Current ultrasound methods for measuring myocardial strain are often limited to measurements in one or two dimensions. Cardiac motion and deformation however are truly 3-D. With the introduction of matrix transducer technology, 3-D ultrasound imaging of the heart has become feasible but suffers from low temporal and spatial resolution, making 3-D strain estimation challenging. In this paper, it is shown that automatic intensity-based spatio-temporal elastic registration of currently available 3-D volumetric ultrasound data sets can be used to measure the full 3-D strain tensor. The method was validated using simulated 3-D ultrasound data sets of the left ventricle (LV). Three types of data sets were simulated: a normal and symmetric LV with different heart rates, a more realistic asymmetric normal LV and an infarcted LV. The absolute error in the estimated displacement was between 0.47 +/-0.23 and 1.00 +/-0.59 mm, depending on heart rate and amount of background noise. The absolute error on the estimated strain was 9%-21% for the radial strain and 1%-4% for the longitudinal and circumferential strains. No large differences were found between the different types of data sets. The shape of the strain curves was estimated properly and the position of the infarcts could be identified correctly. Preliminary results on clinical data taken in vivo from three healthy volunteers and one patient with an apical aneurism confirmed these findings in a qualitative manner as the strain curves obtained with the proposed method have an amplitude and shape similar to what could be expected.

We present a novel framework for efficient retrospective respiratory motion correction of 3-D abdominal MRI using manifold regression. K-space data are continuously acquired under free breathing using the stack-of-stars radial golden-angle trajectory. The stack-of-profiles (SoP) from all temporal positions are embedded into a common manifold, in which SoPs that were acquired at similar respiratory states are close together. Next, the SoPs in the manifold are clustered into groups using the k-means algorithm. One 3-D volume is reconstructed at the central SoP position of each cluster (a.k.a. key-volumes). Motion fields are estimated using deformable image registration between each of these key-volumes and a reference end-exhale volume. Subsequently, the motion field at any other SoP position in the manifold is derived using manifold regression. The regressed motion fields for each of the SoPs are used to determine a final motion-corrected MRI volume. The method was evaluated on realistic synthetic datasets which were generated from real MRI data and also tested on an in vivo dataset. The framework enables more accurate motion correction compared to the conventional binning-based approach, with high computational efficiency.

Automatic quantification of regional left ventricular deformation in volumetric ultrasound data remains challenging. Many methods have been proposed to extract myocardial motion, including techniques using block matching, phase-based correlation, differential optical flow methods, and image registration. Our lab previously presented an approach based on elastic registration of subsequent volumes using a B-spline representation of the underlying transformation field. Encouraging results were obtained for the assessment of global left ventricular function, but a thorough validation on a regional level was still lacking. For this purpose, univentricular thick-walled cardiac phantoms were deformed in an experimental setup to locally assess strain accuracy against sonomicrometry as a reference method and to assess whether regions containing stiff inclusions could be detected. Our method showed good correlations against sonomicrometry: r(2) was 0.96, 0.92, and 0.84 for the radial (ε(RR)), longitudinal (ε(LL)), and circumferential (ε(CC)) strain, respectively. Absolute strain errors and strain drift were low for ε(LL) (absolute mean error: 2.42%, drift: -1.05%) and ε(CC) (error: 1.79%, drift: -1.33%) and slightly higher for ε(RR) (error: 3.37%, drift: 3.05%). The discriminative power of our methodology was adequate to resolve full transmural inclusions down to 17 mm in diameter, although the inclusion-to-surrounding tissue stiffness ratio was required to be at least 5:2 (absolute difference of 39.42 kPa). When the inclusion-to-surrounding tissue stiffness ratio was lowered to approximately 2:1 (absolute difference of 22.63 kPa), only larger inclusions down to 27 mm in diameter could still be identified. Radial strain was found not to be reliable in identifying dysfunctional regions.

Fusion of in-vivo magnetic resonance imaging (MRI) with whole mount histology of the prostate is facilitated by the use of a patient-specific mold, that is designed from in-vivo MRI. The mold defines specific sectioning planes with the same orientation and position relative to the prostate as the stack of MRI slices, reducing the registration problem from a 3D to a 2D problem. We present an innovative mold design that specifies the in-and outflow of the urethra as additional landmarks for correct positioning of the prostate in the mold, that allows for the fresh prostate tissue to be fixated inside the mold such that its in-vivo shape is maintained, and that allows for ex-vivo MRI of the prostate in the mold in alignment with in-vivo MRI using the mold as reference frame. Using high-resolution 3D ex-vivo MRI aligned with in-vivo MRI, we demonstrate that our improved mold design results in a more accurate positioning of the prostate inside the mold, significantly reducing out-of-plane rotational offsets. Initial results show that the proposed workflow has the potential to provide detailed histopathological ground truth for the quantitative interpretation of in-vivo and ex-vivo MRI in prostate cancer.

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