Point-Spread-Function-Aware Slice-to-Volume Registration: Application to Upper Abdominal MRI Super-Resolution

Abstract

Abstract. MR image acquisition of moving organs remains challenging despite the advances in ultra-fast 2D MRI sequences. Post-acquisition techniques have been proposed to increase spatial resolution a posteriori by combining acquired orthogonal stacks into a single, high-resolution (HR) volume. Current super-resolution techniques classically rely on a two-step procedure. The volumetric reconstruction step leverages a physical slice acquisition model. However, the motion correction step typically neglects the point spread function (PSF) information. In this paper, we propose a PSF-aware slice-to-volume registration approach and, for the first time, demonstrate the potential benefit of Super-Resolution for upper abdominal imaging. Our novel reconstruction pipeline takes advantage of different MR acquisitions clinically used in routine MR cholangiopancreatography studies to guide the registration. On evaluation of clinically relevant image information, our approach outperforms state-of-theart reconstruction toolkits in terms of visual clarity and preservation of raw data information. Overall, we achieve promising results towards replacing currently required CT scans.

An Automated Localization, Segmentation and Reconstruction Framework for Fetal Brain MRI

“…Fig. 2 presents a visual comparison of three methods for fetal brain segmentation applied to Group B1 and Group B2 respectively: 1) Salehi et al 2 [10], applying the U-Net to the whole input image for segmentation without a localization stage, 2) P-Net (S) trained with the basic Dice loss function (at a single scale), and 3) P-Net (S) + ML where P-Net (S) was trained with our proposed multi-scale loss function. Both P-Net (S) and P-Net (S) + ML were applied to the output of P-Net (L).…”

“…≥ σ containing only slices in high agreement with their simulated counterparts projected from the previous HR iterate according to a similarity measure Sim and parameter σ > 0. α ≥ 0 denotes a regularization parameter and ∇ the differential operator. We thus have a convex SRR problem with complete outlier removal in a linear leastsquares formulation that is efficiently solvable using matrix-free operations [2].…”

Super‐resolution for upper abdominal MRI: Acquisition and post‐processing protocol optimization using brain MRI control data and expert reader validation

“…Our preliminary study demonstrated the feasibility of upper abdominal MRI SRRs generated from only two standard MRCP protocol axial and coronal SST2W series using HT2W volumes as a reference‐guide for in‐plane deformable slice‐to‐volume (S2V) registration/motion correction. but anatomical clarity was lacking and a more robust registration/motion correction was needed.…”

Joint Multimodal Segmentation of Clinical CT and MR from Hip Arthroplasty Patients

“…The axial and the coronal MR images are first corrected for bias field effects [17]. In order to compensate for the highly anisotropic resolution of clinical MR images (up to a factor of 10), we combine both MR acquisitions into a 1×1×1 mm 3 resolution image using the SRR algorithm presented in [18]. To ease the subsequent registration, the CT is also resampled to the same resolution using a cubic interpolation scheme.…”