SALTO : Early detection of Osteoarthritis (OA) with SPCCT

Osteoarthritis (OA) is a severe public health problem, which concerns a large percentage of patients and is estimated to increase on the next 10 years. It is associated with pains, problems in quality of life and disability. The relevance of diagnostic imaging for assessing the integrity of the joint is well recognized, especially for early detection of OA. Conventional radiography is still the standard imaging diagnostic tool, which is sensitive to late OA. However, there are not current methods for detecting early OA with sufficiently high resolution and image quality to visualize at the same time the internal structure of the bone, meniscus and details of cartilage. A new generation of X-Ray CT systems called Spectral CT (SP-CT) should provide energy-dependent information, which translates into material decomposition capabilities allowing a better quantification of the different constituents of tissue. However, such systems are currently only available at the stage of prototypes and must be thoroughly assessed and validated. 

The main objectives of the SALTO project was to investigate the ability of SP-CT based on photon counting detectors and associated to new data processing methods to characterize cartilage and meniscus integrity and to investigate possible biomarkers that could improve diagnosis of OA, especially devoted to early detection of OA. To achieve these goals the following sub-objectives will be addressed: 1) Verify the theoretical feasibility of SP-CT to image soft tissue of joints (cartilage, meniscus, and ligaments) and to establish the best requirements for contrast and signal-to-noise ratio (by using computer simulated studies). 2) Validate the energy resolution of SP-CT images by using energy-dependent gold-standard images from the synchrotron radiation. 3) Enhance image quality of SP-CT by testing state-of-the art reconstruction algorithms . 4) Test the feasibility of SP-CT for OA diagnosis in comparison with histopathology scorings of the meniscus and cartilage. 

This project combines experimentations on a unique SP-CT prototype and development of image processing methods for SP-CT reconstruction and analysis as well as specific expertise on OA imaging.

The strategy developed in this project has a high potential to contribute to the development of the new generations of SP-CT devices and their evaluation in the diagnosis of OA,

Publications in peer reviewed journals

ABASCAL JFPJ, DUCROS N, PRONINA VN RIT S, RODESCH PA, BROUSSAUD T, BUSSOD S, DOUEK P, HAUPTMANN A, ARRIDGE S, PEYRIN F, Material Decomposition in Spectral CT Using Deep Learning: A Sim2Real Transfer Approach, IEEE Access, vol. 9, pp. 25632-25647, 2021

International conference with reviewed proceedings

BUSSOD S, ABASCAL JFPJ, DUCROS N, OLIVIER C, SI-MOHAMED S, DOUEK P, CHAPPARD C, PEYRIN F, Realistic Knee Phantom To Validate Material Decomposition Algorithm Human Knee Phantom To Validate Material Decomposition Algorithms For Spectral CT, IEEE ISBI, Venise, Italie, April 2019, Proc 4p (reviewed proceedings)

ABASCAL JFPJ, DUCROS N, PRONINA V, BUSSOD S, ARRIDGE S, HAUPTMANN A, DOUEK P, PEYRIN F, Deep learning-based material decomposition for spectral CT, AIP Grenoble 2019

ABASCAL JFPJ, SI-MOHAMED S, DOUEK P, CHAPPARD C, PEYRIN F, A sparse and prior based method for 3D image denoising, EUSIPCO (Eur Signal Processing Conference), A Coruna, Sept 2019 (Proc 4p.)

ABASCAL JFPJ, DUCROS N, PRONINA V, RODESCH PA, RIT S, BUSSOD S, BROUSSAUD T, SI-MOHAMED S, ARRIDGE S, HAUPTMANN A, DOUEK P, PEYRIN F, Nonlinear material decomposition in spectral CT using a deep learning approach, Mini symposium on “Mathematical Challenges in Recent and Future Imaging", SIAM Conf on Imaging Science, Toronto, July 6-9, 2020 (invited)

ABASCAL JFPJ, BUSSOD S, DUCROS N, SI-MOHAMED S, DOUEK P, CHAPPARD C, PEYRIN F, A residual U-Net network with image prior for 3D image denoising EUSIPCO, Proc 4p., Amsterdam, 2020

BUSSOD S, ABASCAL JFPJ, ARRIDGE S, HAUPTMANN A CHAPPARD C, DUCROS N, PEYRIN F, Convolutional Neural Network for Material Decomposition in Spectral CT scans, EUSIPCO, Proc 4p., Amsterdam, 2020

Conferences

CHAPPARD C, ABASCAL JFPJ, BUSSOD S, UK S, SI-MOHAMED S, DOUEK P, PEYRIN F, Feasibility of photon counting spectral CT to assess knee cartilage, 22nd International Workshop on Quantitative Musculoskeletal Imaging (QMSKI), Lake Louise, Canada, Fev 2019 (abstract oral)

GARCELON C, ABASCAL J, OLIVIER C, SI-MOHAMED S, UK S, EA K, BOUSSEL L, DOUEK P, PEYRIN F, CHAPPARD C, Analyse quantitative morphologique du cartilage a partir du scanner spectral a comptage de photons, 33ème congrès Français de Rhumatologie, 2021 (abstract)

ABASCAL J, OLIVIER C, SI-MOHAMED S, UK S, EA K, BOUSSEL L, DOUEK P, PEYRIN F, CHAPPARD C, Analyse quantitative morphologique des geodes sous chondrales a partir du scanner spectral a comptage de photons, SF Rheumatology, 2021 (abstract)

Figure 1 : Experimental comparison of a monoE SP-CT slice and a monochromatic synchrotron CT slice on a human knee specimen : right Synchrotron CT, left : SP-CT 70 keV monoE (from [Bussod et al., IEEE ISBI 2019, Proceedings])

Figure 2 : Zoom in the cartilage area, left : monochromatic synchrotron CT, right : SP-CT 70 keV monoE

(from [Bussod et al., IEEE ISBI 2019, Proceedings])

Figure 3 : 3D display of an entire knee from an experimental SP-CT monoE image at 70 keV (unpublished)