Metal artifact reduction techniques in musculoskeletal CT-imaging

Evaluation of a prototype metal artifact reduction algorithm for cone beam CT in patients undergoing radioembolization

Metal artifacts notoriously pose significant challenge in computed tomography (CT), leading to inaccuracies in image formation and interpretation. Artifact reduction tools have been designed to improve cone beam computed tomography (CBCT) image quality by reducing artifacts caused by certain high-density materials. Metal artifact reduction (MAR) tools are specific algorithms that are applied during image reconstruction to minimize or eliminate artifacts degrading CBCT images. The purpose of the study is to evaluate the effect of a MAR algorithm on image quality in CBCT performed for evaluating patients before transarterial radioembolization (TARE). We retrospectively included 40 consecutive patients (aged 65 ± 13 years; 23 males) who underwent 45 CBCT examinations (Allura FD 20, XperCT Roll protocol, Philips Healthcare, Best, The Netherlands) in the setting of evaluation for TARE between January 2017 and December 2018. Artifacts caused by coils, catheters, and surgical clips were scored subjectively by four readers on a 5-point scale (1 = artifacts affecting diagnostic information to 5 = no artifacts) using a side-by-side display of uncorrected and MAR-corrected images. In addition, readers scored tumor visibility and vessel discrimination. MAR-corrected images were assigned higher scores, indicating better image quality. The differences between the measurements with and without MAR were most impressive for coils with a mean improvement of 1.6 points (95%CI [1.5 1.8]) on the 5-point likert scale, followed by catheters 1.4 points (95%CI [1.3 1.5]) and clips 0.7 points (95%CI [0.3 1.1]). Improvements for other artifact sources were consistent but relatively small (below 0.25 points on average). Interrater agreement was good to perfect (Kendall's W coefficient = 0.68-0.95) and was higher for MAR-corrected images, indicating that MAR improves diagnostic accuracy. A metal artifact reduction algorithm can improve diagnostic and interventional accuracy of cone beam CT in patients undergoing radioembolization by reducing artifacts caused by diagnostic catheters and coils, lowering interference of metal artifacts with adjacent major structures, and improving tumor visibility.

Automated design of bone-preserving, insertable, and shape-matching patient-specific acetabular components

Effective treatment of large acetabular defects remains among the most challenging aspects of revision total hip arthroplasty (THA), due to the deficiency of healthy bone stock and degradation of the support columns. Generic uncemented components, which are favored in primary THA, are often unsuitable in revision cases, where the bone-implant contact may be insufficient for fixation, without significant reaming of the limited residual bone. This study presents a computational design strategy for automatically generating patient-specific implants that simultaneously maximize the bone-implant contact area, and minimize bone reaming while ensuring insertability. These components can be manufactured using the same additive manufacturing methods as porous components and may reduce cost and operating-time, compared to existing patient-specific systems. This study compares the performance of implants generated via the proposed method to optimally fitted hemispherical implants, in terms of the achievable bone-implant contact surface, and the volume of reamed bone. Computer-simulated results based on the reconstruction of a set of 15 severe pelvic defects (Paprosky 2A-3B) suggest that the patient-specific components increase bone-implant contact by 63% (median: 63%; SD: 44%; 95% CI: 52.3%-74.0%; RMSD: 42%), and reduce the volume of reamed bone stock by 97% (median: 98%; SD: 4%; 95% CI: 95.9%-97.4%; RMSD: 3.7%).

Dual-energy CT in musculoskeletal imaging: technical considerations and clinical applications

Dual-energy CT stands out as a robust and innovative imaging modality, which has shown impressive advancements and increasing applications in musculoskeletal imaging. It allows to obtain detailed images with novel insights that were once the exclusive prerogative of magnetic resonance imaging. Attenuation data obtained by using different energy spectra enable to provide unique information about tissue characterization in addition to the well-established strengths of CT in the evaluation of bony structures. To understand clearly the potential of this imaging modality, radiologists must be aware of the technical complexity of this imaging tool, the different ways to acquire images and the several algorithms that can be applied in daily clinical practice and for research. Concerning musculoskeletal imaging, dual-energy CT has gained more and more space for evaluating crystal arthropathy, bone marrow edema, and soft tissue structures, including tendons and ligaments. This article aims to analyze and discuss the role of dual-energy CT in musculoskeletal imaging, exploring technical aspects, applications and clinical implications and possible perspectives of this technique.

