Quantitative spectral micro-CT of a CA4+ loaded osteochondral sample with a tabletop system

Abstract Micro-computed tomography ( $$\upmu$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>μ</mml:mi> </mml:math> CT) is the gold standard for nondestructive 3D imaging of biomedical samples in the centimeter scale, but it has limited effectiveness in revealing intricate soft tissue details due to the limited attenuation contrast. Radiopaque contrast agents that accumulate in the structures of interest are employed to enhance their visibility. However, the increased attenuation provided by the contrast agents does not guarantee discrimination among tissues. This issue can be solved by spectral $$\upmu$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>μ</mml:mi> </mml:math> CT (S $$\upmu$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>μ</mml:mi> </mml:math> CT) systems employing small-pixel chromatic photon-counting detectors. These detectors, combined with material decomposition algorithms, allow the generation of high-resolution material-specific 3D maps. This work aims to demonstrate the potential of photon-counting X-ray S $$\upmu$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>μ</mml:mi> </mml:math> CT on osteochondral samples loaded with a cationic iodinated contrast agent (CA4+) at a spatial resolution below 50 $$\upmu$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>μ</mml:mi> </mml:math> m, and to compare the results against a conventional $$\upmu$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>μ</mml:mi> </mml:math> CT system. An osteochondral sample extracted from a bovine stifle joint was loaded with CA4+ and imaged with a novel multimodal X-ray imaging system, featuring a 62 $$\upmu$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>μ</mml:mi> </mml:math> m pixel CdTe spectral detector (Pixirad1-PixieIII). After material decomposition, quantitative 3D density maps of iodine and hydroxyapatite were reconstructed. The same sample was also scanned with a commercial $$\upmu$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>μ</mml:mi> </mml:math> CT scanner with matched spectrum and exposure time. SCT $$\upmu$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>μ</mml:mi> </mml:math> images at a (measured) spatial resolution comparable with the commercial scanner ( $$\sim$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>∼</mml:mo> </mml:math> 45 $$\upmu$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>μ</mml:mi> </mml:math> m) were obtained. Spectral images allowed for a fully automatic segmentation of cartilage and subchondral bone. The unambiguous discrimination between iodine and hydroxyapatite revealed a more realistic representation of proteoglycan distribution compared to conventional imaging.