Mitchel Benovoy

Automatic nonrigid motion correction for quantitative first-pass cardiac MR perfusion imaging

First-pass dynamic contrast-enhanced cardiac magnetic resonance imaging is an increasingly important diagnostic tool for coronary artery disease. It is typically performed with breath holding and electrocardiogram gating to minimize motion, but movement caused by residual respiration, cardiac arrhythmia, or missed gating triggers during image acquisition will induce nonrigid deformations of the myocardium that can hamper interpretation and perfusion quantification. We propose an automatic nonrigid image registration framework to correct motion in a series of cardiac magnetic resonance perfusion images. Our method employs reference frame detection combined with robust flow field estimation using a large displacement optical flow formulation coupled with post-hoc image warping and flow field distortion correction. This framework is multi-threadable and can be applied to standard myocardial series, arterial input function series, and proton density weighted images to facilitate perfusion quantification.

Robust universal nonrigid motion correction framework for first-pass cardiac MR perfusion imaging.

To present and assess an automatic nonrigid image registration framework that compensates motion in cardiac magnetic resonance imaging (MRI) perfusion series and auxiliary images acquired under a wide range of conditions to facilitate myocardial perfusion quantification.

Automated measurement of arterial input function in first-pass myocardial perfusion magnetic resonance images using independent component analysis

Quantitative assessment of first-pass cardiac magnetic resonance (CMR) perfusion imaging is useful for detecting coronary artery disease, but it requires the measurement of the arterial input function (AIF) from the left ventricle. This is usually done manually, which is time consuming and subjective. This study presents an automated method for measuring the AIF from the first-pass CMR perfusion images. It was tested on 194 clinical perfusion studies and compared with manual reference measurements. Our results show the proposed method successfully measured 98.79% of the perfusion series, with manual and automated measurements strongly correlating. Temporal statistics were similar for both measurements, showing agreement between the automated and manual AIFs. The automated method, however, more accurately selected the brightest left ventricle pixels and excluded papillary muscles. These improvements may help make AIF measurement and quantitative CMR myocardial perfusion analysis more accurate and readily available.

Fully automated pixel-wise myocardial blood flow quantification with first-pass perfusion CMR

Methods Rest and adenosine stress perfusion imaging was performed on 17 normal volunteers. A saturation recovery SSFP dual-sequence technique was used to acquire three myocardial slices and an arterial input function (AIF) image series. A proton-density weighted image was acquired at the beginning of each series. Fully quantitative perfusion pixel maps were generated by an automated processing method, including non-rigid motion correction, surface-coil intensity correction, AIF and myocardial signal and contrast timing detection, and model constrained deconvolution. The results of the automatically generated MBF pixel maps were compared with manual quantification using an 18-segment model.

Correlations and validations of dual-bolus and dual-sequence quantification of first-pass myocardial perfusion CMR in humans and canines

Methods CMR perfusion imaging was performed in six canines and thirty patients at 1.5T using dual-bolus (0.005 and 0.05 mmol/kg Gd-DTPA) and dual-sequence techniques with 1RR, 90° composite pulse, 50° SSFP readout, saturation recovery 90 ms, TR 2.4 ms, TE 1.2 ms, matrix size 128 × 80. A low TE 0.6 ms, low-resolution 64 × 48 FLASH image series was also acquired. The AIF was measured from the low-dose high-resolution series (DB), the high-dose low-resolution series (DS), and the high-dose high-resolution conventional single-bolus series (SB). Myocardial time intensity curves were analyzed on a midslice based on 6 transmural sectors and quantified by model-constrained deconvolution.