Case 5.16

2014 ◽  
pp. 272-273
Author(s):  
Christine U. Lee ◽  
James F. Glockner
Keyword(s):  
Polycythemia Vera ◽  
Signal Intensity ◽  
Arterial Phase ◽  
Endoscopic Drainage ◽  
Blood Transfusions ◽  
Splenic Hilum ◽  
Portal Venous Phase ◽  
History Of ◽  
Venous Phase ◽  
Multiple Blood

68-year-old man with a history of polycythemia vera and a recent episode of pancreatitis, which required endoscopic drainage of a pancreatic pseudocyst with a cystogastrostomy tube Coronal SSFSE (Figure 5.16.1) and axial fat-suppressed FSE T2-weighted (Figure 5.16.2) images show splenomegaly with a cyst in the posterior spleen. Note also the decreased signal intensity in the liver and spleen due to hemosiderosis from multiple blood transfusions. A round structure in the splenic hilum is bright on T2-weighted images and is surrounded by a small amount of fluid containing a fluid-fluid level. Axial arterial phase and portal venous phase postgadolinium 3D SPGR images (...

Case 5.6

2014 ◽  
pp. 253-253
Author(s):  
Christine U. Lee ◽  
James F. Glockner
Keyword(s):  
Abdominal Pain ◽  
Equilibrium Phase ◽  
Arterial Phase ◽  
Recurrent Pancreatitis ◽  
Portal Venous Phase ◽  
History Of ◽  
Venous Phase ◽  
Delayed Phase

37-year-old woman with a history of recurrent pancreatitis and abdominal pain Arterial phase (Figure 5.6.1A), portal venous phase (Figure 5.6.1B), equilibrium phase (Figure 5.6.1C), and 8-minute delayed phase (Figure 5.6.1D) postgadolinium 3D SPGR images show multiple splenic lesions that are initially hypoenhancing relative to adjacent spleen and become hyperintense on delayed images....

Case 2.4

2014 ◽  
pp. 69-70
Author(s):  
Christine U. Lee ◽  
James F. Glockner
Keyword(s):  
Colon Cancer ◽  
Family History ◽  
Signal Intensity ◽  
Chest Ct ◽  
Portal Venous Phase ◽  
History Of ◽  
Venous Phase ◽  
Noncontrast Enhanced

58-year-old man with a family history of colon cancer; noncontrast enhanced chest CT showed low-attenuation hepatic masses in the visualized liver Axial T1-weighted IP and OP 2D SPGR images (Figure 2.4.1) demonstrate multiple low-signal-intensity nodules throughout the liver, visible only on the OP images. These nodules are not apparent on axial arterial and portal venous phase, postgadolinium 3D SPGR images (...

Case 1.2

2014 ◽  
pp. 3-4
Author(s):  
Christine U. Lee ◽  
James F. Glockner
Keyword(s):  
Abdominal Pain ◽  
Signal Intensity ◽  
Equilibrium Phase ◽  
High Signal Intensity ◽  
Arterial Phase ◽  
High Signal ◽  
Portal Venous Phase ◽  
Hepatic Lobe ◽  
Venous Phase ◽  
The Right

66-year-old woman with nausea, vomiting, and abdominal pain after a recent fundoplication. Abdominal CT revealed a right hepatic lobe mass Coronal SSFSE (Figure 1.2.1) and axial fat-suppressed FSE T2-weighted (Figure 1.2.2) images demonstrate a lobulated mass with high signal intensity in the right hepatic lobe. Axial arterial phase, portal venous phase, and coronal oblique reformatted equilibrium phase postgadolinium 3D SPGR images (...

Case 5.11

2014 ◽  
pp. 262-263
Author(s):  
Christine U. Lee ◽  
James F. Glockner
Keyword(s):  
Signal Intensity ◽  
Portal Venous Phase ◽  
Diffusion Weighted Images ◽  
Diffusion Weighted ◽  
Lumbar Vertebral Body ◽  
Normal Spleen ◽  
History Of ◽  
Venous Phase ◽  
Hepatic Lesions ◽  
And Diffusion

51-year-old woman with a history of breast cancer Axial fat-suppressed FSE T2-weighted images (Figure 5.11.1), axial diffusion-weighted images (b=100 s/mm2) (Figure 5.11.2), and axial portal venous phase postgadolinium 3D SPGR images (Figure 5.11.3) show multiple nodules in the liver and spleen. Hepatic lesions have increased signal intensity relative to liver, while the splenic lesions show decreased signal intensity compared with normal spleen on T2- and diffusion-weighted images. Most lesions show hypoenhancement compared with liver and spleen, although several hepatic lesions have central enhancement. Note also small enhancing lesions in an upper lumbar vertebral body....

Acute-onset abdominal pain in a woman in her 30s

Heart ◽  
2018 ◽  
Vol 105 (4) ◽  
pp. 275-322 ◽  
Author(s):  
Rory O’Donohoe ◽  
Samantha Fitzsimmons ◽  
Timothy J C Bryant
Keyword(s):  
Abdominal Pain ◽  
Hepatocellular Adenoma ◽  
Sudden Onset ◽  
Acute Onset ◽  
Arterial Phase ◽  
List Type ◽  
Portal Venous Phase ◽  
Venous Phase ◽  
Axial Ct ◽  
Multiphase Ct

Clinical introductionA woman in her 30s presented to the emergency department with sudden-onset abdominal pain with hypotension and tachycardia. She gave a history of congenital heart disease for which she had previously undergone multiple operations. On examination she demonstrated right upper quadrant tenderness. She underwent an urgent multiphase CT (figure 1A–C).Figure 1(A) Arterial phase coronal CT. (B) Arterial phase axial CT. (C) Portal venous phase axial CT.QuestionWhat is the underlying liver pathology?Hepatocellular adenomaCholangiocarcinomaHepatocellular carcinomaFocal nodular hyperplasiaHepatoblastoma

