hepatic parenchyma and spinal cord between the 2 protocols were significantly different (liver, 8.9 [interquartile range {IQR}, 7.6-12.2] vs 13.0 [IQR, 10.0-16.7]; P < 0.001 and spinal cord, 6.0 [IQR, 4.7-9.4] vs 4.3 [IQR, 3.8-6.8]; P < 0.02). No significant differences between the 2 protocols in the other retrospective analyses were noted. In the receiver operating characteristic curve analysis, area under the curve was 0.49 (95% confidence intervals, 0.40-0.58). CONCLUSION: Multiband VERSE reduced scan time and SNR of hepatic DWI; however, subjective image quality parameters were not significantly impacted.

Unenhanced Dual-Layer Spectral-Detector CT for Characterizing Indeterminate Adrenal Lesions.

Background Unenhanced dual-layer spectral-detector CT may facilitate adrenal lesion characterization; however, no studies have evaluated its incremental diagnostic yield for indeterminate lesions (unenhanced attenuation >10 HU) in comparison to that with conventional unenhanced CT. Purpose To determine whether spectral attenuation analysis improves characterization of lipid-poor adrenal adenomas from nonadenomas compared to that with mean attenuation and histogram analysis of conventional CT images. Materials and Methods This retrospective study included patients with indeterminate adrenal lesions who underwent unenhanced dual-layer spectral-detector CT between March 2018 and June 2020. Mean attenuation on conventional 120-kVp images (HUconv), histogram-based percentage negative pixels (proportion of all pixels <0 HU) on conventional 120-kVp images, and mean attenuation on virtual monoenergetic images (VMIs) at 40-140 keV were measured for each lesion. The attenuation difference between virtual monoenergetic 140- and 40-keV images (ΔHU; ie, Hounsfield unit at 140 keV - Hounsfield unit at 40 keV) and ΔHU indexed with HUconv (ΔHU index; ie, ΔHU/HUconv × 100) were calculated. Conventional and virtual monoenergetic imaging parameters were compared between lipid-poor adenomas and nonadenomas by using the Mann-Whitney U test. Receiver operating characteristic analysis was performed to determine the sensitivity for attaining at least 95% specificity in characterizing adenomas from nonadenomas; sensitivity was compared by using the McNemar test. Results A total of 232 patients (mean age ± standard deviation, 67 years ± 11; 145 men) with 129 lipid-poor adenomas and 103 nonadenomas were evaluated. HUconv and mean attenuation on VMIs at 40-140 keV were lower and the percentage negative pixels, ΔHU, and ΔHU index higher in lipid-poor adenomas than in nonadenomas (P < .001 for all). Attenuation differences between adenomas and nonadenomas on VMIs were maximal at 40 keV (23 HU at 40 keV vs 5 HU at 140 keV). The highest sensitivities for differentiating adenomas and nonadenomas were achieved for virtual monoenergetic ΔHU index (77% [99 of 129 adenomas]), attenuation on 40-keV images (71% [91 of 129 adenomas]), and ΔHU (67% [87 of 129 adenomas]) compared to HUconv (35% [45 of 129 adenomas]) and percentage negative pixels (30% [39 of 129 adenomas]) (P < .001 for all; specificity, 95% [98 of 103 nonadenomas]). Conclusion Spectral attenuation analysis enabled differentiation of lipid-poor adenomas from nonadenomas with higher sensitivity than mean attenuation or histogram analysis of conventional CT images. © RSNA, 2021 Online supplemental material is available for this article.

Relative Enhancement Ratio of Portal Venous Phase to Unenhanced CT in the Diagnosis of Lipid-poor Adrenal Adenomas.