Uncertainty-aware multiple-instance learning for reliable classification: Application to optical coherence tomography

Deep learning classification models for medical image analysis often perform well on data from scanners that were used to acquire the training data. However, when these models are applied to data from different vendors, their performance tends to drop substantially. Artifacts that only occur within scans from specific scanners are major causes of this poor generalizability. We aimed to enhance the reliability of deep learning classification models using a novel method called Uncertainty-Based Instance eXclusion (UBIX). UBIX is an inference-time module that can be employed in multiple-instance learning (MIL) settings. MIL is a paradigm in which instances (generally crops or slices) of a bag (generally an image) contribute towards a bag-level output. Instead of assuming equal contribution of all instances to the bag-level output, UBIX detects instances corrupted due to local artifacts on-the-fly using uncertainty estimation, reducing or fully ignoring their contributions before MIL pooling. In our experiments, instances are 2D slices and bags are volumetric images, but alternative definitions are also possible. Although UBIX is generally applicable to diverse classification tasks, we focused on the staging of age-related macular degeneration in optical coherence tomography. Our models were trained on data from a single scanner and tested on external datasets from different vendors, which included vendor-specific artifacts. UBIX showed reliable behavior, with a slight decrease in performance (a decrease of the quadratic weighted kappa (κw) from 0.861 to 0.708), when applied to images from different vendors containing artifacts; while a state-of-the-art 3D neural network without UBIX suffered from a significant detriment of performance (κw from 0.852 to 0.084) on the same test set. We showed that instances with unseen artifacts can be identified with OOD detection. UBIX can reduce their contribution to the bag-level predictions, improving reliability without retraining on new data. This potentially increases the applicability of artificial intelligence models to data from other scanners than the ones for which they were developed. The source code for UBIX, including trained model weights, is publicly available through https://github.com/qurAI-amsterdam/ubix-for-reliable-classification.

Impact of different metal artifact reduction techniques in photon-counting computed tomography head and neck scans in patients with dental hardware

OBJECTIVES

Metal artifacts remain a challenge in computed tomography. We investigated the potential of photon-counting computed tomography (PCD-CT) for metal artifact reduction using an iterative metal artifact reduction (iMAR) algorithm alone and in combination with high keV monoenergetic images (140 keV) in patients with dental hardware.

MATERIAL AND METHODS

Consecutive patients with dental implants were prospectively included in this study and received PCD-CT imaging of the craniofacial area. Four series were reconstructed (standard [PCD-CTstd], monoenergetic at 140 keV [PCD-CT140keV], iMAR corrected [PCD-CTiMAR], combination of iMAR and 140 keV monoenergetic [PCD-CTiMAR+140keV]). All reconstructions were assessed qualitatively by four radiologists (independent and blinded reading on a 5-point Likert scale [5 = excellent; no artifact]) regarding overall image quality, artifact severity, and delineation of adjacent and distant anatomy. To assess signal homogeneity and evaluate the magnitude of artifact reduction, we performed quantitative measures of coefficient of variation (CV) and a region of interest (ROI)-based relative change in artifact reduction [PCD-CT/PCD-CTstd].

RESULTS

We enrolled 48 patients (mean age 66.5 ± 11.2 years, 50% (n = 24) males; mean BMI 25.2 ± 4.7 kg/m2; mean CTDIvol 6.2 ± 6 mGy). We found improved overall image quality, reduced artifacts and superior delineation of both adjacent and distant anatomy for the iMAR vs. non-iMAR reconstructions (all p < 0.001). No significant effect of the different artifact reduction approaches on CV was observed (p = 0.42). The ROI-based analysis indicated the most effective artifact reduction for the iMAR reconstructions, which was significantly higher compared to PCD-CT140keV (p < 0.001).

CONCLUSION

PCD-CT offers highly effective approaches for metal artifact reduction with the potential to overcome current diagnostic challenges in patients with dental implants.

CLINICAL RELEVANCE STATEMENT

Metallic artifacts pose a significant challenge in CT imaging, potentially leading to missed findings. Our study shows that PCD-CT with iMAR post-processing reduces artifacts, improves image quality, and can possibly reveal pathologies previously obscured by artifacts, without additional dose application.

KEY POINTS

• Photon-counting detector CT (PCD-CT) offers highly effective approaches for metal artifact reduction in patients with dental fillings/implants. • Iterative metal artifact reduction (iMAR) is superior to high keV monoenergetic reconstructions at 140 keV for artifact reduction and provides higher image quality. • Signal homogeneity of the reconstructed images is not affected by the different artifact reduction techniques.

