Coronary angiography quantitative angiography

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Coronary Angiography


General Principles

Historical Perspective
Appropriate Use Criteria for Revascularization
Film Quality

Anatomy & Projection Angles

Normal Anatomy

Coronary arteries
Right System
Left System
Left Main
Left Anterior Descending
Median Ramus

Anatomic Variants

Separate Ostia
Anomalous Origins

Projection Angles

Standard Views
Left Coronary Artery
Right Coronary Artery

Epicardial Flow & Myocardial Perfusion

Epicardial Flow

TIMI Frame Count
TIMI Flow Grade
TIMI Grade 0 Flow
TIMI Grade 1 Flow
TIMI Grade 2 Flow
TIMI Grade 3 Flow
TIMI Grade 4 Flow
Pulsatile Flow

Myocardial Perfusion

TIMI Myocardial Perfusion Grade
TMP Grade 0
TMP Grade 0.5
TMP Grade 1
TMP Grade 2
TMP Grade 3

Lesion Complexity

ACC/AHA Lesion-Specific Classification of the Primary Target Stenosis

Preprocedural Lesion Morphology

Intimal Flap
Sawtooth Pattern
Ostial location
Proximal tortuosity
Degenerated SVG
Total occlusion
Coronary Artery Thrombus
TIMI Thrombus Grade
TIMI Thrombus Grade 0
TIMI Thrombus Grade 1
TIMI Thrombus Grade 2
TIMI Thrombus Grade 3
TIMI Thrombus Grade 4
TIMI Thrombus Grade 5
TIMI Thrombus Grade 6

Lesion Morphology

Quantitative Coronary Angiography
Definitions of Preprocedural Lesion Morphology
Irregular Lesion
Disease Extent
Arterial Foreshortening
Infarct Related Artery
Degenerated SVG

Left ventriculography

Quantification of LV Function
Quantification of Mitral Regurgitation

Editor-In-Chief: C. Michael Gibson, M.S., M.D. [1]


While “on-line” quantitative angiographic is somewhat cumbersome to use in the catheterization laboratory, “off-line” quantitative angiography has proven invaluable for research investigation in determining the effect of new drugs and devices on lumen dimensions early and late after percutaneous coronary interventions. Notably, for clinical decision making in intermediate lesions, neither trained visual estimates or on-line quanitative angiography are substitutes for precise physiologic measurements of stenosis severity, such as fractional flow reserve or coronary Doppler measurements.

Quantitative Angiography

Computer-Assisted Quantitative Angiography

Quantitative coronary angiography was initiated nearly 30 years ago by Brown and colleagues who magnified 35-mm cineangiograms obtained from orthogonal projections and hand traced the arterial edges on a large screen. After computer-assisted correction for pincushion distortion, the tracings were digitized and the orthogonal projections were combined to form a three-dimensional representation of the arterial segment, assuming an elliptical geometry. While the accuracy and precision were enhanced compared with visual methods, the time needed for image processing limited its clinical use.

Several automated edge-detection algorithms were then developed and applied to directly acquired digital images or to 35-mm cinefilm digitized using a cine-video converter. Subsequent interations of these first degeneration devices have utilized enhanced microprocessing speed and digital image acquisition to render the end-user interface more flexible and substantially shortened the time required for image analysis.

Quantitative coronary angiography is divided into several distinct processes, including film digitization (when applicable), image calibration, and arterial contour detection. For processing 35-mm cinefilm, a cine-video converter is used to digitize images into a 512 × 512 (or larger) × 8-bit pixel matrix. Optical, or less preferred digital, magnification results in an effective pixel matrix up to 2458 × 2458.

Image Calibration

For estimation of absolute coronary dimensions, the diagnostic or guiding catheter is generally used as the scaling device. In general, a nontapered segment of the catheter is selected, and a centerline through the catheter is drawn. Linear density profiles are then constructed perpendicular to the catheter centerline, and a weighted average of the first and second derivative function is used to define the catheter edge points. Individual edge points are then connected using an automated algorithm, outliers are discarded, and the edges are smoothed. The diameter of the catheter is then used to obtain a calibration factor, expressed in millimeters per pixel. The injection catheter dimensions may be influenced by whether contrast or saline is imaged within the catheter tip, and by the type of material used in catheter construction. As the high-flow injection catheters have been developed, more quantitative angiographic systems using contrast-filled injection catheters for image calibration.

Quantitative Coronary Analysis

The automated algorithm is then applied to a selected arterial segment, and absolute coronary dimensions are obtained from the minimal lumen diameter (MLD) reference diameter, and from these, the percent diameter stenoses are derived. For most angiographic systems, interobserver variabilities are 3.1% for diameter stenosis and 0.10 to 0.18 mm for MLD for cineangiographic readings; variabilities are slightly higher (< 0.25 mm) for repeated analyses of the digital angiograms due the the slightly lower resolution compared with cineangiography. The two most commonly used quantitative angiographic systems are described below:

  • CARDIOVASCULAR ANGIOGRAPHY ANALYSIS SYSTEM. CAAS (Pie Data Medical B.V., Maastricht, The Netherlands) is a quantitative angiographic system developed for off-line cineangiographic analysis. The edge-detection algorithm incorporates an optional correction for pincushion distortion; its edge detection uses a weighted (50%) sum of the first and second derivatives of the mean pixel density; and it applies minimal cost criteria for smoothing of the arterial edge contours. In addition to reporting a interpolated reference diameter and a minimal lumen diameter (MLD), a subsegment analysis provides mean, minimum, and maximum subsegment diameters. Specific reporting algorithms have been developed for drug-eluting stents, patients undergoing radiation brachytherapy, and in those undergoing peripheral intervention.
  • CORONARY MEASUREMENT SYSTEM. (CMS) (MEDIS, Leiden, The Netherlands). Specific features of the CMS include two-point user-defined centerline identification, arterial edge detection using a weighted (50%) sum of the first and second derivatives of the mean pixel density, arterial contour detection using a minimal cost matrix algorithm, and an “interpolated” reference vessel diameter. One limitation of the minimal cost algorithm used with the first-generation CMS (and CAAS-II) system has been its inability to precisely quantify arterial lumen contours characterized by abrupt changes. The CMS-GFT is an algorithm that is not restricted in its search directions, incorporating multidirectional information about the arterial boundaries for construction of the arterial edge that is suitable for the analysis of complex coronary artery lesions. Specific reporting algorithms have been developed for bifurcation lesions, drug-eluting stents, patients undergoing radiation brachytherapy, and in those undergoing peripheral intervention.

Factors Contributing to Variability Using QCA

Variability associated with measurements of the MLD and reference diameter is affected by a number of factors:

  • The biologic differences among lumen diameters (e.g., reference vessel size, vasomotor tone, thrombus).
  • Inconsistencies in radiographic image acquisition parameters (e.g., quantum mottling, out-of-plane magnification, foreshortening).
  • Angiographic measurement variability (e.g., frame selection, factors affecting the edge-detection algorithm). These factors should be controlled in order to improve on the overall diagnostic accuracy of quantitative angiography

Quantitative Angiographic Indices

  • Angiographic Success
  • Binary Angiographic Restenosis
  • Late Lumen Loss