Obstructive jaundice by vascular compression is rare. The causative arteries were identified as the right hepatic artery, gastroduodenal artery, cystic artery, proper hepatic artery, and an unspecified branch of the common hepatic artery. Also the venous system, such as enlarging collateral veins in cases of portal hypertension was a causative vessel. Herein, we describe a case of a proximal choledocholithiasis due to compression of the common bile duct by right hepatic artery originated from gastroduodenal artery. Final diagnosis and treatment were achieved through an operation.
Cholangiography ; Common Bile Duct/blood supply/*pathology/surgery ; Diagnosis, Differential ; *Hepatic Artery ; Humans ; Jaundice, Obstructive/*diagnosis/etiology/radiography ; Male ; Middle Aged ; Tomography, X-Ray Computed

A double pylorus, a fistulous communication between the gastric antrum and duodenal bulb, is a very rare anomaly. It appears as an acquired lesion in the majority of cases and mainly occurs in males with chronic obstructive pulmonary disease, chronic renal failure, chronic rheumatism, systemic lupus erythematosus, or diabetes. However, there are no previous reports of a double pylorus combined with diabetes mellitus in Korea. Recently we experienced a case of double pylorus caused by a peptic ulcer in 74-year-old man who had diabetes mellitus with complications. We report this case with a review of the literature.
Aged ; Diabetes Mellitus ; Humans ; Kidney Failure, Chronic ; Korea ; Lupus Erythematosus, Systemic ; Male ; Peptic Ulcer ; Pulmonary Disease, Chronic Obstructive ; Pyloric Antrum ; Pylorus ; Rheumatic Diseases

Many reports have shown that endoscopic polypectomy or endoscopic mucosal resection can successfully remove tumor less than 1.0 cm in size. However, most carcinoid tumors in the rectum occur in the submucosal layer so that the entire tumor cannot be completely removed via endoscopic polypectomy or endoscopic mucosal resection. Endoscopic mucosal resection can also cause perforation of the intestinal wall and bleeding. Due to these reasons, instead of these two conventional methods, endoscopic mucosal resection using a ligation device is currently being used for the treatment of rectal carcinoid tumor. Recent studies that used this method have reported that endoscopic mucosal resection of rectal carcinoid tumor by band ligation and the snare resection technique is safe with minimal complications and this is quite useful to completely remove rectal carcinoid tumor.
Carcinoid Tumor ; Hemorrhage ; Ligation ; Rectum ; SNARE Proteins

Granular cell tumor (GCT) is a relatively rare benign tumor that can be located anywhere throughout the body, but it is uncommon in the gastrointestinal tract, and especially in the colon and rectum. A 41-year-old man visited our hospital with a three-month history of intermittent abdominal discomfort at the left lower quadrant area. Colonoscopic examination revealed a hemispheric, submucosal lesion, about 1.5 x 1.2 cm in size, on the opposite side of the ileocecal valve in the proximal ascending colon. The tumor was removed by an endoscopic snare without any immediate complication. The pathologic findings revealed spindled or polygonal large cells that were positive for S-100, neuron-specific enolase and vimentin, and they were negative for smooth muscle actin and desmin, which was consistent with granular cell tumor. We report here on a case of granular cell tumor of the colon that was successfully treated with endoscopic polypectomy.
Actins ; Adult ; Colon* ; Colon, Ascending ; Colonoscopy* ; Desmin ; Gastrointestinal Tract ; Granular Cell Tumor* ; Humans ; Ileocecal Valve ; Muscle, Smooth ; Phosphopyruvate Hydratase ; Rectum ; SNARE Proteins ; Vimentin

Objective: This study aimed to validate the risk stratification system (RSS) and biopsy criteria for cervical lymph nodes (LNs) proposed by the Korean Society of Thyroid Radiology (KSThR). Materials and Methods: This retrospective study included a consecutive series of preoperative patients with thyroid cancer who underwent LN biopsy, ultrasound (US), and computed tomography (CT) between December 2006 and June 2015. LNs were categorized as probably benign, indeterminate, or suspicious according to the current US- and CT-based RSS and the size thresholds for cervical LN biopsy as suggested by the KSThR. The diagnostic performance and unnecessary biopsy rates were calculated. Results: A total of 277 LNs (53.1% metastatic) in 228 patients (mean age ± standard deviation, 47.4 years ± 14) were analyzed. In US, the malignancy risks were significantly different among the three categories (all P < 0.001); however, CTdetected probably benign and indeterminate LNs showed similarly low malignancy risks (P = 0.468). The combined US + CT criteria stratified the malignancy risks among the three categories (all P < 0.001) and reduced the proportion of indeterminate LNs (from 20.6% to 14.4%) and the malignancy risk in the indeterminate LNs (from 31.6% to 12.5%) compared with US alone. In all image-based classifications, nodal size did not affect the malignancy risks (short diameter [SD] ≤ 5 mm LNs vs. SD > 5 mm LNs, P ≥ 0.177). The criteria covering only suspicious LNs showed higher specificity and lower unnecessary biopsy rates than the current criteria, while maintaining sensitivity in all imaging modalities. Conclusion Integrative evaluation of US and CT helps in reducing the proportion of indeterminate LNs and the malignancy risk among them. Nodal size did not affect the malignancy risk of LNs, and the addition of indeterminate LNs to biopsy candidates did not have an advantage in detecting LN metastases in all imaging modalities.

