Cardiovascular CT Angiography for Level 1 ACC Certification

Mar 6th, 2008Mar 8th, 2008

New York City, NY

CT Angiography at the Lenox Hill Heart & Vascular Institute: Hands-on Training for Level 1 ACC Certification is a two-and-a-half-day in New York City will satisfy ACC Level 1 certification. The course is based upon the principle that extensive physician training in data processing is essential; interpretation alone does not prepare the trainee for the complexities of coronary CTA. It is an intensive, hands-on experience, involving postprocessing and interpretation of 50 CT coronary angiograms using the most advanced Philips work stations. The postprocessing will be performed simultaneously by the course participants and the course director, with two participants per work station. Cases will begin with the simplest, and progress to the most complex; stent and bypass graft evaluation will be emphasized, as well as dealing with artifacts and arrhythmias.

Illustrations show the role of angiosome-to-tumor volume ratio (ATR) in different clinical scenarios based on tumor and treatment angiosome volume. The pale orange oval represents the tumor within the liver, and the pie-shaped region encompassed by the black lines represents the angiosome. (A) An adequate ATR (≥16) is reached when treating tumor and an accompanying appropriate volume of expendable surrounding normal parenchyma, such as a 2.5-cm tumor within a 150-mL treatment angiosome. Lower (inadequate) ATR can occur with superselective administrations, such as (B) attempting to treat a larger (4-cm) tumor within the same 150-mL angiosome, which proves suboptimal, and (C) attempting to treat the same 2.5-cm tumor within a reduced angiosome (40 mL).

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$Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).$