Four-dimensional image processing of myocardial CT perfusion for improved image quality and noise reduction

Abstract

Background: Image noise and multiple sources of artifact may affect the accurate interpretation of myocardial CT perfusion (CTP) studies. Although artifact within the image is often time dependent, tissue characteristics remain unchanged irrespective of cardiac phase.

Objective: We assessed a new technique of 4-dimensional, spatiotemporal analysis, using redundant time domain information within additional phase acquisitions to reduce CTP image noise.

Methods: Four-dimensional analysis was assessed in a static phantom and in 10 CTP studies with invasive fractional flow reserve (FFR) correlation. For each voxel within the CTP study the distribution of local Hounsfield values was measured in both time and space with the use of a customized program within MATLAB software. These values were filtered to eliminate those likely to represent noise or rapidly changing beam hardening artifact. All CTP images were acquired within a single heartbeat with 320 detector-row CT. Image noise was quantified as the SD of voxel values within myocardial segments. Contrast was measured between normal and abnormal vascular territories as assessed by FFR.

Results: The mean image noise within the unprocessed CTP images was 30 HU (range, 23-42 HU). After 4-dimensional filtering the mean image noise was 22 HU (range, 15-29 HU). The mean reduction in image noise was 28% (P < 0.001). The mean contrast between normally perfused and ischemic segments was not significantly changed. The mean increase in contrast-to-noise ratio between ischemic territories and the myocardial average was 52% (P < 0.001).

Conclusion: Four-dimensional analysis of CTP significantly reduces image noise and may assist in the assessment of myocardial perfusion studies.