Background The development of an accurate, practical, noninvasive, and widely available diagnostic approach to characterize lipid-poor adrenal lesions (greater than 10 HU at unenhanced CT) remains an ongoing demand. Purpose To investigate whether combined assessment of unenhanced and portal venous phase CT allows for the differentiation of lipid-poor adrenal adenomas from nonadenomas. Materials and Methods Patients with lipid-poor adrenal lesions who underwent unenhanced and portal venous phase CT with a single-energy scanner between January 2016 and March 2020 were identified retrospectively. For each lesion, the unenhanced and contrast-enhanced attenuation were measured; the absolute enhancement (contrast-enhanced minus unenhanced attenuation [HU]) and relative enhancement ratio ([absolute enhancement divided by unenhanced attenuation] × 100%) were calculated. The sensitivity achieved at 95% specificity to distinguish adenomas from nonadenomas was determined with receiver operating characteristic curve analysis and compared among parameters with use of the McNemar test. Results A total of 220 patients (mean age ± standard deviation, 66 years ± 12; 134 men) with 131 lipid-poor adenomas and 89 nonadenomas were analyzed. The sensitivity (achieved at 95% specificity) of the relative enhancement ratio (86% [113 of 131 adenomas; 95% CI: 79, 92] at a threshold of >210%) was higher than that of unenhanced attenuation (50% [66 of 131 adenomas; 95% CI: 42, 59] at a threshold of ≤21 HU), contrast-enhanced attenuation (3% [four of 131 adenomas; 95% CI: 1, 8] at a threshold of >120 HU), and absolute enhancement (24% [32 of 131 adenomas; 95% CI: 17, 33] at a threshold of >74 HU; all P < .001). The sensitivities of the relative enhancement ratio were 100% (58 of 58 adenomas; 95% CI: 94, 100), 83% (52 of 63 adenomas; 95% CI: 71, 91), and 30% (three of 10 adenomas; 95% CI: 7, 65) for adenomas measuring unenhanced attenuation of more than 10 HU up to 20 HU, 21-30 HU, and more than 30 HU, respectively. Conclusion A relative enhancement ratio threshold of greater than 210%, measured at unenhanced and portal venous phase CT, accurately differentiated lipid-poor adenomas from nonadenomas, particularly for lesions with unenhanced attenuation of 10-30 HU. © RSNA, 2021 Online supplemental material is available for this article.

Liver fibrosis assessment with multiphasic dual-energy CT: diagnostic performance of iodine uptake parameters.

OBJECTIVES: To evaluate the ability of iodine uptake parameters from hepatic multiphasic CT to predict liver fibrosis, and compare absolute contrast enhancement (ΔHU) with dual-energy iodine density (ID) methods. METHODS: One hundred seventeen patients with pathologically proven liver fibrosis who underwent dual-energy CT during portal-venous phase (PVP) and 3-min delayed phase (DP) between January 2017 and Octotber 2019 were retrospectively included. Two radiologists measured the hepatic and blood-pool iodine uptake using ΔHU and ID methods; extracellular volume fraction (ECV) and the iodine washout rate (IWR) calculated with both methods were compared between different fibrosis stages (F0-1 vs. F2-4, F0-2 vs. F3-4, or F0-3 vs. F4). The inter-observer reproducibility (intraclass correlation coefficients [ICCs]) for ECV and IWR was compared between the ΔHU and ID methods. The areas under the receiver operating characteristic curves (AUCs) to predict liver fibrosis severity were calculated for serum and imaging fibrosis markers. To identify independent predictors, multivariable logistic regression analysis was performed, and combined performance was assessed for the ΔHU and ID models. RESULTS: Patients with F ≥ 2 (n = 70), F ≥ 3 (n = 51), and F4 (n = 29) had higher ECV and lower IWR than those with F ≤ 1, F ≤ 2, and F ≤ 3, respectively (all p < 0.001). ICCs were higher in the ID method than in the ΔHU method (ECV: p = 0.045; IWR: p < 0.001). The AUC ranges of ECVΔHU, ECVID, IWRΔHU, and IWRID for predicting liver fibrosis severity were 0.65-0.71, 0.67-0.73, 0.76-0.81, and 0.81-0.85, respectively. IWR and fibrosis-4 index were independent predictors, with combined AUCs of 0.82-0.87 for the ΔHU model and 0.86-0.89 for the ID model. CONCLUSIONS: IWR more accurately predicted liver fibrosis than ECV in routine multiphasic CT. The dual-energy ID method yielded higher inter-observer reproducibility and predictive values than the single-energy ΔHU method. KEY POINTS: • The IWR calculated from hepatic iodine uptake during PVP and 3-min DP predicted liver fibrosis (AUC, 0.76-0.85), while the ECV had a relatively limited predictive value (ACU, 0.65-0.73). • Compared with the conventional ΔHU method, the dual-energy ID method provided superior inter-observer reproducibility for measurement of ECV (p = 0.046) and IWR (p < 0.001). • The IWR and FIB-4 served as independent predictors of liver fibrosis; their combination yielded the high diagnostic performance particularly when using the ID method (combined AUCs of 0.86-0.89).

