Dr. Amy Thurmond

Breast
Breast imaging providers must lead density education
Radiologists are uniquely suited to discuss breast density with patients and should take the lead in patient information efforts, according to Dr. Amy Thurmond of Womens Imaging & Intervention in Oregon. In this column, Thurmond discusses how her practice talks about density with its patients.
April 1, 2015
2015 04 01 16 07 23 606 Thurmond Amy 200
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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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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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