Research - Cardiac MR

 

PRELIMINARY CORRELATIVE RESULTS OF MR MYOCARDIAL PERFUSION RESERVE INDEX IMAGING

James E. Siebert, Radiology; Mark C. DeLano, Radiology;
Joel D. Eisenberg, Cardiology; Jason A. Gift, Radiology

Gift, Siebert, DeLano, Eisenberg

 

PURPOSE

To demonstrate the feasibility of practical multislice semiquantitative myocardial perfusion reserve index (MPRi) imaging of the LV myocardium at the native resolution of the MR image acquisition for the work-up of ischemic heart disease.

 

INTRODUCTION

Myocardial perfusion reserve quantifies the capacity of the circulatory response to a maximal increase in metabolic demand. MPR indicates the net circulatory consequence from coronary lesions and other vascular states, regardless of their morphological appearance, including the compensation by collateral flow.¹ Myocardial collaterals readily develop in response to ischemia induced by upstream stenosis or occlusion. In canine model, Ameroid constrictors will induce new coronary collaterals providing perfusion of ~35% of maximal normal blood flow in ~95% of dogs.² Recent improvements in perfusion acquisition methods now provide increased temporal and spatial resolution, SNR, and first-pass contrast enhancement ratio.^3-4 Semiquantitative MPR can be achieved with MR methods using current USFDA-approved contrast agents.^1,5

 

METHODS

High-speed multislice MR images were acquired during the first pass of Gd-contrast antecubital-vein injection (0.1 mmol/kg, 5 ml/sec) in both adenosine vasodilated ‘stress’, and ‘rest’ states.^3-4 Stress and rest dynamic image sequences for 7-9 short-axis slice locations spanning the LV were post processed to calculate the estimated MPRi image for each slice location (~6000 lines of IDL code by JES, JAG). The computation procedure applied for each slice location:

(1) Spatially register across each individual time series at each slice location. Solid-body registration partially compensates for diaphragm motion and cardiac phase jitter.⁷

(2) Determine a surface coil intensity correction for each slice location
See Fig. 1 & Fig. 2.⁶

(3) Calculate first-pass upslope parametric images.
The first-pass peak upslope event is detected at each pixel x,y to create the Upslope parametric images. Fig. 3 shows a typical example of the original stress and rest images at Image Index 10, the resulting Parametric Upslope images, and a plot of the time data for this slice location in stress and rest.

(4) Segment the LV blood pool and determine the input function normalization
Two image regions are determined: the LV blood pool mask (Fig. 4 red areas), the ROI for calculating the average blood pool upslopes for the MPR normalization calculation (blue areas; top 5% enhancing blood pool pixels). Segmentation can be based upon peak intensities achieved or enhancement arrival time.

(5) Perform warping image registration of rest slope image onto stress slope image
Different heart rates during the stress and resting states results in a cardiac phase shift between the 2 sets of registered images at the same slice location--evident in Fig. 3 & Fig. 4. For pixel-wise processing of the 2 image series, the Rest Upslope parametric image is warp registered onto the Stress Upslope image. The user clicks on corresponding landmark pairs around the LV myocardium.

(6) Calculate the MPR index image by:

where S_{MYO.stress/rest} is the computed first-pass upslope at pixel (x,y), and S_{LVBP.stress/rest} is the upslope of LV blood pool input function for normalization.⁵  See Fig. 6.

 

RESULTS

Fig. 6 shows resulting MPRi images for patient having an 80% right coronary artery stenosis. MPRi values within normal myocardium range from ~0.5–2.5, about half the expected MPR values. MPRi values within perfusion defects range from 0.0–1.0. Distributions of normal and abnormal myocardium MPR values are separated suggesting that this simple index approach may be useful for clinical discrimination.

 

DISCUSSION

Three morphological issues challenge the computation of MPR parametric images:

(1) Cardiac phase shifts between the stress and rest acquisitions arise from the difference in heart rate during the two test states. These morphological differences necessitate a warping image registration. (2) Stress-rest difference in diaphragm positions cause mismatch in ventricular anatomy that must be compensated via warping image registration. (3) Cardiac phase jitter within a given slice location image sequence results in variable partial volume averaging of the myocardial edge voxels, which introduces variability over time in LV edge features. Stress-rest mismatches of myocardial anatomy may pose the ultimate limitation of MPR imaging.

Intensity correction of surface coil reception modulations is needed to determine the input function normalization (slope spatial dependency), to achieve postprocessing automation, as well as to improve the qualitative assessment of cardiac perfusion exams (Fig. 2).

The feasibility of high-resolution practical MPRi images has been demonstrated. MPRi imaging may provide quantitative objective information to reduce variability in perfusion exam interpretation, and to document MR myocardial perfusion exam results.

 

ACKNOWLEDGMENT

Research supported in part by GE Medical Systems. Patient data provided by Dr. Steven Wolff, ICT, Woodbury, NY.

 

REFERENCES 

1. Wilke N, Jerosch-Herold M, et al. Radiology 204:373-384, 1997
2. Unger EF, Banai S, et al. Am J Physiol 266(4 Pt 2):H1588-95, 1994
3. Ding S, Wolff SD, Epstein FH. Magn Reson Med 39(4):514-519, 1998
4. Slavin GS, Wolff SD, et al. Proc ISMRM 8th Mtg, p 36, 2000
5. Nidal A-S, Nagel E, et al. Circulation 101:1379-1383, 2000
6. Siebert JE, DeLano MC, et al. Proc ISMRM 7th Mtg, p2179, 1999
7. Gupta SN, Foo TK, et al. Proc ISMRM 7th Mtg, p2178, 1999

 

 

Patient Services | Department | Anatomy | Education | Residency | Research | News & Events | MSU | CHM | COM