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Optimal Real-Time QBI using Regularized Kalman Filtering with Incremental Orientation Sets

Rachid Deriche 1, * Jeff Calder 1 Maxime Descoteaux 2 
* Corresponding author
1 ODYSSEE - Computer and biological vision
DI-ENS - Département d'informatique - ENS Paris, CRISAM - Inria Sophia Antipolis - Méditerranée , ENS-PSL - École normale supérieure - Paris, Inria Paris-Rocquencourt, ENPC - École des Ponts ParisTech
Abstract : Diffusion MRI has become an established research tool for the investigation of tissue structure and orientation from which has stemmed a number of variations, such as Diffusion Tensor Imaging (DTI), Diffusion Spectrum Imaging (DSI) and Q-Ball Imaging (QBI). The acquisition and analysis of such data is very challenging due to its complexity. Recently, an exciting new Kalman filtering framework has been proposed for DTI and QBI reconstructions in real time during the repetition time (TR) of the acquisition sequence \cite{Miccai:2007,Med. Image Analysis -Vol 12, Issue 5, June 2008}. In this article, we first revisite and thoroughly analyze this approach and show it is actually sub-optimal and not recursively minimizing the intended criterion due to the Laplace-Beltrami regularization term. Then, we propose a new approach that implements the QBI reconstruction algorithm in real-time using a fast and robust Laplace-Beltrami regularization without sacrificing the optimality of the Kalman filter. We demonstrate that our method solves the correct minimization problem at each iteration and recursively provides the optimal QBI solution. We validate with real QBI data that our proposed real-time method is equivalent in terms of QBI estimation accuracy to the standard off-line processing techniques and outperforms the existing solution. Last, we propose a fast algorithm to recursively compute gradient orientation sets whose partial subsets are almost uniform and show that it can also be applied to the problem of efficiently ordering an existing point-set of any size. Our work allows to start an acquisition just with the minimum number of gradient directions and an initial estimate of the q-ball and then all the rest, including the next gradient directions and the q-ball estimates, are recursively and optimally determined, allowing the acquisition to be stopped as soon as desired or at any iteration with the optimal q-ball estimate. This opens new and interesting opportunities for real-time feedback for clinicians during an acquisition and also for researchers investigating into optimal diffusion orientation sets and, real-time fiber tracking and connectivity mapping.
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Submitted on : Friday, January 9, 2009 - 6:28:27 PM
Last modification on : Thursday, March 17, 2022 - 10:08:30 AM
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Rachid Deriche, Jeff Calder, Maxime Descoteaux. Optimal Real-Time QBI using Regularized Kalman Filtering with Incremental Orientation Sets. [Research Report] RR-6793 - Also appeared in Medical Image Analysis Volume 13, Issue 4, August 2009, Pages 564-579, INRIA. 2009, pp.44. ⟨inria-00351660⟩



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