Adrenal Adenomas versus Metastases: Diagnostic Performance of Dual-Energy Spectral CT Virtual Noncontrast Imaging and Iodine Maps.

Background Dual-energy CT allows virtual noncontrast (VNC) attenuation and iodine density measurements from contrast material-enhanced examination, potentially enabling adrenal lesion characterization. However, data regarding diagnostic performance remain limited, and combined diagnostic values have never been investigated. Purpose To determine whether VNC attenuation, iodine density, and combination of the two allow reliable differentiation between adrenal adenomas and metastases. Materials and Methods This retrospective study included patients with adrenal lesions who underwent unenhanced and portal venous phase dual-energy CT between January 2017 and December 2018. Unenhanced, contrast-enhanced, and VNC attenuation, as well as iodine density, were measured for each lesion. Agreement between unenhanced and VNC attenuation was assessed by using Wilcoxon rank-sum test, Pearson correlation coefficient, and Bland-Altman plot. The ratio of iodine density to VNC attenuation was calculated for lesions with positive VNC attenuation. Each parameter was compared between adenomas and metastases; diagnostic performance was evaluated by using the area under the receiver operating characteristic curve (AUC) with sensitivity and specificity. Results A total of 149 patients (mean age, 65 years ± 13 [standard deviation]; 89 men; 98 patients with 104 adenomas; 51 patients with 56 metastases) were evaluated. VNC attenuation showed strong positive correlation with unenhanced attenuation (r = 0.92) but resulted in overestimates of adenoma attenuation (mean bias, +11 HU; P < .001) and was less sensitive (P = .03) in the diagnosis of adenomas compared with unenhanced attenuation (sensitivity of 79% [81 of 102] [95% confidence interval {CI}: 70%, 87%] and specificity of 95% [53 of 56] [95% CI: 85%, 99%] versus sensitivity of 85% [87 of 102] [95% CI: 77%, 92%] and specificity of 96% [54 of 56] [95% CI: 88%, 100%], with thresholds of ≤29 HU and ≤22 HU, respectively). Contrast-enhanced attenuation had no discriminatory ability (AUC, 0.54; 95% CI: 0.45, 0.62). Iodine density yielded moderate performance (sensitivity of 78% [80 of 102] [95% CI: 69%, 86%] and specificity of 71% [40 of 56] [95% CI: 58%, 83%], with a threshold of ≥1.82 mg/mL). The iodine-to-VNC ratio was higher in adenomas than in metastases (mean, 14.5 vs 4.6; P < .001), with sensitivity of 95% (97 of 102; 95% CI: 89%, 98%) and specificity of 95% (53 of 56; 95% CI: 85%, 99%), with a threshold of 6.7 or greater. Conclusion Contrast-enhanced dual-energy CT during the portal venous phase enabled accurate differentiation between adrenal adenomas and metastases by combining virtual noncontrast attenuation and iodine density. Virtual noncontrast imaging alone led to overestimates of adenoma attenuation, and iodine density alone had limited discriminatory utility. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Hindman and Megibow in this issue.

Dual-layer spectral CT improves image quality of multiphasic pancreas CT in patients with pancreatic ductal adenocarcinoma.

