Image quality and metal artifact reduction in total hip arthroplasty CT: deep learning-based algorithm versus virtual monoenergetic imaging and orthopedic metal artifact reduction.

BACKGROUND: To compare image quality, metal artifacts, and diagnostic confidence of conventional computed tomography (CT) images of unilateral total hip arthroplasty patients (THA) with deep learning-based metal artifact reduction (DL-MAR) to conventional CT and 130-keV monoenergetic images with and without orthopedic metal artifact reduction (O-MAR). METHODS: Conventional CT and 130-keV monoenergetic images with and without O-MAR and DL-MAR images of 28 unilateral THA patients were reconstructed. Image quality, metal artifacts, and diagnostic confidence in bone, pelvic organs, and soft tissue adjacent to the prosthesis were jointly scored by two experienced musculoskeletal radiologists. Contrast-to-noise ratios (CNR) between bladder and fat and muscle and fat were measured. Wilcoxon signed-rank tests with Holm-Bonferroni correction were used. RESULTS: Significantly higher image quality, higher diagnostic confidence, and less severe metal artifacts were observed on DL-MAR and images with O-MAR compared to images without O-MAR (p < 0.001 for all comparisons). Higher image quality, higher diagnostic confidence for bone and soft tissue adjacent to the prosthesis, and less severe metal artifacts were observed on DL-MAR when compared to conventional images and 130-keV monoenergetic images with O-MAR (p ≤ 0.014). CNRs were higher for DL-MAR and images with O-MAR compared to images without O-MAR (p < 0.001). Higher CNRs were observed on DL-MAR images compared to conventional images and 130-keV monoenergetic images with O-MAR (p ≤ 0.010). CONCLUSIONS: DL-MAR showed higher image quality, diagnostic confidence, and superior metal artifact reduction compared to conventional CT images and 130-keV monoenergetic images with and without O-MAR in unilateral THA patients. RELEVANCE STATEMENT: DL-MAR resulted into improved image quality, stronger reduction of metal artifacts, and improved diagnostic confidence compared to conventional and virtual monoenergetic images with and without metal artifact reduction, bringing DL-based metal artifact reduction closer to clinical application. KEY POINTS: • Metal artifacts introduced by total hip arthroplasty hamper radiologic assessment on CT. • A deep-learning algorithm (DL-MAR) was compared to dual-layer CT images with O-MAR. • DL-MAR showed best image quality and diagnostic confidence. • Highest contrast-to-noise ratios were observed on the DL-MAR images.

Advances in metal artifact reduction in CT images: A review of traditional and novel metal artifact reduction techniques.

Metal artifacts degrade CT image quality, hampering clinical assessment. Numerous metal artifact reduction methods are available to improve the image quality of CT images with metal implants. In this review, an overview of traditional methods is provided including the modification of acquisition and reconstruction parameters, projection-based metal artifact reduction techniques (MAR), dual energy CT (DECT) and the combination of these techniques. Furthermore, the additional value and challenges of novel metal artifact reduction techniques that have been introduced over the past years are discussed such as photon counting CT (PCCT) and deep learning based metal artifact reduction techniques.

Is AI the way forward for reducing metal artifacts in CT? Development of a generic deep learning-based method and initial evaluation in patients with sacroiliac joint implants.

