Jonathan Shoemaker

PACS/VNA
Imaging Interoperability: Part 2 -- The 3 flavors of VNA
AuntMinnie.com presents the second in a two-part series on imaging interoperability from Jonathan Shoemaker and Jef Williams of Ascendian Healthcare Consulting. In Part 2, the authors present the key factors that best leverage the functionality of a true VNA.
March 14, 2013
2013 03 14 14 49 24 945 Ascendian Logo 70
PACS/VNA
Imaging Interoperability: Part 1 -- New options
AuntMinnie.com presents the first in a two-part series from Jonathan Shoemaker and Jef Williams of Ascendian Healthcare Consulting. In part 1, the authors explain how vendor-neutral archives can provide much-needed interoperability in today's tumultuous healthcare environment.
September 6, 2012
2012 09 06 15 13 24 423 Ascendian 450
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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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AI plus CCTA cuts plaque analysis time, predicts cardiac event risk
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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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