Needle artifact reduction during interventional CT procedures using a silver filter

BACKGROUND

MAR algorithms have not been productized in interventional imaging because they are too time-consuming. Application of a beam hardening filter can mitigate metal artifacts and doesn't increase computational burden. We evaluate the ability to reduce metal artifacts of a 0.5 mm silver (Ag) additional filter in a Multidetector Computed Tomography (MDCT) scanner during CT-guided biopsy procedures.

METHODS

A biopsy needle was positioned inside the lung field of an anthropomorphic phantom (Lungman, Kyoto Kagaku, Kyoto, Japan). CT acquisitions were performed with beam energies of 100 kV, 120 kV, 135 kV, and 120 kV with the Ag filter and reconstructed using a filtered back projection algorithm. For each measurement, the CTDIvol was kept constant at 1 mGy. Quantitative profiles placed in three regions of the artifact (needle, needle tip, and trajectory artifacts) were used to obtain metrics (FWHM, FWTM, width at - 100 HU, and absolute error in HU) to evaluate the blooming artifact, artifact width, change in CT number, and artifact range. An image quality analysis was carried out through image noise measurement. A one-way analysis of variance (ANOVA) test was used to find significant differences between the conventional CT beam energies and the Ag filtered 120 kV beam.

RESULTS

The 120 kV-Ag is shown to have the shortest range of artifacts compared to the other beam energies. For needle tip and trajectory artifacts, a significant reduction of - 53.6% (p < 0.001) and - 48.7% (p < 0.001) in the drop of the CT number was found, respectively, in comparison with the reference beam of 120 kV as well as a significant decrease of up to - 34.7% in the artifact width (width at - 100 HU, p < 0.001). Also, a significant reduction in the blooming artifact of - 14.2% (FWHM, p < 0.001) and - 53.3% (FWTM, p < 0.001) was found in the needle artifact. No significant changes (p > 0.05) in image noise between the conventional energies and the 120 kV-Ag were found.

CONCLUSIONS

A 0.5 mm Ag additional MDCT filter demonstrated consistent metal artifact reduction generated by the biopsy needle. This reduction may lead to a better depiction of the target and surrounding structures while maintaining image quality.

Optimization of the "Perth CT" Protocol for Preoperative Planning and Postoperative Evaluation in Total Knee Arthroplasty

Background and Objectives: Total knee arthroplasty (TKA) has become the treatment of choice for advanced osteoarthritis. The aim of this paper was to show the possibilities of optimizing the Perth CT protocol, which is highly effective for preoperative planning and postoperative assessment of alignment. Materials and Methods: The cross-sectional study comprised 16 patients for preoperative planning or postoperative evaluation of TKA. All patients were examined with the standard and optimized Perth CT protocol using advance techniques, including automatic exposure control (AEC), iterative image reconstruction (IR), as well as a single-energy projection-based metal artifact reduction algorithm for eliminating prosthesis artifacts. The effective radiation dose (E) was determined based on the dose report. Imaging quality is determined according to subjective and objective (values of signal to noise ratio (SdNR) and figure of merit (FOM)) criteria. Results: The effective radiation dose with the optimized protocol was significantly lower compared to the standard protocol (p < 0.001), while in patients with the knee prosthesis, E increased significantly less with the optimized protocol compared to the standard protocol. No significant difference was observed in the subjective evaluation of image quality between protocols (p > 0.05). Analyzing the objective criteria for image quality optimized protocols resulted in lower SdNR values and higher FOM values. No significant difference of image quality was determined using the SdNR and FOM as per the specified protocols and parts of extremities, and for the presence of prothesis. Conclusions: Retrospecting the ALARA ('As Low As Reasonably Achievable') principles, it is possible to optimize the Perth CT protocol by reducing the kV and mAs values and by changing the collimation and increasing the pitch factor. Advanced IR techniques were used in both protocols, and AEC was used in the optimized protocol. The effective dose of radiation can be reduced five times, and the image quality will be satisfactory.

Ultra-high resolution photon-counting CT with tin prefiltration for bone-metal interface visualization

PURPOSE

To investigate the metal artifact suppression potential of combining tin prefiltration and virtual monoenergetic imaging (VMI) for osseous microarchitecture depiction in ultra-high-resolution (UHR) photon-counting CT (PCCT) of the lower extremity.

