Hui Xue

Extracellular volume fraction mapping in the myocardium, part 1: evaluation of an automated method

BackgroundDisturbances in the myocardial extracellular volume fraction (ECV), such as diffuse or focal myocardial fibrosis or edema, are hallmarks of heart disease. Diffuse ECV changes are difficult to assess or quantify with cardiovascular magnetic resonance (CMR) using conventional late gadolinium enhancement (LGE), or pre- or post-contrast T1-mapping alone. ECV measurement circumvents factors that confound T1-weighted images or T1-maps, and has been shown to correlate well with diffuse myocardial fibrosis. The goal of this study was to develop and evaluate an automated method for producing a pixel-wise map of ECV that would be adequately robust for clinical work flow.MethodsECV maps were automatically generated from T1-maps acquired pre- and post-contrast calibrated by blood hematocrit. The algorithm incorporates correction of respiratory motion that occurs due to insufficient breath-holding and due to misregistration between breath-holds, as well as automated identification of the blood pool. Images were visually scored on a 5-point scale from non-diagnostic (1) to excellent (5).ResultsThe quality score of ECV maps was 4.23u2009±u20090.83 (mu2009±u2009SD), scored for nu2009=u2009600 maps from 338 patients with 83% either excellent or good. Co-registration of the pre-and post-contrast images improved the image quality for ECV maps in 81% of the cases. ECV of normal myocardium was 25.4u2009±u20092.5% (mu2009±u2009SD) using motion correction and co-registration values and was 31.5u2009±u20098.7% without motion correction and co-registration.ConclusionsFully automated motion correction and co-registration of breath-holds significantly improve the quality of ECV maps, thus making the generation of ECV-maps feasible for clinical work flow.

Extracellular volume fraction mapping in the myocardium, part 2: initial clinical experience

BackgroundDiffuse myocardial fibrosis, and to a lesser extent global myocardial edema, are important processes in heart disease which are difficult to assess or quantify with cardiovascular magnetic resonance (CMR) using conventional late gadolinium enhancement (LGE) or T1-mapping. Measurement of the myocardial extracellular volume fraction (ECV) circumvents factors that confound T1-weighted images or T1-maps. We hypothesized that quantitative assessment of myocardial ECV would be clinically useful for detecting both focal and diffuse myocardial abnormalities in a variety of common and uncommon heart diseases.MethodsA total of 156 subjects were imaged including 62 with normal findings, 33 patients with chronic myocardial infarction (MI), 33 with hypertrophic cardiomyopathy (HCM), 15 with non-ischemic dilated cardiomyopathy (DCM), 7 with acute myocarditis, 4 with cardiac amyloidosis, and 2 with systemic capillary leak syndrome (SCLS). Motion corrected ECV maps were generated automatically from T1-maps acquired pre- and post-contrast calibrated by blood hematocrit. Abnormally-elevated ECV was defined as >2SD from the mean ECV in individuals with normal findings. In HCM the size of regions of LGE was quantified as the region >2 SD from remote.ResultsMean ECV of 62 normal individuals was 25.4u2009±u20092.5% (mu2009±u2009SD), normal range 20.4%-30.4%. Mean ECV within the core of chronic myocardial infarctions (without MVO) (Nu2009=u200933) measured 68.5u2009±u20098.6% (pu2009<u20090.001 vs normal). In HCM, the extent of abnormally elevated ECV correlated to the extent of LGE (ru2009=u20090.72, pu2009<u20090.001) but had a systematically greater extent by ECV (mean difference 19u2009±u20097% of slice). Abnormally elevated ECV was identified in 4 of 16 patients with non-ischemic DCM (38.1u2009±u20091.9% (pu2009<u20090.001 vs normal) and LGE in the same slice appeared “normal” in 2 of these 4 patients. Mean ECV values in other disease entities ranged 32-60% for cardiac amyloidosis (Nu2009=u20094), 40-41% for systemic capillary leak syndrome (Nu2009=u20092), and 39-56% within abnormal regions affected by myocarditis (Nu2009=u20097).ConclusionsECV mapping appears promising to complement LGE imaging in cases of more homogenously diffuse disease. The ability to display ECV maps in units that are physiologically intuitive and may be interpreted on an absolute scale offers the potential for detection of diffuse disease and measurement of the extent and severity of abnormal regions.

