Strain measurement in the left ventricle during systole with deformable image registration

Abstract

The objective of this study was to validate a deformable image registration technique, termed Hyperelastic Warping, for left ventricular strain measurement during systole using cine-gated, nontagged MR images with strains measured from tagged MRI. The technique combines deformation from high resolution, non-tagged MR image data with a detailed computational model, including estimated myocardial material properties, fiber direction, and active fiber contraction, to provide a comprehensive description of myocardial contractile function. A normal volunteer (male, age 30) with no history of cardiac pathology was imaged with a 1.5 T Siemens Avanto clinical scanner using a TrueFISP imaging sequence and a 32-channel cardiac coil. Both tagged and non-tagged cine MR images were obtained. The Hyperelastic Warping solution was evolved using a series of non-tagged images in ten intermediate phases from end-diastole to end-systole. The solution may be considered as ten separate warping problems with multiple templates and targets. At each stage, an active contraction was initially applied to a finite element model, and then image-based warping penalty forces were utilized to generate the final registration. Warping results for circumferential strain (R2 = 0.75) and radial strain (R2 = 0.78) were strongly correlated with results obtained from tagged MR images analyzed with a Harmonic Phase (HARP) algorithm. Results for fiber stretch, LV twist, and transmural strain distributions were in good agreement with experimental values in the literature. In conclusion, Hyperelastic Warping provides a unique alternative for quantifying regional LV deformation during systole without the need for tags.

Accurate High-Resolution Measurements of 3-D Tissue Dynamics With Registration-Enhanced Displacement Encoded MRI

“…By correctly estimating the offset, the proposed approach generated the most accurate displacement measurement, with the corresponding errors reduced to 0.6 ± 2.8 a.u., 7.2 ± 12%, and 4.7.± 6.9°. The relatively poor performance of the registration method was expected, given the phantom’s lack of internal texture, which can be compensated by tags [62], material properties [42], [43], or the inherent nature of DENSE provided adequate correction of offset bias [63]. In reconstruction schemes involving DENSE (i.e., the bulk motion-corrected and proposed combined methods), the effects of image noise can be seen in the jagged border of the reverse-estimated initial-position image.…”

“…As a start, instead of tuning Hyperelastic Warping towards automation (i.e., material parameter or geometry independence), as was done in the current study, it can be adjusted to include adequate geometrical, material, and load characterization [4], [42], [43], [66] to deliver a much more comprehensive mechanical analysis, like approximation of fiber shortening and stress. Such approach may be susceptible to element inversion, which was avoided here by allowing volumetric deformation, but can also be addressed with suitable material characterization including fiber directionality and active contraction.…”

“…The resultant displacement field ${\stackrel{true\to }{U}}_{w}false(\overrightarrow{X}false)=\overrightarrow{X}-\overrightarrow{x}false(\overrightarrow{X}false)$ (or ${\stackrel{true\to }{u}}_{w}false(\overrightarrow{r}false)$ in spatial coordinates) corresponds to translations of the FE nodes. Magnitude image information is used to drive a mesh towards deformation by means of $$\overrightarrow{b}false(\overrightarrow{X}false)·\overrightarrow{\eta }=\lambda true([Tfalse(\overrightarrow{X}false)-Sfalse(\overrightarrow{\phi }false)]\frac{\partial Sfalse(\overrightarrow{\phi }false)}{\partial \overrightarrow{\phi }}·\overrightarrow{\eta })$$ where $\overrightarrow{b}$ is equivalent to a body force field in the general direction $\overrightarrow{\eta }$, and λ is a factor that scales the contribution of image-based forces that, according to the FE solution approach, will be balanced by stresses associated with material properties (e.g., stiffness or bulk moduli) by means of conservation of momentum as detailed in [35], [36], [43]. To capture large displacements, it is customary to apply blurring to the images in the early stages of registration, and gradually reduced thereafter, which ensures local displacements are not impacted.…”

“…In fact, the spatial gradient directs registration based on texture and edge information. Consequently, areas with no local intensity gradients do not contribute to registration forces [43]. …”

“…One notable example is Hyperelastic Warping [35], [36], which uses a solid mechanics formulation by means for finite element (FE) modeling. This technique has been shown effective experimentally for deformation characterization of ligaments, blood vessels, and the LV, with different imaging modalities [4], [42], [43]. However, though adding a continuum mechanics model to the registration process can greatly increase its effectiveness, it also introduces dependency on constitutive assumptions and geometric modeling, all of which influence the accuracy of the end result [43].…”