METHOD

Derived from tin-filtered UHR scans at 140 kVp, polychromatic datasets (T3D) and VMI reconstructions at 70, 110, 150, and 190 keV were compared in 117 patients with lower extremity metal implants (53 female; 62.1 ± 18.0 years). Three implant groups were investigated (total arthroplasty [n = 48], osteosynthetic material [n = 43], and external fixation [n = 26]). Image quality was assessed with regions of interest placed in the most pronounced artifacts and adjacent soft tissue, measuring the respective attenuation. Additionally, artifact extent, bone-metal interface interpretability and overall image quality were independently evaluated by three radiologists.

RESULTS

Artifact reduction was superior with increasing keV level of VMI. While T3D was superior to VMI70keV (p ≥ 0.117), artifacts were more severe in T3D than in VMI ≥ 110 keV (all p ≤ 0.036). Image noise was highest for VMI70keV (all p < 0.001) and lowest for VMI110keV with comparable results for VMI110keV - VMI190keV. Subjective image quality regarding artifacts was superior for VMI ≥ 110 keV (all p ≤ 0.042) and comparable for VMI110keV - VMI190keV. Bone-metal interface interpretability was superior for VMI110keV (all p ≤ 0.001), while T3D, VMI150keV and VMI190keV were comparable. Overall image quality was deemed best for VMI110keV and VMI150keV. Interreader reliability was good in all cases (ICC ≥ 0.833).

CONCLUSIONS

Tin-filtered UHR-PCCT scans of the lower extremity combined with VMI reconstructions allow for efficient artifact reduction in the vicinity of bone-metal interfaces.

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.

Imaging approach to prosthetic joint infection

The diagnosis of prosthetic joint infection (PJI) remains challenging, despite multiple available laboratory tests for both serum and synovial fluid analysis. The clinical symptoms of PJI are not always characteristic, particularly in the chronic phase, and there is often significant overlap in symptoms with non-infectious forms of arthroplasty failure. Further exacerbating this challenge is lack of a universally accepted definition for PJI, with publications from multiple professional societies citing different diagnostic criteria. While not included in many of the major societies' guidelines for diagnosis of PJI, diagnostic imaging can play an important role in the workup of suspected PJI. In this article, we will review an approach to diagnostic imaging modalities (radiography, ultrasound, CT, MRI) in the workup of suspected PJI, with special attention to the limitations and benefits of each modality. We will also discuss the role that image-guided interventions play in the workup of these patients, through ultrasound and fluoroscopically guided joint aspirations. While there is no standard imaging algorithm that can universally applied to all patients with suspected PJI, we will discuss a general approach to diagnostic imaging and image-guided intervention in this clinical scenario.

3D Printing Applications for Craniomaxillofacial Reconstruction: A Sweeping Review

The field of craniomaxillofacial (CMF) surgery is rich in pathological diversity and broad in the ages that it treats. Moreover, the CMF skeleton is a complex confluence of sensory organs and hard and soft tissue with load-bearing demands that can change within millimeters. Computer-aided design (CAD) and additive manufacturing (AM) create extraordinary opportunities to repair the infinite array of craniomaxillofacial defects that exist because of the aforementioned circumstances. 3D printed scaffolds have the potential to serve as a comparable if not superior alternative to the "gold standard" autologous graft. In vitro and in vivo studies continue to investigate the optimal 3D printed scaffold design and composition to foster bone regeneration that is suited to the unique biological and mechanical environment of each CMF defect. Furthermore, 3D printed fixation devices serve as a patient-specific alternative to those that are available off-the-shelf with an opportunity to reduce operative time and optimize fit. Similar benefits have been found to apply to 3D printed anatomical models and surgical guides for preoperative or intraoperative use. Creation and implementation of these devices requires extensive preclinical and clinical research, novel manufacturing capabilities, and strict regulatory oversight. Researchers, manufacturers, CMF surgeons, and the United States Food and Drug Administration (FDA) are working in tandem to further the development of such technology within their respective domains, all with a mutual goal to deliver safe, effective, cost-efficient, and patient-specific CMF care. This manuscript reviews FDA regulatory status, 3D printing techniques, biomaterials, and sterilization procedures suitable for 3D printed devices of the craniomaxillofacial skeleton. It also seeks to discuss recent clinical applications, economic feasibility, and future directions of this novel technology. By reviewing the current state of 3D printing in CMF surgery, we hope to gain a better understanding of its impact and in turn identify opportunities to further the development of patient-specific surgical care.

