Iterative and discrete reconstruction in the evaluation of the rabbit model of osteoarthritis

Micro-computed tomography (µCT) is a standard method for bone morphometric evaluation. However, the scan time can be long and the radiation dose during the scan may have adverse effects on test subjects, therefore both of them should be minimized. This could be achieved by applying iterative reconstruction (IR) on sparse projection data, as IR is capable of producing reconstructions of sufficient image quality with less projection data than the traditional algorithm requires. In this work, the performance of three IR algorithms was assessed for quantitative bone imaging from low-resolution data in the evaluation of the rabbit model of osteoarthritis. Subchondral bone images were reconstructed with a conjugate gradient least squares algorithm, a total variation regularization scheme, and a discrete algebraic reconstruction technique to obtain quantitative bone morphometry, and the results obtained in this manner were compared with those obtained from the reference reconstruction. Our approaches were sufficient to identify changes in bone structure in early osteoarthritis, and these changes were preserved even when minimal data were provided for the reconstruction. Thus, our results suggest that IR algorithms give reliable performance with sparse projection data, thereby recommending them for use in µCT studies where time and radiation exposure are preferably minimized.

[1]  D. James,et al.  Control of Adipocyte Differentiation in Different Fat Depots; Implications for Pathophysiology or Therapy , 2015, Front. Endocrinol..

[2]  J. Leipsic,et al.  State of the Art: Iterative CT Reconstruction Techniques. , 2015, Radiology.

[3]  C. Ohlsson,et al.  Structure Model Index Does Not Measure Rods and Plates in Trabecular Bone , 2015, Front. Endocrinol..

[4]  P. Rüegsegger,et al.  Morphometric analysis of human bone biopsies: a quantitative structural comparison of histological sections and micro-computed tomography. , 1998, Bone.

[5]  J. Skilling,et al.  Maximum entropy image reconstruction: general algorithm , 1984 .

[6]  Ralph Müller,et al.  Guidelines for assessment of bone microstructure in rodents using micro–computed tomography , 2010, Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research.

[7]  Jan Sijbers,et al.  The ASTRA Toolbox: A platform for advanced algorithm development in electron tomography. , 2015, Ultramicroscopy.

[8]  Arnold M. R. Schilham,et al.  Iterative reconstruction techniques for computed tomography part 2: initial results in dose reduction and image quality , 2013, European Radiology.

[9]  Jan Sijbers,et al.  The ASTRA Tomography Toolbox , 2013 .

[10]  H J Mankin,et al.  Articular cartilage: degeneration and osteoarthritis, repair, regeneration, and transplantation. , 1998, Instructional course lectures.

[11]  Simo Saarakkala,et al.  Association between subchondral bone structure and osteoarthritis histopathological grade , 2016, Journal of orthopaedic research : official publication of the Orthopaedic Research Society.

[12]  Å. Björck,et al.  Stability of Conjugate Gradient and Lanczos Methods for Linear Least Squares Problems , 1998, SIAM J. Matrix Anal. Appl..

[13]  Jan Sijbers,et al.  Discrete tomography in an in vivo small animal bone study , 2017, Journal of Bone and Mineral Metabolism.

[14]  N. Otsu A threshold selection method from gray level histograms , 1979 .

[15]  Arnold M. R. Schilham,et al.  Iterative reconstruction techniques for computed tomography Part 1: Technical principles , 2013, European Radiology.

[16]  Daniel Kolditz,et al.  Iterative reconstruction methods in X-ray CT. , 2012, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.

[17]  Harrie Weinans,et al.  A role for subchondral bone changes in the process of osteoarthritis; a micro-CT study of two canine models , 2008, BMC musculoskeletal disorders.

[18]  F. Boas,et al.  CT artifacts: Causes and reduction techniques , 2012 .

[19]  Prabhat Munshi,et al.  A ROBUST MART ALGORITHM FOR TOMOGRAPHIC APPLICATIONS , 1999 .

[20]  E Somersalo,et al.  Statistical inversion for medical x-ray tomography with few radiographs: I. General theory. , 2003, Physics in medicine and biology.

[21]  Sandra J Shefelbine,et al.  BoneJ: Free and extensible bone image analysis in ImageJ. , 2010, Bone.

[22]  Rudi Deklerck,et al.  Evaluation of the radiation dose in micro-CT with optimization of the scan protocol. , 2010, Contrast media & molecular imaging.

[23]  Per Christian Hansen,et al.  Analysis of Discrete Ill-Posed Problems by Means of the L-Curve , 1992, SIAM Rev..

[24]  M. Bouxsein,et al.  In vivo assessment of trabecular bone microarchitecture by high-resolution peripheral quantitative computed tomography. , 2005, The Journal of clinical endocrinology and metabolism.

[25]  Guangshu Hu,et al.  Micro-computed tomography for small animal imaging: Technological details , 2008 .

[26]  M. Doube The Ellipsoid Factor for Quantification of Rods, Plates, and Intermediate Forms in 3D Geometries , 2015, Front. Endocrinol..

