Quantitative imaging of excised osteoarthritic cartilage using spectral CT

Rajendran, Kishore; Löbker, Caroline; Schon, Benjamin S.; Bateman, Christopher; Raja, Aamir; Ruiter, Niels J A de; Chernoglazov, Alex; Ramyar, Mohsen; Hooper, Gary J.; Butler, Anthony; Woodfield, Tim B. F.; Anderson, Nigel G.

doi:10.1007/s00330-016-4374-7

Cited by 46 publications

(24 citation statements)

References 30 publications

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“…This is due to greater misidentification of gadolinium as water with protocol-2 (refer to Figures 11 and 12a). For more insight about the biological sample, the reader is referred to [9].…”

Section: Resultsmentioning

confidence: 99%

“…Two cylindrical cartilage-bone plugs harvested from the bovine stifle joint, each with a diameter of 8 mm and length of 7 mm, were used to quantify gadolinium uptake in articular cartilage. The sample preparation setup was similar to that used in a previous study using iodinated contrast [9]. Samples were incubated for 24 h in Gd contrast agent solution (100% MultiHance) at 37 • C and then rinsed in PBS for 30 s. A non-Gd incubated sample was used as a control.…”

Section: Imaging Samplesmentioning

confidence: 99%

“…However, the specificity of target material to water cannot be determined by Equation (8) as the water basis is used as a reference material. Therefore, percentage of voxels in the composite materials which are only classified as water are calculated as (9) Finally, the relationship between the known and measured concentrations of gadolinium and hydroxyapatite was analyzed in the material domain images by manually selecting ROIs comprising ~1409 voxels (N) over each solution insert. The percentage of successful voxels which are correctly identified as not the target material in the control ROI provides the true negative voxels (TN) such that…”

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confidence: 99%

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Measuring Identification and Quantification Errors in Spectral CT Material Decomposition

et al. 2018

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Material decomposition methods are used to identify and quantify multiple tissue components in spectral CT but there is no published method to quantify the misidentification of materials. This paper describes a new method for assessing misidentification and mis-quantification in spectral CT. We scanned a phantom containing gadolinium (1, 2, 4, 8 mg/mL), hydroxyapatite (54.3, 211.7, 808.5 mg/mL), water and vegetable oil using a MARS spectral scanner equipped with a poly-energetic X-ray source operated at 118 kVp and a CdTe Medipix3RX camera. Two imaging protocols were used; both with and without 0.375 mm external brass filter. A proprietary material decomposition method identified voxels as gadolinium, hydroxyapatite, lipid or water. Sensitivity and specificity information was used to evaluate material misidentification. Biological samples were also scanned. There were marked differences in identification and quantification between the two protocols even though spectral and linear correlation of gadolinium and hydroxyapatite in the reconstructed images was high and no qualitative segmentation differences in the material decomposed images were observed. At 8 mg/mL, gadolinium was correctly identified for both protocols, but concentration was underestimated by over half for the unfiltered protocol. At 1 mg/mL, gadolinium was misidentified in 38% of voxels for the filtered protocol and 58% of voxels for the unfiltered protocol. Hydroxyapatite was correctly identified at the two higher concentrations for both protocols, but mis-quantified for the unfiltered protocol. Gadolinium concentration as measured in the biological specimen showed a two-fold difference between protocols. In future, this methodology could be used to compare and optimize scanning protocols, image reconstruction methods, and methods for material differentiation in spectral CT.