Deep learning-based ultrasound transducer induced CT metal artifact reduction using generative adversarial networks for ultrasound-guided cardiac radioablation

In US-guided cardiac radioablation, a possible workflow includes simultaneous US and planning CT acquisitions, which can result in US transducer-induced metal artifacts on the planning CT scans. To reduce the impact of these artifacts, a metal artifact reduction (MAR) algorithm has been developed based on a deep learning Generative Adversarial Network called Cycle-MAR, and compared with iMAR (Siemens), O-MAR (Philips) and MDT (ReVision Radiology), and CCS-MAR (Combined Clustered Scan-based MAR). Cycle-MAR was trained with a supervised learning scheme using sets of paired clinical CT scans with and without simulated artifacts. It was then evaluated on CT scans with real artifacts of an anthropomorphic phantom, and on sets of clinical CT scans with simulated artifacts which were not used for Cycle-MAR training. Image quality metrics and HU value-based analysis were used to evaluate the performance of Cycle-MAR compared to the other algorithms. The proposed Cycle-MAR network effectively reduces the negative impact of the metal artifacts. For example, the calculated HU value improvement percentage for the cardiac structures in the clinical CT scans was 59.58%, 62.22%, and 72.84% after MDT, CCS-MAR, and Cycle-MAR application, respectively. The application of MAR algorithms reduces the impact of US transducer-induced metal artifacts on CT scans. In comparison to iMAR, O-MAR, MDT, and CCS-MAR, the application of developed Cycle-MAR network on CT scans performs better in reducing these metal artifacts.

Synthetic Megavoltage Cone Beam Computed Tomography Image Generation for Improved Contouring Accuracy of Cardiac Pacemakers

In this study, we aimed to enhance the contouring accuracy of cardiac pacemakers by improving their visualization using deep learning models to predict MV CBCT images based on kV CT or CBCT images. Ten pacemakers and four thorax phantoms were included, creating a total of 35 combinations. Each combination was imaged on a Varian Halcyon (kV/MV CBCT images) and Siemens SOMATOM CT scanner (kV CT images). Two generative adversarial network (GAN)-based models, cycleGAN and conditional GAN (cGAN), were trained to generate synthetic MV (sMV) CBCT images from kV CT/CBCT images using twenty-eight datasets (80%). The pacemakers in the sMV CBCT images and original MV CBCT images were manually delineated and reviewed by three users. The Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and mean surface distance (MSD) were used to compare contour accuracy. Visual inspection showed the improved visualization of pacemakers on sMV CBCT images compared to original kV CT/CBCT images. Moreover, cGAN demonstrated superior performance in enhancing pacemaker visualization compared to cycleGAN. The mean DSC, HD95, and MSD for contours on sMV CBCT images generated from kV CT/CBCT images were 0.91 ± 0.02/0.92 ± 0.01, 1.38 ± 0.31 mm/1.18 ± 0.20 mm, and 0.42 ± 0.07 mm/0.36 ± 0.06 mm using the cGAN model. Deep learning-based methods, specifically cycleGAN and cGAN, can effectively enhance the visualization of pacemakers in thorax kV CT/CBCT images, therefore improving the contouring precision of these devices.

Combining virtual monoenergetic imaging and iterative metal artifact reduction in first-generation photon-counting computed tomography of patients with dental implants

OBJECTIVES

While established for energy-integrating detector computed tomography (CT), the effect of virtual monoenergetic imaging (VMI) and iterative metal artifact reduction (iMAR) in photon-counting detector (PCD) CT lacks thorough investigation. This study evaluates VMI, iMAR, and combinations thereof in PCD-CT of patients with dental implants.

MATERIAL AND METHODS

In 50 patients (25 women; mean age 62.0 ± 9.9 years), polychromatic 120 kVp imaging (T3D), VMI, T3DiMAR, and VMIiMAR were compared. VMIs were reconstructed at 40, 70, 110, 150, and 190 keV. Artifact reduction was assessed by attenuation and noise measurements in the most hyper- and hypodense artifacts, as well as in artifact-impaired soft tissue of the mouth floor. Three readers subjectively evaluated artifact extent and soft tissue interpretability. Furthermore, new artifacts through overcorrection were assessed.

RESULTS

iMAR reduced hyper-/hypodense artifacts (T3D 1305.0/-1418.4 versus T3DiMAR 103.2/-46.9 HU), soft tissue impairment (106.7 versus 39.7 HU), and image noise (16.9 versus 5.2 HU) compared to non-iMAR datasets (p ≤ 0.001). VMIiMAR ≥ 110 keV subjectively enhanced artifact reduction over T3DiMAR (p ≤ 0.023). Without iMAR, VMI displayed no measurable artifact reduction (p ≥ 0.186) and facilitated no significant denoising over T3D (p ≥ 0.366). However, VMI ≥ 110 keV reduced soft tissue impairment (p ≤ 0.009). VMIiMAR ≥ 110 keV resulted in less overcorrection than T3DiMAR (p ≤ 0.001). Inter-reader reliability was moderate/good for hyperdense (0.707), hypodense (0.802), and soft tissue artifacts (0.804).