PURPOSE: To develop a deep learning-based metal artifact reduction technique (dl-MAR) and quantitatively compare metal artifacts on dl-MAR-corrected CT-images, orthopedic metal artifact reduction (O-MAR)-corrected CT-images and uncorrected CT-images after sacroiliac (SI) joint fusion. METHODS: dl-MAR was trained on CT-images with simulated metal artifacts. Pre-surgery CT-images and uncorrected, O-MAR-corrected and dl-MAR-corrected post-surgery CT-images of twenty-five patients undergoing SI joint fusion were retrospectively obtained. Image registration was applied to align pre-surgery with post-surgery CT-images within each patient, allowing placement of regions of interest (ROIs) on the same anatomical locations. Six ROIs were placed on the metal implant and the contralateral side in bone lateral of the SI joint, the gluteus medius muscle and the iliacus muscle. Metal artifacts were quantified as the difference in Hounsfield units (HU) between pre- and post-surgery CT-values within the ROIs on the uncorrected, O-MAR-corrected and dl-MAR-corrected images. Noise was quantified as standard deviation in HU within the ROIs. Metal artifacts and noise in the post-surgery CT-images were compared using linear multilevel regression models. RESULTS: Metal artifacts were significantly reduced by O-MAR and dl-MAR in bone (p < 0.001), contralateral bone (O-MAR: p = 0.009; dl-MAR: p < 0.001), gluteus medius (p < 0.001), contralateral gluteus medius (p < 0.001), iliacus (p < 0.001) and contralateral iliacus (O-MAR: p = 0.024; dl-MAR: p < 0.001) compared to uncorrected images. Images corrected with dl-MAR resulted in stronger artifact reduction than images corrected with O-MAR in contralateral bone (p < 0.001), gluteus medius (p = 0.006), contralateral gluteus medius (p < 0.001), iliacus (p = 0.017), and contralateral iliacus (p < 0.001). Noise was reduced by O-MAR in bone (p = 0.009) and gluteus medius (p < 0.001) while noise was reduced by dl-MAR in all ROIs (p < 0.001) in comparison to uncorrected images. CONCLUSION: dl-MAR showed superior metal artifact reduction compared to O-MAR in CT-images with SI joint fusion implants.

Quantitative analysis of metal artifact reduction in total hip arthroplasty using virtual monochromatic imaging and orthopedic metal artifact reduction, a phantom study.

OBJECTIVE: To quantify metal artifact reduction using 130 keV virtual monochromatic imaging (VMI) with and without orthopedic metal artifact reduction (O-MAR) in total hip arthroplasty. METHODS: Conventional polychromatic images and 130 keV VMI of a phantom with pellets representing bone with unilateral or bilateral prostheses were reconstructed with and without O-MAR on a dual-layer CT. Pellets were categorized as unaffected, mildly affected and severely affected. RESULTS: When 130 keV VMI with O-MAR was compared to conventional imaging with O-MAR, a relative metal artifact reduction in CT values, contrast-to-noise (CNR), signal-to-noise (SNR) and noise in mildly affected pellets (67%, 74%, 48%, 68%, respectively; p < 0.05) was observed but no significant relative metal artifact reduction in severely affected pellets. Comparison between 130 keV VMI without O-MAR and conventional imaging with O-MAR showed relative metal artifact reduction in CT values, CNR, SNR and noise in mildly affected pellets (92%, 72%, 38%, 51%, respectively; p < 0.05) but negative relative metal artifact reduction in CT values and noise in severely affected pellets (- 331% and -223%, respectively; p < 0.05), indicating aggravation of metal artifacts. CONCLUSION: Overall, VMI of 130 keV with O-MAR provided the strongest metal artifact reduction.

Semi-Automatic Tracking of Laser Speckle Contrast Images of Microcirculation in Diabetic Foot Ulcers.

Foot ulcers are a severe complication of diabetes mellitus. Assessment of the vascular status of diabetic foot ulcers with Laser Speckle Contrast Imaging (LSCI) is a promising approach for diagnosis and prognosis. However, manual assessment during analysis of LSCI limits clinical applicability. Our aim was to develop and validate a fast and robust tracking algorithm for semi-automatic analysis of LSCI data. The feet of 33 participants with diabetic foot ulcers were recorded with LSCI, including at baseline, during the Post-Occlusive Reactive Hyperemia (PORH) test, and during the Buerger's test. Different regions of interest (ROIs) were used to measure microcirculation in different areas of the foot. A tracking algorithm was developed in MATLAB to reposition the ROIs in the LSCI scans. Manual- and algorithm-tracking of all recordings were compared by calculating the Intraclass Correlation Coefficient (ICC). The algorithm was faster in comparison with the manual approach (90 s vs. 15 min). Agreement between manual- and algorithm-tracking was good to excellent during baseline (ICC = 0.896-0.984; p < 0.001), the PORH test (ICC = 0.790-0.960; p < 0.001), and the Buerger's test (ICC = 0.851-0.978; p < 0.001), resulting in a tracking algorithm that delivers assessment of LSCI in diabetic foot ulcers with results comparable to a labor-intensive manual approach, but with a 10-fold workload reduction.