Dynamic computed dynamic computed tomography imaging of vasa vasorum perfusion and angiogenesis in the vascular wall
OwnerMorteza Naghavi, Chenyu Zhang +1inventors
InventorsMorteza Naghavi, Chenyu Zhang, Kyle Atlas
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Abstract
A method for quantitative mapping of vasa vasorum density within and adjacent to the coronary arterial wall using contrast-enhanced coronary CT angiography scans, including time-resolved perfusion, multi-energy material decomposition, and longitudinal functional monitoring of vasa vasorum dynamics.
Claims16
A method for detecting vascular inflammation and angiogenesis in a vascular wall using computed tomography (CT), comprising:
- training a Machine Learning Model (MLM) to train on a paired non-contrast and contrast enhanced CT scans to segment the vessel wall;
- acquiring at least two CT datasets of the same vascular segment at different time points following administration of contrast;
- quantifying time-dependent enhancement within the wall to generate a plurality of voxel-level perfusion curves representing microvascular blood flow;
- computing a vasa vasorum density index (VVDI) and vasa vasorum perfusion index (VVPI), based on the intensity values and time-dependent intensity changes from one or more temporal parameters including peak enhancement, time-to-peak, wash-in rate, and area-under-curve for each voxel within and surrounding the segmented wall;
- displaying the VVDI and VVPI parameters in color-coded parametric overlay on the segmented wall and surrounding;
- and
- comparing VVDI and VVPI parameters between baseline and follow-up studies, to determine effectiveness of anti-inflammatory or lipid therapy, and natural progression of subclinical atherosclerosis.
A method for characterizing vasa vasorum perfusion using dual-energy or photon-counting CT, comprising:
- training a Machine Learning Model (MLM) to train on a paired non-contrast and contrast enhanced CT scans to segment the vessel wall;
- acquiring spectral CT images of the vessel wall;
- performing material decomposition to isolate iodine concentration maps inside and surrounding the wall;
- quantifying iodine-based enhancement as a proxy for vasa vasorum blood volume;
- generating a three-dimensional perfusion density map;
- and
- classifying vascular segments as inflamed, stable, or fibrotic based on iodine distribution.
A computer-implemented method for monitoring progression or regression of vascular inflammation, comprising:
- training a Machine Learning Model (MLM) to train on a paired non-contrast and contrast enhanced CT scans to segment the vessel wall;
- obtaining baseline and follow-up CT scans of the same patient;
- extracting vasa vasorum perfusion features from both scans;
- feeding the temporal features into a trained machine learning model (MLM) configured to output a vasa vasorum activity index (VVAI);
- and
- outputting a clinical recommendation output regarding therapeutic response or risk of cardiovascular event.
Dependencies are inferred from the claim text.