CONCLUSION

While VMI alone holds minimal metal artifact reduction potential, iMAR post-processing enabled substantial reduction of hyperdense and hypodense artifacts. The combination of VMI ≥ 110 keV and iMAR resulted in the least extensive metal artifacts.

CLINICAL RELEVANCE

Combining iMAR with VMI represents a potent tool for maxillofacial PCD-CT with dental implants achieving substantial artifact reduction and high image quality.

KEY POINTS

• Post-processing of photon-counting CT scans with an iterative metal artifact reduction algorithm substantially reduces hyperdense and hypodense artifacts arising from dental implants. • Virtual monoenergetic images presented only minimal metal artifact reduction potential. • The combination of both provided a considerable benefit in subjective analysis compared to iterative metal artifact reduction alone.

Dual-Energy Computed Tomography and Beyond: Musculoskeletal System

Traditional monoenergetic computed tomography (CT) scans in musculoskeletal imaging provide excellent detail of bones but are limited in the evaluation of soft tissues. Dual-energy CT (DECT) overcomes many of the traditional limitations of CT and offers anatomical details previously seen only on MR imaging. In addition, DECT has benefits in the evaluation and characterization of arthropathies, bone marrow edema, and collagen applications in the evaluation of tendons, ligaments, and vertebral discs. There is current ongoing research in the application of DECT in arthrography and bone mineral density calculation.

Spectral photon-counting CT: Image quality evaluation using a metal-containing bovine bone specimen

PURPOSE

To find the optimal imaging parameters for a photon-counting detector CT (PCD-CT) and to compare it to an energy-integrating detector CT (EID-CT) in terms of image quality and metal artefact severity using a metal-containing bovine knee specimen.

METHODS

A bovine knee with a stainless-steel plate and screws was imaged in a whole-body research PCD-CT at 120 kV and 140 kV and in an EID dual-source CT (DSCT) at Sn150 kV and 80/Sn150 kV. PCD-CT virtual monoenergetic 72 and 150 keV images and EID-CT images processed with and without metal artefact reduction algorithms (iMAR) were compared. Four radiologists rated the visualisation of bony structures and metal artefact severity. The Friedman test and Wilcoxon signed-rank test with Bonferroni's correction were used. P-values of ≤ 0.0001 were considered statistically significant. Distributions of HU values of regions of interest (ROIs) in artefact-affected areas were analysed.

RESULTS

PCD-CT 140 kV 150 keV images received the highest scores and were significantly better than EID-CT Sn150 kV images. PCD-CT 72 keV images were rated significantly lower than all the others. HU-value variation was larger in the 120 kV and the 72 keV images. The ROI analysis revealed no large difference between scanners regarding artefact severity.

CONCLUSION

PCD-CT 140 kV 150 keV images of a metal-containing bovine knee specimen provided the best image quality. They were superior to, or as good as, the best EID-CT images; even without the presumed advantage of tin filter and metal artefact reduction algorithms. PCD-CT is a promising method for reducing metal artefacts.

Two-stage generative adversarial networks for metal artifact reduction and visualization in ablation therapy of liver tumors

PURPOSE

The strong metal artifacts produced by the electrode needle cause poor image quality, thus preventing physicians from observing the surgical situation during the puncture process. To address this issue, we propose a metal artifact reduction and visualization framework for CT-guided ablation therapy of liver tumors.

METHODS

Our framework contains a metal artifact reduction model and an ablation therapy visualization model. A two-stage generative adversarial network is proposed to reduce the metal artifacts of intraoperative CT images and avoid image blurring. To visualize the puncture process, the axis and tip of the needle are localized, and then the needle is rebuilt in 3D space intraoperatively.

RESULTS

Experiments show that our proposed metal artifact reduction method achieves higher SSIM (0.891) and PSNR (26.920) values than the state-of-the-art methods. The accuracy of ablation needle reconstruction is 2.76 mm average in needle tip localization and 1.64° average in needle axis localization.

