Dataset Information

Nonrigid active shape model-based registration framework for motion correction of cardiac T1 mapping.

ABSTRACT: PURPOSE:Accurate reconstruction of myocardial T1 maps from a series of T1 -weighted images consists of cardiac motions induced from breathing and diaphragmatic drifts. We propose and evaluate a new framework based on active shape models to correct for motion in myocardial T1 maps. METHODS:Multiple appearance models were built at different inversion time intervals to model the blood-myocardium contrast and brightness changes during the longitudinal relaxation. Myocardial inner and outer borders were automatically segmented using the built models, and the extracted contours were used to register the T1 -weighted images. Data acquired from 210 patients using a free-breathing acquisition protocol were used to train and evaluate the proposed framework. Two independent readers evaluated the quality of the T1 maps before and after correction using a four-point score. The mean absolute distance and Dice index were used to validate the registration process. RESULTS:The testing data set from 180 patients at 5 short axial slices showed a significant decrease of mean absolute distance (from 3.3?±?1.6 to 2.3?±?0.8?mm, P?

SUBMITTER: El-Rewaidy H

PROVIDER: S-EPMC5941305 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Nonrigid active shape model-based registration framework for motion correction of cardiac T<sub>1</sub> mapping.

El-Rewaidy Hossam H Nezafat Maryam M Jang Jihye J Nakamori Shiro S Fahmy Ahmed S AS Nezafat Reza R

Magnetic resonance in medicine 20180103 2

<h4>Purpose</h4>Accurate reconstruction of myocardial T<sub>1</sub> maps from a series of T<sub>1</sub> -weighted images consists of cardiac motions induced from breathing and diaphragmatic drifts. We propose and evaluate a new framework based on active shape models to correct for motion in myocardial T<sub>1</sub> maps.<h4>Methods</h4>Multiple appearance models were built at different inversion time intervals to model the blood-myocardium contrast and brightness changes during the longitudinal ...[more]

PMID: 29314198

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Project description:PURPOSE: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. MATERIALS AND METHODS:Our framework combines discrete feature matching for large displacement estimation with a dense variational optical flow formulation in a multithreaded architecture. This framework was evaluated on 291 clinical subjects to register 1.5T and 3.0T steady-state free-precession (FISP) and fast low-angle shot (FLASH) dynamic contrast myocardial perfusion images, arterial input function (AIF) images, and proton density (PD)-weighted images acquired under breath-hold (BH) and free-breath (FB) settings. RESULTS:Our method significantly improved frame-to-frame appearance consistency compared to raw series, expressed in correlation coefficient (R2 ?=?0.996?±?3.735E-3 vs. 0.978?±?2.024E-2, P < 0.0001) and mutual information (3.823?±?4.098E-1 vs. 2.967?±?4.697E-1, P < 0.0001). It is applicable to both BH (R2 ?=?0.998?±?3.217E-3 vs. 0.990?±?7.527E-3) and FB (R2 ?=?0.995?±?3.410E-3 vs. 0.968?±?2.257E-3) paradigms as well as FISP and FLASH sequences. The method registers PD images to perfusion T1 series (9.70% max increase in R2 vs. no registration, P < 0.001) and also corrects motion in low-resolution AIF series (R2 ?=?0.987?±?1.180E-2 vs. 0.964?±?3.860E-2, P < 0.001). Finally, we showed the myocardial perfusion contrast dynamic was preserved in the motion-corrected images compared to the raw series (R2 ?=?0.995?±?6.420E-3). CONCLUSION:The critical step of motion correction prior to pixel-wise cardiac MR perfusion quantification can be performed with the proposed universal system. It is applicable to a wide range of perfusion series and auxiliary images with different acquisition settings. LEVEL OF EVIDENCE:3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017;46:1060-1072.