Objective: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI. Materials and Methods: This study included 150 participants (51 male; mean age 57.3 ± 16.2 years). Each group of 50 participants was scanned using one of three 3T scanners from three different vendors. Conventional and Accel-DL MRI images were obtained from each participant and compared using 2D T1- and T2-weighted and 3D gradient-echo sequences. Accel-DL acquisition was achieved using optimized scan parameters to reduce the scan time, with the acquired images reconstructed using U-Net-based software to transform low-quality, undersampled k-space data into high-quality images. The scan times of Accel-DL and conventional MRI methods were compared. Four neuroradiologists assessed the overall image quality, structural delineation, and artifacts using Likert scale (5- and 3-point scales). Inter-reader agreement was assessed using Fleiss’ kappa coefficient. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and volumetric quantification of regional structures and white matter hyperintensities (WMHs) was performed. Results: Accel-DL showed a mean scan time reduction of 39.4% (range, 24.2%–51.3%). Accel-DL improved overall image quality (3.78 ± 0.71 vs. 3.36 ± 0.61, P < 0.001), structure delineation (2.47 ± 0.61 vs. 2.35 ± 0.62, P < 0.001), and artifacts (3.73 ± 0.72 vs. 3.71 ± 0.69, P = 0.016). Inter-reader agreement was fair to substantial (κ = 0.34–0.50). SNR and CNR increased in Accel-DL (82.0 ± 23.1 vs. 31.4 ± 10.8, P = 0.02; 12.4 ± 4.1 vs. 4.4 ± 11.2, P = 0.02). Bland-Altman plots revealed no significant differences in the volumetric measurements of 98.2% of the relevant regions, except in the deep gray matter, including the thalamus. Five of the six lesion categories showed no significant differences in WMH segmentation, except for leukocortical lesions (r = 0.64 ± 0.29). Conclusion Accel-DL substantially reduced the scan time and improved the quality of brain MRI in both spin-echo and gradientecho sequences without compromising volumetry, including lesion quantification.

Objective: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI. Materials and Methods: This study included 150 participants (51 male; mean age 57.3 ± 16.2 years). Each group of 50 participants was scanned using one of three 3T scanners from three different vendors. Conventional and Accel-DL MRI images were obtained from each participant and compared using 2D T1- and T2-weighted and 3D gradient-echo sequences. Accel-DL acquisition was achieved using optimized scan parameters to reduce the scan time, with the acquired images reconstructed using U-Net-based software to transform low-quality, undersampled k-space data into high-quality images. The scan times of Accel-DL and conventional MRI methods were compared. Four neuroradiologists assessed the overall image quality, structural delineation, and artifacts using Likert scale (5- and 3-point scales). Inter-reader agreement was assessed using Fleiss’ kappa coefficient. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and volumetric quantification of regional structures and white matter hyperintensities (WMHs) was performed. Results: Accel-DL showed a mean scan time reduction of 39.4% (range, 24.2%–51.3%). Accel-DL improved overall image quality (3.78 ± 0.71 vs. 3.36 ± 0.61, P < 0.001), structure delineation (2.47 ± 0.61 vs. 2.35 ± 0.62, P < 0.001), and artifacts (3.73 ± 0.72 vs. 3.71 ± 0.69, P = 0.016). Inter-reader agreement was fair to substantial (κ = 0.34–0.50). SNR and CNR increased in Accel-DL (82.0 ± 23.1 vs. 31.4 ± 10.8, P = 0.02; 12.4 ± 4.1 vs. 4.4 ± 11.2, P = 0.02). Bland-Altman plots revealed no significant differences in the volumetric measurements of 98.2% of the relevant regions, except in the deep gray matter, including the thalamus. Five of the six lesion categories showed no significant differences in WMH segmentation, except for leukocortical lesions (r = 0.64 ± 0.29). Conclusion Accel-DL substantially reduced the scan time and improved the quality of brain MRI in both spin-echo and gradientecho sequences without compromising volumetry, including lesion quantification.