Motion correction for myocardial T1 mapping using image registration with synthetic image estimation

Quantification of myocardial T1 relaxation has potential value in the diagnosis of both ischemic and nonischemic cardiomyopathies. Image acquisition using the modified Look‐Locker inversion recovery technique is clinically feasible for T1 mapping. However, respiratory motion limits its applicability and degrades the accuracy of T1 estimation. The robust registration of acquired inversion recovery images is particularly challenging due to the large changes in image contrast, especially for those images acquired near the signal null point of the inversion recovery and other inversion times for which there is little tissue contrast. In this article, we propose a novel motion correction algorithm. This approach is based on estimating synthetic images presenting contrast changes similar to the acquired images. The estimation of synthetic images is formulated as a variational energy minimization problem. Validation on a consecutive patient data cohort shows that this strategy can perform robust nonrigid registration to align inversion recovery images experiencing significant motion and lead to suppression of motion induced artifacts in the T1 map. Magn Reson Med, 2011.

Phase-Sensitive Inversion Recovery for Myocardial T1 Mapping with Motion Correction and Parametric Fitting

The assessment of myocardial fibrosis and extracellular volume requires accurate estimation of myocardial T1s. While image acquisition using the modified Look‐Locker inversion recovery technique is clinically feasible for myocardial T1 mapping, respiratory motion can limit its applicability. Moreover, the conventional T1 fitting approach using the magnitude inversion recovery images can lead to less stable T1 estimates and increased computational cost. In this article, we propose a novel T1 mapping scheme that is based on phase‐sensitive image reconstruction and the restoration of polarity of the MR signal after inversion. The motion correction is achieved by registering the reconstructed images after background phase removal. The restored signal polarity of the inversion recovery signal helps the T1 fitting resulting in improved quality of the T1 map and reducing the computational cost. Quantitative validation on a data cohort of 45 patients proves the robustness of the proposed method against varying image contrast. Compared to the magnitude T1 fitting, the proposed phase‐sensitive method leads to less fluctuation in T1 estimates. Magn Reson Med, 2013.

Myocardial T2 Mapping With Respiratory Navigator and Automatic Nonrigid Motion Correction

Quantitative T2 mapping was recently shown to be superior to T2‐weighted imaging in detecting T2 changes across myocardium. Pixel‐wise T2 mapping is sensitive to misregistration between the images used to generate the parameter map. In this study, utility of two motion‐compensation strategies—(i) navigator gating with prospective slice correction and (ii) nonrigid registration—was investigated for myocardial T2 mapping in short axis and horizontal long axis views. Navigator gating provides respiratory motion compensation, whereas registration corrects for residual cardiac and respiratory motion between images; thus, the two strategies provided complementary functions. When these were combined, respiratory‐motion‐induced T2 variability, as measured by both standard deviation and interquartile range, was comparable to that in breath‐hold T2 maps. In normal subjects, this combined motion‐compensation strategy increased the percentage of myocardium with T2 measured to be within normal range from 60.1% to 92.2% in short axis and 62.3% to 92.7% in horizontal long axis. The new motion‐compensated T2 mapping technique, which combines navigator gating, prospective slice correction, and nonrigid registration to provide through‐plane and in‐plane motion correction, enables a method for fully automatic and robust free‐breathing T2 mapping. Magn Reson Med, 2012.

