An Open Benchmark Challenge for Motion Correction of Myocardial Perfusion MRI

Abstract

Cardiac magnetic resonance perfusion examinations enable non-invasive quantification of myocardial blood flow. However, motion between frames due to breathing must be corrected for quantitative analysis. Although several methods have been proposed, there is a lack of widely available benchmarks to compare different algorithms. We sought to compare many algorithms from several groups in an open benchmark challenge. Nine clinical studies from two different centers comprising normal and diseased myocardium at both rest and stress were made available for this study. The primary validation measure was regional myocardial blood flow based on the transfer coefficient (Ktrans), which was computed using a compartment model and the myocardial perfusion reserve (MPR) index. The ground truth was calculated using contours drawn manually on all frames by a single observer, and visually inspected by a second observer. Six groups participated and 19 different motion correction algorithms were compared. Each method used one of three different motion models: rigid, global affine or local deformation. The similarity metric also varied with methods employing either sum-of-squared differences, mutual information or cross-correlation. There were no significant differences in Ktrans or MPR compared across different motion models or similarity metrics. Compared with the ground truth, only Ktrans for the sum of squared differences metric, and for local deformation motion models, had significant bias. In conclusion, the open benchmark enabled evaluation of clinical perfusion indices over a wide range of methods. In particular, there was no benefit of non-rigid registration techniques over the other methods evaluated in this study. The benchmark data and results are available from the Cardiac Atlas Project (www.cardiacatlas.org).

Influence of contrast agent dispersion on bolus‐based MRI myocardial perfusion measurements: A computational fluid dynamics study

“…In fact, several MRI studies reported an underestimation of MBF . In those studies, the MBF values were analyzed with regard to issues of quantification algorithms, applied MRI signal intensity corrections, interstudy repeatability, or motion correction .…”

“…In fact, several MRI studies reported an underestimation of MBF . In those studies, the MBF values were analyzed with regard to issues of quantification algorithms, applied MRI signal intensity corrections, interstudy repeatability, or motion correction . For hyperemia, MBF underestimation was more pronounced in comparison to microsphere perfusion measurements; however, it is well‐known that the microsphere technique somewhat overestimates MBF in regions of high flow, and underestimates MBF in regions of low flow .…”

“…3,[66][67][68][69][70][71][72][73] In those studies, the MBF values were analyzed with regard to issues of quantification algorithms, 69 applied MRI signal intensity corrections, 70 interstudy repeatability, 71 or motion correction. 72 For hyperemia, MBF underestimation was more pronounced in comparison to microsphere perfusion measurements 70,73 ; however, it is well-known that the microsphere technique somewhat overestimates MBF in regions of high flow, and underestimates MBF in regions of low flow. 74 In general, the observation in those studies that quantitative perfusion MRI somewhat underestimates MBF compared to the microsphere technique agrees well with our simulation results.…”

“…The comparison of our results with existing PET data is also not straightforward. In Miller, 17 Even though in several of these studies the obtained MBF values are calculated on a regional 69,[71][72][73]75 or pixel-by-pixel basis, 3 an association of the quantified tissue voxel or segment with a specific coronary flow territory is not performed. Hence, an assessment of quantified MBF values in relation to the distance traveled by CA between the locations of AIF and perfusion quantification is difficult.…”

Quantitative 3D myocardial perfusion with an efficient arterial input function

“…Registration was used to compensate for the motion from the free‐breathing perfusion acquisitions. Rigid body registration in 2D has been shown to provide comparable perfusion values compared with nonrigid registration techniques . Although little study has been done in extending this concept to 3D, a 3D rigid‐body registration technique including through‐plane motion was found to provide reliable results for this work.…”