Dr. Sarvenaz Pourjabbar

CT
Postmortem CT, 3D touchscreen table aid virtual autopsy
Postmortem CT can be an invaluable autopsy planning tool. With the help of a life-sized, 3D-enabled touchscreen table, pathologists can also view and manipulate images in 3D modes to locate pathology, perform virtual dissections, and decide on the best autopsy approach.
June 13, 2013
2013 06 13 14 20 35 299 Autopsy3 450 20130613213150
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Two representative cases demonstrate the performance of PlaqueSegNet for coronary plaque segmentation. (A–D) Coronary CT angiography (CCTA) and intravascular US (IVUS) images in a 65-year-old male patient with chest pain for two months show plaque. (A) Cross-sectional IVUS image shows a lipid-rich plaque (hypoechoic, arrows). (B) Axial and (C) cross-sectional CCTA images show the same plaque (low-attenuation area, yellow line and arrows) in the proximal left anterior descending artery. (D) PlaqueSegNet segmentation of the noncalcified plaque (yellow shading) and coronary lumen (green shading) in the cross-sectional image. (E–G) CCTA images in a 57-year old female patient with chest pain for three months show multiple plaques. (E) Volume-rendered CCTA image of the coronary tree shows PlaqueSegNet plaque segmentation (yellow) in the right and left coronary arteries. (F, G) PlaqueSegNet segmentation delineates a mixed plaque (yellow shading) and coronary lumen (green shading) in the left main artery and proximal-to-mid left anterior descending artery.
CT
AI plus CCTA cuts plaque analysis time, predicts cardiac event risk
A deep-learning model quantifies coronary plaque volume from coronary CT angiography (CCTA) images.
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A 65-y-old man with HCC and liver cirrhosis confirmed by MRI. Ga-68 FAPI-46 PET/CT demonstrates increased focal uptake (SUVmax, 13.26, and TBRmax, 2.89) in liver, whereas F-18 FDG-PET/CT scans showed non–F-18 FDG-avid lesion (SUVmax, 3.24, and TBRmax, 1.43), indicated by solid arrows. Additionally, histologically proven adenocarcinoma in colon transversum was identified as pathologic with Ga-68 FAPI-46 alone (dashed arrows).
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AI plus CCTA cuts plaque analysis time, predicts cardiac event risk
By Kate Madden Yee
A deep-learning model quantifies coronary plaque volume from coronary CT angiography (CCTA) images.
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Two representative cases demonstrate the performance of PlaqueSegNet for coronary plaque segmentation. (A–D) Coronary CT angiography (CCTA) and intravascular US (IVUS) images in a 65-year-old male patient with chest pain for two months show plaque. (A) Cross-sectional IVUS image shows a lipid-rich plaque (hypoechoic, arrows). (B) Axial and (C) cross-sectional CCTA images show the same plaque (low-attenuation area, yellow line and arrows) in the proximal left anterior descending artery. (D) PlaqueSegNet segmentation of the noncalcified plaque (yellow shading) and coronary lumen (green shading) in the cross-sectional image. (E–G) CCTA images in a 57-year old female patient with chest pain for three months show multiple plaques. (E) Volume-rendered CCTA image of the coronary tree shows PlaqueSegNet plaque segmentation (yellow) in the right and left coronary arteries. (F, G) PlaqueSegNet segmentation delineates a mixed plaque (yellow shading) and coronary lumen (green shading) in the left main artery and proximal-to-mid left anterior descending artery.
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A 65-y-old man with HCC and liver cirrhosis confirmed by MRI. Ga-68 FAPI-46 PET/CT demonstrates increased focal uptake (SUVmax, 13.26, and TBRmax, 2.89) in liver, whereas F-18 FDG-PET/CT scans showed non–F-18 FDG-avid lesion (SUVmax, 3.24, and TBRmax, 1.43), indicated by solid arrows. Additionally, histologically proven adenocarcinoma in colon transversum was identified as pathologic with Ga-68 FAPI-46 alone (dashed arrows).
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