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“…This is due to greater misidentification of gadolinium as water with protocol-2 (refer to Figures 11 and 12a). For more insight about the biological sample, the reader is referred to [9].…”

Section: Resultsmentioning

confidence: 99%

Section: Imaging Samplesmentioning

confidence: 99%

mentioning

confidence: 99%

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Measuring Identification and Quantification Errors in Spectral CT Material Decomposition

et al. 2018

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“…Other adjunctive methods have been explored to overcome the insensitivity of soft tissue evaluation with CT: contrast-enhanced CT (CECT), positron emission tomography, spectral CT, phase contrast x-ray computed tomography and dual energy CT [49][50][51][52][53][54][55][56]. These methods improve upon and expand the physiological information usually limited with standard CT.…”

Section: Computed Tomographymentioning

confidence: 99%

Recent advances in articular cartilage evaluation using computed tomography and magnetic resonance imaging

Nelson

Kawcak

Barrett

et al. 2018

Equine Veterinary Journal

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Articular cartilage is a critical joint tissue and its evaluation remains a diagnostic challenge in horses. Coupled with a poor capacity for healing, early degenerative changes in articular cartilage are difficult to characterise using routine diagnostic imaging evaluations. Both computed tomography (CT) and magnetic resonance imaging (MRI) provide volumetric joint assessment and highlight morphological and quantitative properties of articular cartilage, improving assessment of this essential tissue. While the use of CT and MRI for joint evaluation is not new, there still remains a shortage of literature and scientific studies on the ability of these methods to evaluate articular cartilage in the horse. This review article summarises current CT and MRI techniques capable of characterising equine articular cartilage, highlights recent advances in these techniques and discusses the numerous methods studied in human subjects that have been minimally investigated in horses. Imaging techniques are presented in terms of their capabilities of offering morphological and quantitative evaluation along with a discussion of their benefits and limitations. Finally, it summarises the current state-of-the-art approaches and identifies unmet clinical imaging needs to propel the advancement of articular cartilage and joint imaging in the horse.

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“…The spectral imaging allows the extraction of functional and anatomical features of the tissues via tracing biomarkers and pharmaceuticals in a low dose and noninvasive way . MARS imaging has been used in various preclinical applications such as characterizing the composition of excised vulnerable atherosclerosis plaques in arteries, functional imaging of arthritic cartilage, and targeting cancerous cells using nanoparticles…”

Section: Introductionmentioning

confidence: 99%

Beam profile assessment in spectral CT scanners

Anjomrouz

Shamshad

Panta

et al. 2018

J Applied Clin Med Phys

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In this paper, we present a method that uses a combination of experimental and modeled data to assess properties of x‐ray beam measured using a small‐animal spectral scanner. The spatial properties of the beam profile are characterized by beam profile shape, the angular offset along the rotational axis, and the photon count difference between experimental and modeled data at the central beam axis. Temporal stability of the beam profile is assessed by measuring intra‐ and interscan count variations. The beam profile assessment method was evaluated on several spectral CT scanners equipped with Medipix3RX‐based detectors. On a well‐calibrated spectral CT scanner, we measured an integral count error of 0.5%, intrascan count variation of 0.1%, and an interscan count variation of less than 1%. The angular offset of the beam center ranged from 0.8° to 1.6° for the studied spectral CT scanners. We also demonstrate the capability of this method to identify poor performance of the system through analyzing the deviation of the experimental beam profile from the model. This technique can, therefore, aid in monitoring the system performance to obtain a robust spectral CT; providing the reliable quantitative images. Furthermore, the accurate offset parameters of a spectral scanner provided by this method allow us to incorporate a more realistic form of the photon distribution in the polychromatic‐based image reconstruction models. Both improvements of the reliability of the system and accuracy of the volume reconstruction result in a better discrimination and quantification of the imaged materials.

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Quantitative imaging of excised osteoarthritic cartilage using spectral CT

Abstract: • Contrast-enhanced articular cartilage and subchondral bone can be distinguished using multi-energy CT. • Iodine as a marker of glycosaminoglycan content is quantifiable with multi-energy CT. • Multi-energy CT could track alterations in GAG content occurring in osteoarthritis.

Cited by 46 publications

References 30 publications

Measuring Identification and Quantification Errors in Spectral CT Material Decomposition

Measuring Identification and Quantification Errors in Spectral CT Material Decomposition

Recent advances in articular cartilage evaluation using computed tomography and magnetic resonance imaging

Beam profile assessment in spectral CT scanners

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