Feasibility of in vivo intravascular ultrasound tissue characterization in the detection of early vascular transplant rejection.

Abstract

Unprocessed ultrasound radiofrequency (RF) signal analysis has been shown to distinguish different tissue structures more reliably than gray-scale interpretation of conventional ultrasound images. The objective of this study was to test the feasibility of in vivo intravascular ultrasound (IVUS) RF signal analysis in an animal model of allograft rejection. Six cynomolgus monkeys underwent transplantation of 3-cm aortic allograft segments distal to the renal arteries from immunologically mismatched donors. IVUS imaging with a 30-MHz system was performed 84 to 105 days after the operation. RF signals were acquired from cross sections of the recipient and the allograft aortas in real time with a digitizer at 500 MHz with 8-bit resolution. Sixty-five cross sections and 68 regions of interest (31 in host aorta and 37 in allograft) were analyzed in the adventitial layer with a total number of 8568 vectors processed. For each region of interest, a weighted-average attenuation was calculated on the basis of the attenuation and length for each individual vector. Histological examination was performed at every cross section imaged by IVUS. When the gray-scale images of conventional IVUS scored by an independent observer were compared, no distinction between adventitia of the native aorta and allograft was possible. Analysis of the average RF backscatter power also showed no significant difference (70.32+/-3.55 versus 70.72+/-3.38 dB). However, the average attenuation of allografts was significantly lower than that of the host aortas (2.64+/-1.38 versus 4.02+/-1.16 dB/mm, P<0.001). Histology demonstrated a marked adventitial inflammatory response in all allografts, with no inflammation observed in the host aortas. In vivo IVUS tissue characterization can be performed during routine imaging. In this model of transplant vasculopathy, RF attenuation measurements were more sensitive than visual or quantitative gray-scale analysis.