Impact of computed tomography metal artifact reduction protocol on periprosthetic tissue characterization after total hip arthroplasty: A cadaveric study

Abstract

Metal artifact reduction (MAR) has improved computed tomography (CT) imaging of total hip arthroplasty (THA) but the assessment of osteolysis and implant to bone contact relies on the accurate depiction of bone defects, cancellous bone, and cement. This study evaluates the impact of available single and dual-energy protocols on periprosthetic tissue characterization in a cadaveric phantom. Bilateral THA was performed on a fresh frozen cadaveric pelvis with simulated osteolytic cavities. CT acquisitions with projection-based MAR and noise equivalence were performed using single energy 140 kVp, single energy 150 kVp with 0.6 mm tin filtration, and dual-energy at 100/150 kVp with 0.6 mm tin filtration, from which simulated energies were extracted. Image subtraction, segmentation, region of interest histograms, and line profiles were used to characterize tissue density and separation. Tissue densities were heavily dependent on the energy profile of the protocol. Cancellous bone ranged from 182 to 45 HU and cement from 1012 to 131 HU using 140 kVp compared to dual-energy with weighted high energy tube, respectively. Spectral separation between cancellous bone, osteolytic defect, and cement was reduced for all protocols compared with 140 kVp. Spectral overlap was most severe using dual-energy with heavily weighted high-energy tubes. Dualenergy algorithms reduced trabecular contrast within the cancellous bone and cortical edge response. Although the dual-energy acquisition has been proposed as an additive to projection-based MAR techniques in THA, reduced density and contrast in clinically relevant periprosthetic tissue compared to 140 kVp single energy may limit its use in characterizing periprosthetic tissues.

Development and validation of a reliable method for automated measurements of psoas muscle volume in CT scans using deep learning-based segmentation: a cross-sectional study

“…Despite numerous studies in the past decades, the issue of metal artefacts in CT scans remains a significant problem. [39][40][41] Nevertheless, to achieve clinical sarcopenia diagnosis using CT scans, it is necessary to establish psoas muscle volume data for the general population and compare them with existing sarcopenia measurement methods. The AI software described in this study is expected to make a substantial contribution towards achieving these objectives.…”