[27]  Christoph Groden,et al.  Application of micro-CT in small animal imaging. , 2010, Methods.

[28]  H Weinans,et al.  Quantification of subchondral bone changes in a murine osteoarthritis model using micro-CT. , 2006, Biorheology.

[29]  Kees Joost Batenburg,et al.  DART: A Practical Reconstruction Algorithm for Discrete Tomography , 2011, IEEE Transactions on Image Processing.

[30]  R Schulze,et al.  Artefacts in CBCT: a review. , 2011, Dento maxillo facial radiology.

[31]  J. Jurvelin,et al.  Alterations in subchondral bone plate, trabecular bone and articular cartilage properties of rabbit femoral condyles at 4 weeks after anterior cruciate ligament transection. , 2015, Osteoarthritis and cartilage.

[32]  L. Feldkamp,et al.  Practical cone-beam algorithm , 1984 .

[33]  U. Wyss,et al.  Trabecular microstructure in the medial condyle of the proximal tibia of patients with knee osteoarthritis. , 1995, Bone.

[34]  Steve B. Jiang,et al.  Low-dose CT reconstruction via edge-preserving total variation regularization. , 2010, Physics in medicine and biology.

[35]  Guy Demoment,et al.  Image reconstruction and restoration: overview of common estimation structures and problems , 1989, IEEE Trans. Acoust. Speech Signal Process..

[36]  Kees Joost Batenburg,et al.  Easy implementation of advanced tomography algorithms using the ASTRA toolbox with Spot operators , 2016, Numerical Algorithms.

[37]  S. Siltanen,et al.  Total variation regularization for large-scale X-ray tomography , 2014 .

[38]  Robert C. Wolpert,et al.  A Review of the , 1985 .

[39]  D. Holdsworth,et al.  Micro-CT in small animal and specimen imaging , 2002 .

[40]  P. Rüegsegger,et al.  Morphometric analysis of noninvasively assessed bone biopsies: comparison of high-resolution computed tomography and histologic sections. , 1996, Bone.

[41]  M. Grynpas,et al.  The effects of glucosamine hydrochloride on subchondral bone changes in an animal model of osteoarthritis. , 2007, Arthritis and rheumatism.

[42]  J. Gati,et al.  High-resolution MRI and micro-CT in an ex vivo rabbit anterior cruciate ligament transection model of osteoarthritis. , 2004, Osteoarthritis and cartilage.

[43]  S. Jimenez,et al.  Osteoarthritis cartilage histopathology: grading and staging. , 2006, Osteoarthritis and cartilage.

[44]  D. R. Sumner,et al.  A decreased subchondral trabecular bone tissue elastic modulus is associated with pre‐arthritic cartilage damage , 2001, Journal of orthopaedic research : official publication of the Orthopaedic Research Society.

[45]  L. Cristofolini,et al.  Precision of Digital Volume Correlation Approaches for Strain Analysis in Bone Imaged with Micro-Computed Tomography at Different Dimensional Levels , 2017, Front. Mater..

[46]  A. Kak,et al.  Simultaneous Algebraic Reconstruction Technique (SART): A Superior Implementation of the Art Algorithm , 1984, Ultrasonic imaging.

[47]  Ge Wang,et al.  A Perspective on Deep Imaging , 2016, IEEE Access.

[48]  Michael D. Abràmoff,et al.  Image processing with ImageJ , 2004 .

[49]  M. Vannier,et al.  Why do commercial CT scanners still employ traditional, filtered back-projection for image reconstruction? , 2009, Inverse problems.

[50]  John B. Shoven,et al.  I , Edinburgh Medical and Surgical Journal.

[51]  J. Borwein,et al.  Two-Point Step Size Gradient Methods , 1988 .

[52]  M. Hestenes,et al.  Methods of conjugate gradients for solving linear systems , 1952 .

[53]  Jeannot Trampert,et al.  Simultaneous iterative reconstruction technique: Physical interpretation based on the generalized least squares solution , 1990 .

[54]  Steve B. Jiang,et al.  Low-dose CT reconstruction via edge-preserving total variation regularization , 2010, Physics in medicine and biology.

[55]  F Peyrin,et al.  Subchondral bone micro-architectural alterations in osteoarthritis: a synchrotron micro-computed tomography study. , 2006, Osteoarthritis and cartilage.

[56]  D. J. Kubinski,et al.  Examination of subchondral bone architecture in experimental osteoarthritis by microscopic computed axial tomography. , 1988, Arthritis and rheumatism.

[57]  Jan Sijbers,et al.  Fast and flexible X-ray tomography using the ASTRA toolbox. , 2016, Optics express.

[58]  Yong-Koo Park,et al.  Grading and Staging , 2020, Tumors and Tumor-Like Lesions of Bone.

[59]  Samuli Siltanen,et al.  Linear and Nonlinear Inverse Problems with Practical Applications , 2012, Computational science and engineering.

[60]  Kees Joost Batenburg,et al.  Automatic Parameter Estimation for the Discrete Algebraic Reconstruction Technique (DART) , 2012, IEEE Transactions on Image Processing.