Groupwise image registration based on a total correlation dissimilarity measure for quantitative MRI and dynamic imaging data

Abstract

The most widespread technique used to register sets of medical images consists of selecting one image as fixed reference, to which all remaining images are successively registered. This pairwise scheme requires one optimization procedure per pair of images to register. Pairwise mutual information is a common dissimilarity measure applied to a large variety of datasets. Alternative methods, called groupwise registrations, have been presented to register two or more images in a single optimization procedure, without the need of a reference image. Given the success of mutual information in pairwise registration, we adapt one of its multivariate versions, called total correlation, in a groupwise context. We justify the choice of total correlation among other multivariate versions of mutual information, and provide full implementation details. The resulting total correlation measure is remarkably close to measures previously proposed by Huizinga et al. based on principal component analysis. Our experiments, performed on five quantitative imaging datasets and on a dynamic CT imaging dataset, show that total correlation yields registration results that are comparable to Huizinga’s methods. Total correlation has the advantage of being theoretically justified, while the measures of Huizinga et al. were designed empirically. Additionally, total correlation offers an alternative to pairwise mutual information on quantitative imaging datasets.

Groupwise Image Alignment via Self Quotient Images

“…where the parameter's set P N (k − 1) is known and we would like to minimize with respect to the k-th approximation of the "mean" image i(k). It is clear that the new cost function is strongly related to both above defined cost functions C 0 (P N (k − 1); i(k)) and C 1 (∆P N (k), i(k)) in (6) and (15) respectively. Minimization of the cost function C 2 (i(k); P N (k − 1)) with respect to the "mean" image i(k), results in the following optimal solution:…”

“…In addition, in [12] and [13] extended entropy-based congealing for the usage on the real-world complex images is proposed while in [2] a variational Bayesian approach for ensemble registration is presented. Recently, in [14,15] groupwise registration techniques tailored for the registration of quantitative MRI datasets were presented.…”

Groupwise Non-Rigid Registration with Deep Learning: An Affordable Solution Applied to 2D Cardiac Cine MRI Reconstruction

“…The field of registration has a long record [ 8 ]; recently the DL paradigm has entered the field with solutions that provide fast registrations once networks have been trained. On the one hand, supervised solutions such as [ 9 , 10 ] rely on segmentations or landmarks to estimate the displacements.…”

Combination of Imaging Infrared Spectroscopy and X-ray Computed Microtomography for the Investigation of Bio- and Physicochemical Processes in Structured Soils

“…As 2D imVNIR and 3D X-ray µCT give different but corroborating information on the same material, the combination of the two techniques can therefore be used to uncover and quantify biogeochemical processes in intact soil samples. The combination of different biogeochemical imaging methods, designated as correlative microscopy or correlative imaging, is increasingly used in life sciences (Caplan et al, 2011;Handschuh et al, 2013;Guyader et al, 2018). A few recent applications have demonstrated that there is a great potential for the application of correlative imaging in soil sciences (Hapca et al, 2011;Juyal et al, 2019;Kravchenko et al, 2019;Schlüter et al, 2019).…”