OBJECTIVES: To evaluate the image quality and optimal energies of virtual monoenergetic images (VMIs) from dual-layer spectral detector computed tomography (DLCT) in multiphasic pancreatic CT and investigate whether low-keV VMI at the portal venous phase (PVP) provides sufficient tumor conspicuity and arterial depiction relative to conventional pancreatic parenchymal phase (PPP) images. METHODS: Forty-eight patients with pancreatic ductal adenocarcinoma (PDAC) underwent contrast-enhanced DLCT during PPP and PVP. Conventional polyenergetic images (PEIs) and VMI at 40-100 keV (VMI40-100, 10-keV increments) were reconstructed at each enhancement phase. Image noise and the contrast-to-noise ratio (CNR) of the pancreas, tumors, arteries, and veins were quantified. Two radiologists independently assessed tumor conspicuity, margin delineation, image noise, sharpness of pancreatic duct, and depiction of arteries and veins on a five-point scale. Size-specific dose estimate (SSDE) was calculated. RESULTS: Image noise for VMI40-100 was significantly lower than that for PEI (p < 0.01). The CNR in VMI increased gradually with decreasing energy; CNRs for VMI40-60 were significantly greater than that for PEI (p < 0.01). All subjective VMI scores were maximized at VMI40, followed by VMI50-60, all of which were significantly better than of PEI (p < 0.01). Objective and subjective image qualities of VMI40-50 at the PVP were equivalent to or even better compared with conventional PPP images. No significant difference in SSDE was observed between phases (p = 0.10). CONCLUSIONS: DLCT-VMI improved the subjective and objective image quality in multiphasic pancreatic CT for patients with PDAC. Low-keV PVP imaging may yield diagnostically adequate tumor conspicuity and arterial assessment compared with polyenergetic PPP images. KEY POINTS: • Low-keV VMI from DLCT yields better subjective and objective image quality of multiphasic pancreas CT in comparison with conventional PEI for the assessment of pancreatic ductal adenocarcinoma. • Tumor conspicuity and depiction of peripancreatic vasculature were maximized at VMI 40without an increase in the image noise. • Low-keV VMI of the portal venous phase provides sufficient tumor conspicuity and arterial depiction, potentially allowing the early detection and local staging of PDAC on routine abdominal CT performed for various clinical indications.

Usefulness of Virtual Monochromatic Dual-Layer Computed Tomographic Imaging for Breast Carcinoma.

OBJECTIVE: This study aimed to evaluate virtual monochromatic images (VMIs) obtained using dual-layer dual-energy computed tomography (CT) for breast carcinoma. METHODS: We retrospectively enrolled 28 patients with breast cancer who were pathologically diagnosed using dual-layer dual-energy CT. Virtual monochromatic images (40-200 keV) were generated. We compared CT number, image noise, contrast, and contrast-to-noise ratio (CNR) between VMIs with the highest CNR and conventional CT images. We performed qualitative image analysis between VMIs at optimized energy and conventional CT images. RESULTS: Image noise of VMIs was not significantly different from that of the conventional CT images. As the x-ray energy decreased, CNR increased. The 40-keV VMIs were highest CNR and higher than that of the conventional CT images. In qualitative image analysis, the 40-keV images were significantly higher than conventional CT images. CONCLUSION: Both qualitative and quantitative analyses showed that the image quality of VMIs at 40 keV was significantly higher than that of conventional CT images.

Dual-Energy Computed Tomography for Evaluating Acute Brain Infarction of Middle Cerebral Artery Territories: Optimization of Voltage Settings in Virtual Monoenergetic Imaging.

OBJECTIVE: To evaluate the optimal virtual monochromatic energy in dual-energy computed tomography for differentiating between infarcted areas and normal brain parenchyma. METHODS: We enrolled 29 patients with middle cerebral artery acute brain infarction of who underwent examination by dual-energy computed tomography. We calculated the contrast-to-noise ratio (CNR) between white or gray matter and the infarcted area (CNR(W-I) and CNR(G-I), respectively) and normalized CNRs. From the normalized CNRs, we assessed which monochromatic energy gave the best balance between the infarcted area and normal brain parenchyma. The 70-keV images were used for comparison. RESULTS: The 99-keV images demonstrated the best balance between the infarction and normal brain parenchyma. In quantitative analysis, the 99-keV images were not inferior to the 70-keV images. (CNR(G-I), 1.92 ± 0.80 vs 2.00 ± 0.70, respectively [P = 0.16]; CNR(W-I), 0.52 ± 0.72 vs 0.40 ± 0.64, P < 0.01, respectively). CONCLUSIONS: Monochromatic 99-keV energy images may be optimal for evaluating middle cerebral artery acute brain infarction.

Magnetic resonance cholangiopancreatography with GRASE sequence at 3.0T: does it improve image quality and acquisition time as compared with 3D TSE?

