Construction of a Statistical Model for Cardiac Motion Analysis Using Nonrigid Image Registration

Chandrashekara, R.; Rao, Anil; Sanchez-Ortiz, Gerardo I.; Mohiaddin, Raad H.; Rueckert, Daniel

doi:10.1007/978-3-540-45087-0_50

Cited by 52 publications

(45 citation statements)

References 18 publications

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“…In terms of the imaging modality, cine MR and CT are often used to infer shape information (Frangi et al, 2002;Fonseca et al, 2011;Hoogendoorn et al, 2013), whereas tagged MR, cine MR and US are often used to capture cardiac motion (Chandrashekara et al, 2003;Suinesiaputra et al, 2009;Duchateau et al, 2011). In order to characterise the cardiac myocardial fibre architecture, diffusion tensor MRI (DT-MRI) is used for imaging ex vivo human hearts (Lombaert et al, 2012).…”

Section: Related Workmentioning

confidence: 99%

A bi-ventricular cardiac atlas built from 1000+ high resolution MR images of healthy subjects and an analysis of shape and motion

Bai

Shi

Marvao

et al. 2015

Medical Image Analysis

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126

116

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Section: Related Workmentioning

confidence: 99%

A bi-ventricular cardiac atlas built from 1000+ high resolution MR images of healthy subjects and an analysis of shape and motion

Bai

Shi

Marvao

et al. 2015

Medical Image Analysis

Self Cite

126

116

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“…Authors have based their models on different geometric and physical representations, including spring-mass models (Nastar and Ayache, 1996), triangle-based finite element models (Park et al, 1996;McInerney and Terzopoulos, 1995), simplex meshes (Montagnat et al, 1999;Gérard et al, 2002), tetrahedral finite element models (Pham et al, 2001;Sermesant et al, 2002;Papademetris et al, 2001), level sets (Charnoz et al, 2003;Paragios, 2002;Lin et al, 2002), 3D B-Spline deformation fields (Bardinet et al, 1996;Chandrashekara et al, 2003) and statistical shape and appearance models (Frangi et al, 2002;Ü zü mcü et al, 2003).…”

Section: Segmentation and Tracking Of Cardiac Imagesmentioning

confidence: 99%

4D deformable models with temporal constraints: application to 4D cardiac image segmentation

Montagnat¹,

Delingette²

2005

Medical Image Analysis

147

105

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Segmentation of time series of 3D cardiac images is clinically used for the assessment of the mechanical function of the left ventricle. To take into account the 4D (3D + T) nature of those images, we propose to extend the deformable surface framework by introducing time-dependent constraints. Thus, in addition to computing an internal force for enforcing the regularity of the deformable model, prior motion knowledge is introduced in the deformation process through either temporal smoothing or trajectory constraints.In this paper, deformable surfaces are represented as simplex meshes owing to their generality and their ability to compute mean curvature at each vertex. The segmentation accuracy of this 4D deformable model is estimated on synthetic SPECT image sequences for which a ground truth about the LV volume is known. Segmentation of non-synthetic SPECT and other modalities 4D images is also discussed.

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“…6 The motion of the heart during the different cardiac phases consists of radial, longitudinal, and circumferential displacements. [19][20][21][22] The base of the heart contracts, ejecting blood from the ventricles. This contraction changes the shape and volume of the heart, causing the lung to expand and slide against the heart.…”

Section: Introductionmentioning

confidence: 99%

Modeling and incorporating cardiac‐induced lung tissue motion in a breathing motion model

et al. 2014

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Purpose:The purpose of this work is to develop a cardiac-induced lung motion model to be integrated into an existing breathing motion model. Methods:The authors' proposed cardiac-induced lung motion model represents the lung tissue's specific response to the subject's cardiac cycle. The model is mathematically defined as a product of a converging polynomial function h of the cardiac phase (c) and the maximum displacement γ ( X 0 ) of each voxel ( X 0 ) among all the cardiac phases. The function h(c) was estimated from cardiac-gated MR imaging of ten healthy volunteers using an Akaike Information Criteria optimization algorithm. For each volunteer, a total of 24 short-axis and 18 radial planar views were acquired on a 1.5 T MR scanner during a series of 12-15 s breath-hold maneuvers. Each view contained 30 temporal frames of equal time-duration beginning with the end-diastolic cardiac phase. The frames in each of the planar views were resampled to create a set of three-dimensional (3D) anatomical volumes representing thoracic anatomy at different cardiac phases. A 3D multiresolution optical flow deformable image registration algorithm was used to quantify the difference in tissue position between the end-diastolic cardiac phase and the remaining cardiac phases. To account for image noise, voxel displacements whose maximum values were less than 0.3 mm, were excluded. In addition, the blood vessels were segmented and excluded in order to eliminate registration artifacts caused by blood-flow. Results: The average cardiac-induced lung motions for displacements greater than 0.3 mm were found to be 0.86 ± 0.74 and 0.97 ± 0.93 mm in the left and right lungs, respectively. The average model residual error for the ten healthy volunteers was found to be 0.29 ± 0.08 mm in the left lung and 0.38 ± 0.14 mm in the right lung for tissue displacements greater than 0.3 mm. The relative error decreased with increasing cardiac-induced lung tissue motion. While the relative error was > 60% for submillimeter cardiac-induced lung tissue motion, the relative error decreased to < 5% for cardiac-induced lung tissue motion that exceeded 10 mm in displacement. Conclusions:The authors' studies implied that modeling and including cardiac-induced lung motion would improve breathing motion model accuracy for tissues with cardiac-induced motion greater than 0.3 mm.

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Construction of a Statistical Model for Cardiac Motion Analysis Using Nonrigid Image Registration

Cited by 52 publications

References 18 publications

A bi-ventricular cardiac atlas built from 1000+ high resolution MR images of healthy subjects and an analysis of shape and motion

A bi-ventricular cardiac atlas built from 1000+ high resolution MR images of healthy subjects and an analysis of shape and motion

4D deformable models with temporal constraints: application to 4D cardiac image segmentation

Modeling and incorporating cardiac‐induced lung tissue motion in a breathing motion model

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