Combining Registration and Minimum Surfaces for the Segmentation of the Left Ventricle in Cardiac Cine MR Images

This paper describes a system to automatically segment the left ventricle in all slices and all phases of cardiac cine magnetic resonance datasets. After localizing the left ventricle blood pool using motion, thresholding and clustering, slices are segmented sequentially. For each slice, deformable registration is used to align all the phases, candidates contours are recovered in the average image using shortest paths, and a minimal surface is built to generate the final contours. The advantage of our method is that the resulting contours follow the edges in each phase and are consistent over time. We demonstrate using 19 patient examples that the results are very good. The RMS distance between ground truth and our segmentation is only 1.6 pixels (2.7 mm) and the Dice coefficient is 0.89.

Unsupervised Inline Analysis of Cardiac Perfusion MRI

In this paper we first discuss the technical challenges preventing an automated analysis of cardiac perfusion MR images and subsequently present a fully unsupervised workflow to address the problems. The proposed solution consists of key-frame detection, consecutive motion compensation, surface coil inhomogeneity correction using proton density images and robust generation of pixel-wise perfusion parameter maps. The entire processing chain has been implemented on clinical MR systems to achieve unsupervised inline analysis of perfusion MRI. Validation results are reported for 260 perfusion time series, demonstrating feasibility of the approach.

Automatic segmentation of the myocardium in cine MR images using deformable registration

This paper proposes a system to automatically segment the left ventricle in cardiac MR cine images. Individual frames are segmented using a shortest path algorithm and temporal consistency is enforced through the backward and forward deformation fields of an inverse consistent deformable registration. In addition, a segmentation of the mitral valve plane is obtained from long axis images. This algorithm was applied to 95 datasets as part of the STACOM11 4D LV Segmentation Challenge. We analyze the results and evaluate the strengths and weaknesses of our system.

Efficient symmetric and inverse-consistent deformable registration through interleaved optimization

Symmetry and inverse consistency are two important features for deformable image registration in medical imaging analysis. This work presents a novel registration method computing symmetric and inverse-consistent image alignment efficiently while preserving high accuracy and consistency of the mapping. This is achieved by optimizing a symmetric energy functional estimating forward and backward transformations constrained by the transformations being inverse to each other. In other words, this approach uses an interleaved optimization scheme borrowed from multiobjective optimization theory constrained by an inverse-consistency criterium. The new optimization scheme provides an efficient search in the space of diffeomorphisms while solving the symmetric registration problem. Moreover, it is not bound to any specific optimizer or energy functional other than to the requirement of being well-defined. In our experiments on clinical cardiac data, superior performance compared to standard, one-directional registration is achieved. The resulting inverse-consistency and symmetry errors match previously reported values while being computed more efficiently. This general approach addresses a clinical need for consistent, highly accurate image alignment achieved in a practically accepted time-frame.

Assessment of Cardiac Motion Effects on the Fiber Architecture of the Human Heart In Vivo

The use of diffusion tensor imaging (DTI) for studying the human heart in vivo is very challenging due to cardiac motion. This paper assesses the effects of cardiac motion on the human myocardial fiber architecture. To this end, a model for analyzing the effects of cardiac motion on signal intensity is presented. A Monte-Carlo simulation based on polarized light imaging data is then performed to calculate the diffusion signals obtained by the displacement of water molecules, which generate diffusion weighted (DW) images. Rician noise and in vivo motion data obtained from DENSE acquisition are added to the simulated cardiac DW images to produce motion-induced datasets. An algorithm based on principal components analysis filtering and temporal maximum intensity projection (PCATMIP) is used to compensate for motion-induced signal loss. Diffusion tensor parameters derived from motion-reduced DW images are compared to those derived from the original simulated DW images. Finally, to assess cardiac motion effects on in vivo fiber architecture, in vivo cardiac DTI data processed by PCATMIP are compared to those obtained from one trigger delay (TD) or one single phase acquisition. The results showed that cardiac motion produced overestimated fractional anisotropy and mean diffusivity as well as a narrower range of fiber angles. The combined use of shifted TD acquisitions and postprocessing based on image registration and PCATMIP effectively improved the quality of in vivo DW images and subsequently, the measurement accuracy of fiber architecture properties. This suggests new solutions to the problems associated with obtaining in vivo human myocardial fiber architecture properties in clinical conditions.