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

References

• Aizenberg E, Roex EAH, Nieuwenhuis WP, Mangnus L, van der Helm-van Mil AHM, Reijnierse M, Bloem JL, Lelieveldt BPF, Stoel BC. 2018. Automatic quantification of bone marrow edema on MRI of the wrist in patients with early arthritis: A feasibility study. Magn Reson Med. 79(2):1127–1134. doi:10.1002/mrm.26712.
   Google Scholar
• Blizzard DJ, Haims AH, Lischuk AW, Arunakul R, Hustedt JW, Grauer JN. 2015. 3D-FSE isotropic MRI of the lumbar spine: novel application of an existing technology. Clin Spine Surg. 28(4):152–157.
   Google Scholar
• Chaudhari AS, Fang Z, Kogan F, Wood J, Stevens KJ, Gibbons EK, Lee JH, Gold GE, Hargreaves BA. 2018. Super-resolution musculoskeletal MRI using deep learning. Magn Reson Med. 80(5):2139–2154. doi:10.1002/mrm.27178.
   Google Scholar
• Chow LS, Rajagopal H. 2017. Modified-BRISQUE as no reference image quality assessment for structural MR images. Magn Reson Imaging. 43:74–87. doi:10.1016/j.mri.2017.07.016.
   Google Scholar
• Ebner M, Wang G, Li W, Aertsen M, Patel PA, Aughwane R, Melbourne A, Doel T, David AL, Deprest J, Ourselin S, Veracuteren T. 2018. An automated localization, segmentation and reconstruction framework for fetal brain mri. in Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. 2018. Cham: Springer International Publishing.
   Google Scholar
• Ebner M, Wang G, Li W, Aertsen M, Patel PA, Aughwane R, Melbourne A, Doel T, Dymarkowski S, De Coppi P, David AL, Deprest J, Ourselin S, Veracuteren T. 2019. An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI. NeuroImage:116324.
   Google Scholar
• Ebner M, Chung KK, Prados F, Cardoso MJ, Chard DT, Vercauteren T, Ourselin S. 2018. Volumetric reconstruction from printed films: enabling 30 year longitudinal analysis in MR neuroimaging. NeuroImage. 165(p):238–250. doi:10.1016/j.neuroimage.2017.09.056.
   Google Scholar
• Gholipour A, Afacan O, Aganj I, Scherrer B, Prabhu SP, Sahin M, Warfield SK. 2015. Super-resolution reconstruction in frequency, image, and wavelet domains to reduce through-plane partial voluming in MRI. Med Phys. 42(12):6919–6932. doi:10.1118/1.4935149.
   Google Scholar
• Havsteen I, Ohlhues A, Madsen KH, Nybing JD, Christensen H, Christensen A. 2017. Are movement artifacts in magnetic resonance imaging a real problem?A narrative review. Front Neurol. 8:232. doi:10.3389/fneur.2017.00232.
   Google Scholar
• Helgason B, Gilchrist S, Ariza O, Vogt P, Enns-Bray W, Widmer RP, Fitze T, Palsson H, Pauchard Y, Guy P, Ferguson SJ, Cripton PA. 2016. 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. Med Eng Phys. 38(7):679–689. doi:10.1016/j.medengphy.2016.03.006.
   Google Scholar
• Kroker A, Besler BA, Bhatla JL, Shtil M, Salat P, Mohtadi N, Walker RE, Manske SL, Boyd SK. 2019. Longitudinal effects of acute anterior cruciate ligament tears on peri-articular bone in human knees within the first year of injury. J Orthop Res. 37(11):2325–2336. doi:10.1002/jor.24410.
   Google Scholar
• Lecouvet FE. 2016. Whole-body MR imaging: musculoskeletal applications. Radiology. 279(2):p. 345–365. doi:10.1148/radiol.2016142084.
   Google Scholar
• Mittal A, Moorthy AK, Bovik AC. 2012. No-reference image quality assessment in the spatial domain. IEEE Trans Image Process. 21(12):4695–4708. doi:10.1109/TIP.2012.2214050.
   Google Scholar
• Ostrowska M, Maslinski W, Prochorec-Sobieszek M, Nieciecki M, Sudol-Szopinska I. 2018. Cartilage and bone damage in rheumatoid arthritis. Reumatologia. 56(2):111–120. doi:10.5114/reum.2018.75523.
   Google Scholar
• Pauchard Y, Fitze T, Browarnik D, Eskandari A, Pauchard I, Enns-Bray W, Pálsson H, Sigurdsson S, Ferguson SJ, Harris TB. 2016. Interactive graph-cut segmentation for fast creation of finite element models from clinical ct data for hip fracture prediction. Comput Methods Biomech Biomed Engin. 19(16):1693–1703. doi:10.1080/10255842.2016.1181173.
   Google Scholar
• Poot DHJ, Van Meir V, Sijbers J. 2010. general and efficient super-resolution method for multi-slice MRI. in medical image computing and computer-assisted intervention – MICCAI 2010. Berlin (Heidelberg): Springer Berlin Heidelberg.
   Google Scholar
• Rajapakse CS, Farid AR, Kargilis DC, Jones BC, Lee JS, Johncola AJ, Batzdorf AS, Shetye SS, Hast MW, Chang G. 2020. MRI-based assessment of proximal femur strength compared to mechanical testing. Bone. 133:115227. doi:10.1016/j.bone.2020.115227.
   Google Scholar
• Rose S, Waters EA, Haney CR, Meade CTJ, Perlman H. 2013. High-resolution magnetic resonance imaging of ankle joints in murine arthritis discriminates inflammation and bone destruction in a quantifiable manner. Arthritis Rheum. 65(9):2279–2289. doi:10.1002/art.38030.
   Google Scholar
• Swärd P, Struglics A, Englund M, Roos HP, Frobell RB. 2014. Soft tissue knee injury with concomitant osteochondral fracture is associated with higher degree of acute joint inflammation. Am J Sports Med. 42(5):1096–1102. doi:10.1177/0363546514524924.
   Google Scholar
• Vyas S, Bhalla AS, Ranjan P, Kumar S, Kumar U, Gupta AK. 2016. Rheumatoid arthritis revisited – advanced imaging review. Pol J Radiol. 81:629–635. doi:10.12659/PJR.899317.
   Google Scholar
• Wadhwa V, Malhotra V, Xi Y, Nordeck S, Coyner K, Chhabra A. 2016. Bone and joint modeling from 3D knee MRI: feasibility and comparison with radiographs and 2D MRI. Clin Imaging. 40(4):765–768. doi:10.1016/j.clinimag.2016.02.017.
   Google Scholar
• Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC, Gerig G. 2006. User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. NeuroImage. 31(3):p. p. 1116–1128. doi:10.1016/j.neuroimage.2006.01.015.
   Google Scholar
• Zaitsev M, Maclaren J, Herbst M. 2015. Motion artifacts in MRI: A complex problem with many partial solutions. J Magn Reson Imaging. 42(4):887–901. doi:10.1002/jmri.24850.
   Google Scholar
• Zhang L, Zhang L, Mou X, Zhang D. 2011. FSIM: a feature similarity index for image quality assessment. IEEE Trans Image Process. 20(8):2378–2386. doi:10.1109/TIP.2011.2109730.
   Google Scholar
• Zhou W, Bovik AC, Sheikh HR, Simoncelli EP. 2004. Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process. 13(4):600–612. doi:10.1109/TIP.2003.819861.
   Google Scholar