CONCLUSION

We propose a novel metal artifact reduction and an ablation therapy visualization framework for CT-guided ablation therapy of liver cancer. The experiment results indicate that our approach can reduce metal artifacts and improve image quality. Furthermore, our proposed method demonstrates the potential for displaying the relative position of the tumor and the needle intraoperatively.

Optimal combination periprosthetic vasculature visualization and metal artifact reduction by spectral computed tomography using virtual monoenergetic images in total hip arthroplasty

OBJECTIVES

To investigate the optimal parameters of spectral CT for preferably visualizing the periprosthetic vasculature and metal artifact reduction (MAR) in total hip arthroplasty (THA).

METHODS

A total of 34 THA of 30 patients were retrospectively included. Image reconstructions included conventional image (CI), CI combined with MAR (CIMAR), and virtual monoenergetic images (VMI) combined with MAR (VMIMAR) at 50-120 keV. The attenuation and standard deviation of the vessel and artifact, and the width of artifact were measured. Qualitative scoring was evaluated including the vascular contour, the extent of artifact, and overall diagnostic evaluation.

RESULTS

The attenuation, noise of the vessel and artifact, and the width of artifact decreased as the energy level increased (p < 0.001). The downtrend was relatively flat at 80-120 keV, and the vascular attenuation dropped to 200 HU at 90 keV. The qualitative rating of vascular contour was significantly higher at CIMAR (3.47) and VMIMAR 60-80 keV (2.82-3.65) compared with CI (2.03) (p ≤ 0.029), and the highest score occurred at 70 and 80 keV (3.65 and 3.56). The score of the extent of artifact was higher at VMIMAR 80 keV than CIMAR (3.53 VS 3.12, p = 0.003). The score of the overall diagnostic evaluation was higher at VMIMAR 70 and 80 keV (3.32 and 3.53, respectively) than CIMAR (3.12) (p ≤ 0.035).

CONCLUSION

Eighty kiloelectron volts on VMIMAR, providing satisfactorily reduced metal artifacts and improved vascular visualization, can be an optimal recommended parameter of spectrum CT for the assessment of periprosthetic vasculature in THA patients.

CRITICAL RELEVANCE STATEMENT

The metal artifact is gradually reducing with increasing energy level; however, the vascular visualization is worsening. The vascular visualization is terrible above 100 keV, while the vessel is disturbed by artifacts below 70 keV. The best performance is found at 80 keV.

KEY POINTS

• VMIMAR can provide both reduced metal artifacts and improved vascular visualization. • Eighty kiloelectron volts on VMIMAR performs best in vascular visualization of total hip arthroplasty patients. • Energy spectrum CT is recommended for routine use in patients with total hip arthroplasty.

Rotational alignment of the lower extremity in the presence of total knee endoprosthesis: Reproducibility of torsion analyses using ultra-low-dose photon-counting CT

PURPOSE

Leg torsion analysis can provide valuable information in symptomatic patients after total knee arthroplasty. However, extensive beam-hardening and photon-starvation artifacts limit diagnostic assessability and dose reduction potential. For this study, we investigated the reproducibility of rotational measurements in ultra-low-dose photon-counting CT with spectral shaping via tin prefiltration.

MATERIAL AND METHODS

Employing a first-generation photon-counting CT, eight cadaveric specimens were examined with an established three-level scan protocol (hip: Sn 140, knee: Sn 100, ankle: Sn 100 kVp). In three body donors with unilateral knee endoprostheses, additional modified settings were applied (Sn 140 kVp at knee level). Protocols were executed with three dose levels (hip-knee-ankle, high-quality: 5.0-3.0-2.0 mGy, low-dose: 0.80-0.30-0.26 mGy, ultra-low-dose: 0.25-0.06-0.06 mGy). Six radiologists performed torsion analyses, additionally reporting their diagnostic confidence. Intraclass correlation coefficients (ICC) were calculated to assess interrater reliability.

RESULTS

No significant differences were ascertained for femoral (p = 0.330), tibial (p = 0.177), and overall leg rotation measurements (p = 0.358) among high-quality, low-dose, and ultra-low-dose protocols. Interrater reliability was excellent for torsion of the femur (ICC 0.915, 95% confidence interval 0.871-0.947), tibia (0.960, 0.938-0.976), and overall leg (0.967, 0.945-0.981). In specimens with total knee endoprostheses, absolute rotational measurements were unaffected by dose level and tube voltage despite superior diagnostic confidence on the ipsilateral and contralateral sides with modified settings (p < 0.001).

CONCLUSIONS

Combining the advantages of photon-counting CT and spectral shaping, reliable leg torsion analyses are feasible with ultra-low radiation exposure even in the presence of total knee endoprostheses.

