The application and optimization of super-resolution reconstruction for isotropic out-of-plane MRI to study the musculoskeletal system

ABSTRACT In this paper we optimised and adapted a post-processing super-resolution reconstruction (SRR) algorithm for its use in the semi-automated isotropic reconstruction of non-isotropically acquired musculoskeletal magnetic resonance image (MRI) data. The ability to produce isotropic MRI data facilitated the (1) enhanced out-of-plane image visualisation; (2) semi-automated image registration with CT data of the same anatomical site; and (3) improved image segmentation. The effectiveness of the SRR algorithm was demonstrated on several musculoskeletal scans including ex vivo tibial plateaus, in vivo knees and hands with varying levels of structural complexity and potential for motion artefact.

[1]  J. Grauer,et al.  3D-FSE Isotropic MRI of the Lumbar Spine: Novel Application of an Existing Technology , 2015, Journal of spinal disorders & techniques.

[2]  J. Bloem,et al.  Automatic quantification of bone marrow edema on MRI of the wrist in patients with early arthritis: A feasibility study , 2017, Magnetic resonance in medicine.

[3]  F. Lecouvet Whole-Body MR Imaging: Musculoskeletal Applications. , 2016, Radiology.

[4]  Jan Sijbers,et al.  General and Efficient Super-Resolution Method for Multi-slice MRI , 2010, MICCAI.

[5]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[6]  Alan C. Bovik,et al.  No-Reference Image Quality Assessment in the Spatial Domain , 2012, IEEE Transactions on Image Processing.

[7]  N. Mohtadi,et al.  Longitudinal Effects of Acute Anterior Cruciate Ligament Tears on Peri‐Articular Bone in Human Knees Within the First Year of Injury , 2019, Journal of orthopaedic research : official publication of the Orthopaedic Research Society.

[8]  A. Bhalla,et al.  Rheumatoid Arthritis Revisited – Advanced Imaging Review , 2016, Polish journal of radiology.

[9]  Sébastien Ourselin,et al.  Volumetric reconstruction from printed films: Enabling 30 year longitudinal analysis in MR neuroimaging , 2018, NeuroImage.

[10]  Jeff Wood,et al.  Super‐resolution musculoskeletal MRI using deep learning , 2018, Magnetic resonance in medicine.

[11]  G. Chang,et al.  MRI-based assessment of proximal femur strength compared to mechanical testing. , 2020, Bone.

[12]  Kristoffer Hougaard Madsen,et al.  Are Movement Artifacts in Magnetic Resonance Imaging a Real Problem?—A Narrative Review , 2017, Front. Neurol..

[13]  I. Sudoł-Szopińska,et al.  Cartilage and bone damage in rheumatoid arthritis , 2018, Reumatologia.

[14]  M. Englund,et al.  Soft Tissue Knee Injury With Concomitant Osteochondral Fracture Is Associated With Higher Degree of Acute Joint Inflammation , 2014, The American journal of sports medicine.

[15]  Onur Afacan,et al.  Super-resolution reconstruction in frequency, image, and wavelet domains to reduce through-plane partial voluming in MRI. , 2015, Medical physics.

[16]  M. Zaitsev,et al.  Motion artifacts in MRI: A complex problem with many partial solutions , 2015, Journal of magnetic resonance imaging : JMRI.

[17]  E. A. Waters,et al.  High-resolution magnetic resonance imaging of ankle joints in murine arthritis discriminates inflammation and bone destruction in a quantifiable manner. , 2013, Arthritis and rheumatism.

[18]  B Helgason,et al.  The influence of the modulus-density relationship and the material mapping method on the simulated mechanical response of the proximal femur in side-ways fall loading configuration. , 2016, Medical engineering & physics.

[19]  Guido Gerig,et al.  User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability , 2006, NeuroImage.

[20]  V. Gudnason,et al.  Interactive graph-cut segmentation for fast creation of finite element models from clinical ct data for hip fracture prediction , 2016, Computer methods in biomechanics and biomedical engineering.

[21]  A. Chhabra,et al.  Bone and joint modeling from 3D knee MRI: feasibility and comparison with radiographs and 2D MRI. , 2016, Clinical imaging.

[22]  Sébastien Ourselin,et al.  An Automated Localization, Segmentation and Reconstruction Framework for Fetal Brain MRI , 2018, MICCAI.

[23]  Sébastien Ourselin,et al.  An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI , 2019, NeuroImage.

[24]  David Zhang,et al.  FSIM: A Feature Similarity Index for Image Quality Assessment , 2011, IEEE Transactions on Image Processing.

[25]  Heshalini Rajagopal,et al.  Modified-BRISQUE as no reference image quality assessment for structural MR images. , 2017, Magnetic resonance imaging.