OBJECTIVES: The current study evaluated the clinical usefulness of the gradient and spin-echo (GRASE) sequence with single breath-hold in 3.0 T magnetic resonance cholangiopancreatography (MRCP). We compared the acquisition time and image quality between GRASE and breath navigator-triggered 3D turbo spin echo (3D TSE). METHODS: We examined 54 consecutive patients who underwent MRCP with GRASE and 3D TSE. We compared the image acquisition time and contrast-to-noise ratio (CNR) between the common bile duct (CBD) and liver. Overall image quality, blurring, motion artifacts and CBD visibility were scored on a 4-point scale by two radiologists. Paired t-tests were used to compare the variables. RESULTS: The mean image acquisition time was 95 % shorter with the GRASE than with 3D TSE (GRASE: 20 s; 3D TSE: 6 min 27 s). The CNR of GRASE was significantly higher than that of 3D TSE (GRASE: 25.4 ± 13.9 vs. 3D TSE: 18.2 ± 9.6, p < 0.01). All qualitative scores for GRASE were significantly better than those for 3D TSE. CONCLUSIONS: 3.0 T MRCP with GRASE sequence with single breath-hold significantly improved the CNR of CBD with a 95 % shorter acquisition time compared with conventional 3D MRCP with 3D TSE. KEY POINTS: • MRCP acquisition time was 95% shorter with GRASE than with 3D TSE. • Overall image quality of GRASE was significantly better than 3D TSE. • Pancreaticobiliary tree visibility with GRASE was better than that with 3D TSE.

Brain computed tomography using iterative reconstruction to diagnose acute middle cerebral artery stroke: usefulness in combination of narrow window setting and thin slice reconstruction.

PURPOSE: The purpose of this study is to determine whether iterative model reconstruction (IMR) optimized for brain CT could improve the detection of acute stroke in the setting of thin image slices and narrow window settings. METHODS: We retrospectively reviewed 27 patients who presented acute middle cerebral artery (MCA) stroke. Images were reconstructed using filtered back projection (FBP; 1- and 5-mm slice thickness) and IMR (1 mm thickness), and contrast-to-noise ratios (CNRs) of infarcted and non-infarcted areas were compared. To analyze the performance of acute MCA stroke detection, we used receiver operating characteristic (ROC) curve techniques and compared 5-mm FBP with standard and narrow window settings, and 1-mm FBP and IMR with narrow window settings. RESULTS: The CNR in 1-mm IMR (1.1 ± 1.0) was significantly higher than in 5- (0.8 ± 0.7) and 1-mm FBP (0.4 ± 0.4) (p < 0.001). Furthermore, the average area under the ROC curve was significantly higher with 1-mm IMR with narrow window settings (0.90, 95% CI: 0.86, 0.94) than it was with 5-mm FBP (0.78, 95% CI: 0.72, 0.83). CONCLUSION: The combination of thin image slices and narrow window settings under IMR reconstruction provide better diagnostic performance for acute MCA stroke than conventional reconstruction methods.

Diagnosis of small posterior fossa stroke on brain CT: effect of iterative reconstruction designed for brain CT on detection performance.

OBJECTIVES: In this study, we aimed to determine whether iterative model reconstruction designed for brain CT (IMR-neuro) would improve the accuracy of posterior fossa stroke diagnosis on brain CT. METHODS: We enrolled 37 patients with ischaemic stroke in the posterior fossa and 37 patients without stroke (controls). Using axial images reconstructed using filtered back-projection (FBP) and IMR-neuro, we compared the CT numbers in infarcted areas, image noise in the pons, and contrast-to-noise ratios (CNRs) of infarcted and non-infarcted areas on scans subjected to IMR-neuro and FBP. To analyse the performance of hypo-attenuation detection, we used receiver-operating characteristic (ROC) curve techniques. RESULTS: The image noise was significantly lower (2.2 ± 0.5 vs. 5.1 ± 0.9 Hounsfield units, p < 0.01) and the difference in CNR between the infarcted and non-infarcted areas was significantly higher with IMR-neuro than with FBP (2.2 ± 1.7 vs. 4.0 ± 3.6, p < 0.01). Furthermore, the average area under the ROC curve was significantly higher with IMR-neuro (0.90 vs. 0.86 for FBP, p = 0.04). CONCLUSION: IMR-neuro yielded better image quality and improved hypo-attenuation detection in patients with ischaemic stroke. KEY POINTS: • Iterative model reconstruction of brain CT data can facilitate the diagnosis of ischaemic stroke. • IMR improved the detectability of low-contrast lesions in the posterior fossa. • IMR-neuro yielded better image quality and improved observer performance.