Objective: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI. Materials and Methods: This study included 150 participants (51 male; mean age 57.3 ± 16.2 years). Each group of 50 participants was scanned using one of three 3T scanners from three different vendors. Conventional and Accel-DL MRI images were obtained from each participant and compared using 2D T1- and T2-weighted and 3D gradient-echo sequences. Accel-DL acquisition was achieved using optimized scan parameters to reduce the scan time, with the acquired images reconstructed using U-Net-based software to transform low-quality, undersampled k-space data into high-quality images. The scan times of Accel-DL and conventional MRI methods were compared. Four neuroradiologists assessed the overall image quality, structural delineation, and artifacts using Likert scale (5- and 3-point scales). Inter-reader agreement was assessed using Fleiss’ kappa coefficient. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and volumetric quantification of regional structures and white matter hyperintensities (WMHs) was performed. Results: Accel-DL showed a mean scan time reduction of 39.4% (range, 24.2%–51.3%). Accel-DL improved overall image quality (3.78 ± 0.71 vs. 3.36 ± 0.61, P < 0.001), structure delineation (2.47 ± 0.61 vs. 2.35 ± 0.62, P < 0.001), and artifacts (3.73 ± 0.72 vs. 3.71 ± 0.69, P = 0.016). Inter-reader agreement was fair to substantial (κ = 0.34–0.50). SNR and CNR increased in Accel-DL (82.0 ± 23.1 vs. 31.4 ± 10.8, P = 0.02; 12.4 ± 4.1 vs. 4.4 ± 11.2, P = 0.02). Bland-Altman plots revealed no significant differences in the volumetric measurements of 98.2% of the relevant regions, except in the deep gray matter, including the thalamus. Five of the six lesion categories showed no significant differences in WMH segmentation, except for leukocortical lesions (r = 0.64 ± 0.29). Conclusion Accel-DL substantially reduced the scan time and improved the quality of brain MRI in both spin-echo and gradientecho sequences without compromising volumetry, including lesion quantification.

Objective: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI. Materials and Methods: This study included 150 participants (51 male; mean age 57.3 ± 16.2 years). Each group of 50 participants was scanned using one of three 3T scanners from three different vendors. Conventional and Accel-DL MRI images were obtained from each participant and compared using 2D T1- and T2-weighted and 3D gradient-echo sequences. Accel-DL acquisition was achieved using optimized scan parameters to reduce the scan time, with the acquired images reconstructed using U-Net-based software to transform low-quality, undersampled k-space data into high-quality images. The scan times of Accel-DL and conventional MRI methods were compared. Four neuroradiologists assessed the overall image quality, structural delineation, and artifacts using Likert scale (5- and 3-point scales). Inter-reader agreement was assessed using Fleiss’ kappa coefficient. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and volumetric quantification of regional structures and white matter hyperintensities (WMHs) was performed. Results: Accel-DL showed a mean scan time reduction of 39.4% (range, 24.2%–51.3%). Accel-DL improved overall image quality (3.78 ± 0.71 vs. 3.36 ± 0.61, P < 0.001), structure delineation (2.47 ± 0.61 vs. 2.35 ± 0.62, P < 0.001), and artifacts (3.73 ± 0.72 vs. 3.71 ± 0.69, P = 0.016). Inter-reader agreement was fair to substantial (κ = 0.34–0.50). SNR and CNR increased in Accel-DL (82.0 ± 23.1 vs. 31.4 ± 10.8, P = 0.02; 12.4 ± 4.1 vs. 4.4 ± 11.2, P = 0.02). Bland-Altman plots revealed no significant differences in the volumetric measurements of 98.2% of the relevant regions, except in the deep gray matter, including the thalamus. Five of the six lesion categories showed no significant differences in WMH segmentation, except for leukocortical lesions (r = 0.64 ± 0.29). Conclusion Accel-DL substantially reduced the scan time and improved the quality of brain MRI in both spin-echo and gradientecho sequences without compromising volumetry, including lesion quantification.

Objective: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI. Materials and Methods: This study included 150 participants (51 male; mean age 57.3 ± 16.2 years). Each group of 50 participants was scanned using one of three 3T scanners from three different vendors. Conventional and Accel-DL MRI images were obtained from each participant and compared using 2D T1- and T2-weighted and 3D gradient-echo sequences. Accel-DL acquisition was achieved using optimized scan parameters to reduce the scan time, with the acquired images reconstructed using U-Net-based software to transform low-quality, undersampled k-space data into high-quality images. The scan times of Accel-DL and conventional MRI methods were compared. Four neuroradiologists assessed the overall image quality, structural delineation, and artifacts using Likert scale (5- and 3-point scales). Inter-reader agreement was assessed using Fleiss’ kappa coefficient. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and volumetric quantification of regional structures and white matter hyperintensities (WMHs) was performed. Results: Accel-DL showed a mean scan time reduction of 39.4% (range, 24.2%–51.3%). Accel-DL improved overall image quality (3.78 ± 0.71 vs. 3.36 ± 0.61, P < 0.001), structure delineation (2.47 ± 0.61 vs. 2.35 ± 0.62, P < 0.001), and artifacts (3.73 ± 0.72 vs. 3.71 ± 0.69, P = 0.016). Inter-reader agreement was fair to substantial (κ = 0.34–0.50). SNR and CNR increased in Accel-DL (82.0 ± 23.1 vs. 31.4 ± 10.8, P = 0.02; 12.4 ± 4.1 vs. 4.4 ± 11.2, P = 0.02). Bland-Altman plots revealed no significant differences in the volumetric measurements of 98.2% of the relevant regions, except in the deep gray matter, including the thalamus. Five of the six lesion categories showed no significant differences in WMH segmentation, except for leukocortical lesions (r = 0.64 ± 0.29). Conclusion Accel-DL substantially reduced the scan time and improved the quality of brain MRI in both spin-echo and gradientecho sequences without compromising volumetry, including lesion quantification.