Project description:Respiratory and cardiac motion is the most serious limitation to whole-body PET, resulting in spatial resolution close to 1 cm. Furthermore, motion-induced inconsistencies in the attenuation measurements often lead to significant artifacts in the reconstructed images. Gating can remove motion artifacts at the cost of increased noise. This paper presents an approach to respiratory motion correction using simultaneous PET/MRI to demonstrate initial results in phantoms, rabbits, and nonhuman primates and discusses the prospects for clinical application.Studies with a deformable phantom, a free-breathing primate, and rabbits implanted with radioactive beads were performed with simultaneous PET/MRI. Motion fields were estimated from concurrently acquired tagged MR images using 2 B-spline nonrigid image registration methods and incorporated into a PET list-mode ordered-subsets expectation maximization algorithm. Using the measured motion fields to transform both the emission data and the attenuation data, we could use all the coincidence data to reconstruct any phase of the respiratory cycle. We compared the resulting SNR and the channelized Hotelling observer (CHO) detection signal-to-noise ratio (SNR) in the motion-corrected reconstruction with the results obtained from standard gating and uncorrected studies.Motion correction virtually eliminated motion blur without reducing SNR, yielding images with SNR comparable to those obtained by gating with 5-8 times longer acquisitions in all studies. The CHO study in dynamic phantoms demonstrated a significant improvement (166%-276%) in lesion detection SNR with MRI-based motion correction as compared with gating (P < 0.001). This improvement was 43%-92% for large motion compared with lesion detection without motion correction (P < 0.001). CHO SNR in the rabbit studies confirmed these results.Tagged MRI motion correction in simultaneous PET/MRI significantly improves lesion detection compared with respiratory gating and no motion correction while reducing radiation dose. In vivo primate and rabbit studies confirmed the improvement in PET image quality and provide the rationale for evaluation in simultaneous whole-body PET/MRI clinical studies.

Project description:To develop a nonrigid motion estimation method that is robust to heterogeneous intensity inconsistencies amongst the image pairs or image sequence.Intensity and contrast variations, as in dynamic contrast enhanced magnetic resonance imaging, present a considerable challenge to registration methods based on general discrepancy metrics. In this study, the authors propose and validate a novel method that is robust to such variations by utilizing shape features. The geometry of interest (GOI) is represented with a flexible zero level set, segmented via well-behaved regularized optimization. The optimization energy drives the zero level set to high image gradient regions, and regularizes it with area and curvature priors. The resulting shape exhibits high consistency even in the presence of intensity or contrast variations. Subsequently, a multiscale nonrigid registration is performed to seek a regular deformation field that minimizes shape discrepancy in the vicinity of GOIs.To establish the working principle, realistic 2D and 3D images were subject to simulated nonrigid motion and synthetic intensity variations, so as to enable quantitative evaluation of registration performance. The proposed method was benchmarked against three alternative registration approaches, specifically, optical flow, B-spline based mutual information, and multimodality demons. When intensity consistency was satisfied, all methods had comparable registration accuracy for the GOIs. When intensities among registration pairs were inconsistent, however, the proposed method yielded pronounced improvement in registration accuracy, with an approximate fivefold reduction in mean absolute error (MAE = 2.25 mm, SD = 0.98 mm), compared to optical flow (MAE = 9.23 mm, SD = 5.36 mm), B-spline based mutual information (MAE = 9.57 mm, SD = 8.74 mm) and mutimodality demons (MAE = 10.07 mm, SD = 4.03 mm). Applying the proposed method on a real MR image sequence also provided qualitatively appealing results, demonstrating good feasibility and applicability of the proposed method.The authors have developed a novel method to estimate the nonrigid motion of GOIs in the presence of spatial intensity and contrast variations, taking advantage of robust shape features. Quantitative analysis and qualitative evaluation demonstrated good promise of the proposed method. Further clinical assessment and validation is being performed.

Dataset Information

Nonrigid active shape model-based registration framework for motion correction of cardiac T1 mapping.

Publications

Nonrigid active shape model-based registration framework for motion correction of cardiac T<sub>1</sub> mapping.

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