Case 8.11

2014 ◽  
pp. 391-392
Author(s):  
Christine U. Lee ◽  
James F. Glockner
Keyword(s):  
Signal Intensity ◽  
Arterial Phase ◽  
Weighted Image ◽  
History Of

37-year-old woman with a history of neurofibromatosis and menometrorrhagia Sagittal fat-suppressed 3D FSE T2-weighted image (Figure 8.11.1) and axial 2D fat-suppressed FSE images (Figure 8.11.2) reveal masslike thickening of the posterior and superior bladder walls, with relatively low signal intensity. Sagittal arterial phase postgadolinium 3D SPGR image (...

scholarly journals CT differentiation of gastric ectopic pancreas from gastric stromal tumors

2020 ◽  
Author(s):  
Jian Wang ◽  
Chang LIU ◽  
Fang Yang ◽  
Wenming Zhang ◽  
Weiqun Ao ◽  
...  
Keyword(s):  
Univariate Analysis ◽  
Ectopic Pancreas ◽  
Arterial Phase ◽  
Portal Venous Phase ◽  
Test Efficiency ◽  
Attenuation Value ◽  
Ct Attenuation ◽  
Venous Phase ◽  
Ct Attenuation Value ◽  
Ct Features

Abstract BackgroundGastric ectopic pancreas (GEPs) is a rare developmental anomaly which is difficult to differentiate it from submucosal tumor such as gastrointestinal stromal tumor (GIST) by imaging methods. So we retrospectively investigated the CT features of them to help us make the correct diagnosis.Materials and MethodsThis study enrolled 17 GEPs and 119 GSTs, which were proven pathologically. We assessed clinical and CT features to identify significant differential features of GEPs from GSTs using univariate and multivariate analyses.ResultsIn univariate analysis, among all clinicoradiologic features, features of age, symptom, tumor marker, location, contour, blurred serosa or fat-line of peritumor, necrosis, calcification, CT attenuation value of unenhancement phase/arterial phase/portal venous phase (CTu/CTa/CTp), the CT attenuation value of arterial phase/portal venous phase minus that of unenhanced phase (DEAP/DEPP), long diameter (LD), short diameter (SD) were considered statistically significant for the differentiation of them. And the multivariate analysis revealed that location, blurred serosa or fat-line of peritumor, necrosis and DEPP were independent factors affecting the identification of them.What's more, ROC analysis showed that the test efficiency of CTp was perfect(AUC= 0.900).ConclusionLocation, blurred serosa or fat-line of peritumor, necrosis and DEPP are useful CT differentiators of GEPs from GSTs. In addition, the test efficiency of CTp in differentiating them was perfect (AUC=0.900).

scholarly journals Advanced Deep Learning Approach to Automatically Segment Malignant Tumors and Ablation Zone in the Liver With Contrast-Enhanced CT

2021 ◽  
Vol 11 ◽  
Author(s):  
Kan He ◽  
Xiaoming Liu ◽  
Rahil Shahzad ◽  
Robert Reimer ◽  
Frank Thiele ◽  
...  
Keyword(s):  
Deep Learning ◽  
Liver Tumors ◽  
Treatment Success ◽  
Detection Sensitivity ◽  
Arterial Phase ◽  
Liver Segmentation ◽  
Portal Venous Phase ◽  
Venous Phase ◽  
Pre Treatment ◽  
Multi Phase

ObjectiveLiver cancer is one of the most commonly diagnosed cancer, and energy-based tumor ablation is a widely accepted treatment. Automatic and robust segmentation of liver tumors and ablation zones would facilitate the evaluation of treatment success. The purpose of this study was to develop and evaluate an automatic deep learning based method for (1) segmentation of liver and liver tumors in both arterial and portal venous phase for pre-treatment CT, and (2) segmentation of liver and ablation zones in both arterial and portal venous phase for after ablation treatment.Materials and Methods252 CT images from 63 patients undergoing liver tumor ablation at a large University Hospital were retrospectively included; each patient had pre-treatment and post-treatment multi-phase CT images. 3D voxel-wise manual segmentation of the liver, tumors and ablation region by the radiologist provided reference standard. Deep learning models for liver and lesion segmentation were initially trained on the public Liver Tumor Segmentation Challenge (LiTS) dataset to obtain base models. Then, transfer learning was applied to adapt the base models on the clinical training-set, to obtain tumor and ablation segmentation models both for arterial and portal venous phase images. For modeling, 2D residual-attention Unet (RA-Unet) was employed for liver segmentation and a multi-scale patch-based 3D RA-Unet for tumor and ablation segmentation.ResultsOn the independent test-set, the proposed method achieved a dice similarity coefficient (DSC) of 0.96 and 0.95 for liver segmentation on arterial and portal venous phase, respectively. For liver tumors, the model on arterial phase achieved detection sensitivity of 71%, DSC of 0.64, and on portal venous phase sensitivity of 82%, DSC of 0.73. For liver tumors >0.5cm3 performance improved to sensitivity 79%, DSC 0.65 on arterial phase and, sensitivity 86%, DSC 0.72 on portal venous phase. For ablation zone, the model on arterial phase achieved detection sensitivity of 90%, DSC of 0.83, and on portal venous phase sensitivity of 90%, DSC of 0.89.ConclusionThe proposed deep learning approach can provide automated segmentation of liver tumors and ablation zones on multi-phase (arterial and portal venous) and multi-time-point (before and after treatment) CT enabling quantitative evaluation of treatment success.

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