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.

Laparoscopic-assisted treatment for diospyrobezoar-induced intestinal obstruction after distal gastrectomy and cholecystectomy.

Diospyrobezoar is a relatively uncommon cause of small bowel obstruction. Here we report successful treatment in a patient with small bowel obstruction due to diospyrobezoar by laparoscopic-assisted surgery. A 93-year-old woman who had undergone distal gastrectomy and laparoscopic cholecystectomy presented with nausea and anorexia. An intestinal obstruction and an intestinal intraluminal mass were discovered on abdominal enhanced computed tomography. Following a transnasal ileus tube placement, the patient underwent laparoscopic surgery to remove the diospyrobezoar from the small intestine. The postoperative course of the patient was uneventful. Laparoscopic-assisted surgery following the transnasal ileus tube was beneficial for the patient's small bowel obstruction caused by diospyrobezoar.

An initial experience of machine learning based on multi-sequence texture parameters in magnetic resonance imaging to differentiate glioblastoma from brain metastases.

PURPOSE: To evaluate the performance of a machine learning method based on texture parameters in conventional magnetic resonance imaging (MRI) in differentiating glioblastoma (GB) from brain metastases (METs). MATERIALS AND METHODS: In this retrospective study conducted between November 2008 and July 2017, we included 73 patients diagnosed with GB (n = 73) and METs (n = 53) who underwent contrast-enhanced 3 T brain MRI. Twelve histogram and texture parameters were assessed on T2-weighted images (T2WIs), apparent diffusion coefficient maps (ADCs), and contrast-enhanced T1-weighted images (CE-T1WIs). A prediction model was developed for a machine learning method, and the area under the receiver operating characteristic curve of this model was calculated through 5-fold cross-validation. Furthermore, machine learning method's performance was compared with three board-certified radiologists' judgments. RESULTS: Univariate logistic regression model showed that the area under the curve (AUC) was highest with the standard value of T2WIs (0.78), followed by the maximum value of T2WIs (0.764), minimum value of T2WIs (0.738), minimum values of CE-T1WIs and contrast of T2WIs (0.733), and mean value of T2WIs (0.724). AUC calculated using the support vector machine was comparable to that calculated by the three radiologists (0.92 vs. 0.72, p < .01; 0.92 vs. 0.73, p < .01; and 0.92 vs. 0.86, p = .096). CONCLUSION: In differentiating GB from METs on the basis of texture parameters in MRI, the performance of the machine learning method based on convention MRI was superior to that of the univariate method, and comparable to that of the radiologists.

Prevalence of extracardiac findings in patients undergoing coronary computed tomography and additional low-dose whole-body computed tomography.

PURPOSE: In patients with suspected coronary artery disease (CAD), coexisting extracardiac abnormalities have a major impact on the patient management. This study aimed to evaluate the image quality of whole-body computed tomography (CT) immediately after the coronary computed tomography angiography (CTA) and investigate the incidence of extracardiac findings in patients with suspected CAD. MATERIALS AND METHODS: We enrolled 450 patients undergoing whole-body CT at 100 kVp and model-based iterative reconstruction immediately after the coronary CTA (Group A) and retrospectively reviewed 144 control patients who underwent conventional contrast-enhanced CT (120 kVp) with filtered back projection (Group B). We compared the signal-to-noise ratio (SNR) of the aorta and liver and radiation dose between the two groups. Then, we evaluated the prevalence of extracardiac findings in Group A. RESULTS: Compared with Group B, Group A demonstrated significantly higher aorta and liver SNR and lower radiation dose. In Group A, whole-body CT revealed 229 coexisting lesions in 165 patients, including 32 and 106 cases of oncologic and vascular diseases, respectively. CONCLUSION: Additional whole-body CT after coronary CTA may provide adequate image quality. Using additional whole-body CT, 36% of patients with suspected CAD had clinically relevant coexisting findings, including malignancy.