Spectral metal artifact reduction after posterior spinal fixation in photon-counting detector CT datasets

PURPOSE

To investigate the usefulness of virtual monoenergetic image (VMI) reconstructions derived from scans on a novel photon-counting detector CT (PCD-CT) for artifact reduction in patients after posterior spinal fixation.

METHODS

This retrospective cohort study included 23 patients status post posterior spinal fixation. Subjects were scanned on a novel PCD-CT (NAEOTOM Alpha, Siemens Healthineers, Erlangen, Germany) as part of routine clinical care. 14 sets of VMI reconstructions were derived in 10 keV increments for the interval 60-190 keV. The mean and the standard deviation (SD) of CT-values in 12 defined locations around a pair of pedicle screws on one vertebral level and the SD of homogenous fat were measured and used to calculate an artifact index (AIx).

RESULTS

Averaged over all regions, the lowest AIx was observed at VMI levels of 110 keV (32.5 (27.8-37.9)) which was significantly different from those of VMIs ≤ 90 keV (p < 0.001) or ≥160 keV (p < 0.015), respectively. Overall AIx values increased in both lower- and higher-keV levels. Regarding individual locations, either a monotonous AIx-decrease for increasing keV values or an AIx-minimum in intermediate-keV levels (100-140 keV) was found. In locations adjacent to larger metal parts, the increase of AIx values at the high-end of the keV spectrum was mainly explained by a reappearance of streak artifacts.

CONCLUSION

Our findings suggest that 110 keV is the optimal VMI setting for overall artifact suppression. In specific anatomical regions, however, slight adjustments towards higher-keV levels may provide better results.

Photon-Counting Computed Tomography (PC-CT) of the spine: impact on diagnostic confidence and radiation dose

OBJECTIVES

Computed tomography (CT) is employed to evaluate surgical outcome after spinal interventions. Here, we investigate the potential of multispectral photon-counting computed tomography (PC-CT) on image quality, diagnostic confidence, and radiation dose compared to an energy-integrating CT (EID-CT).

METHODS

In this prospective study, 32 patients underwent PC-CT of the spine. Data was reconstructed in two ways: (1) standard bone kernel with 65-keV (PC-CTstd) and (2) 130-keV monoenergetic images (PC-CT130 keV). Prior EID-CT was available for 17 patients; for the remaining 15, an age-, sex-, and body mass index-matched EID-CT cohort was identified. Image quality (5-point Likert scales on overall, sharpness, artifacts, noise, diagnostic confidence) of PC-CTstd and EID-CT was assessed by four radiologists independently. If metallic implants were present (n = 10), PC-CTstd and PC-CT130 keV images were again assessed by 5-point Likert scales by the same radiologists. Hounsfield units (HU) were measured within metallic artifact and compared between PC-CTstd and PC-CT130 keV. Finally, the radiation dose (CTDIvol) was evaluated.

RESULTS

Sharpness was rated significantly higher (p = 0.009) and noise significantly lower (p < 0.001) in PC-CTstd vs. EID-CT. In the subset of patients with metallic implants, reading scores for PC-CT130 keV revealed superior ratings vs. PC-CTstd for image quality, artifacts, noise, and diagnostic confidence (all p < 0.001) accompanied by a significant increase of HU values within the artifact (p < 0.001). Radiation dose was significantly lower for PC-CT vs. EID-CT (mean CTDIvol: 8.83 vs. 15.7 mGy; p < 0.001).

CONCLUSIONS

PC-CT of the spine with high-kiloelectronvolt reconstructions provides sharper images, higher diagnostic confidence, and lower radiation dose in patients with metallic implants.

KEY POINTS

• Compared to energy-integrating CT, photon-counting CT of the spine had significantly higher sharpness and lower image noise while radiation dose was reduced by 45%. • In patients with metallic implants, virtual monochromatic photon-counting images at 130 keV were superior to standard reconstruction at 65 keV in terms of image quality, artifacts, noise, and diagnostic confidence.

Metal artifact reduction on musculoskeletal CT: a phantom and clinical study

BACKGROUND

Artifacts caused by metal implants are challenging when undertaking computed tomography (CT). Dedicated algorithms have shown promising results although with limitations. Tin filtration (Sn) in combination with high tube voltage also shows promise but with limitations. There is a need to examine these limitations in more detail. The purpose of this study was to investigate the impact of different metal artefact reduction (MAR) algorithms, tin filtration, and ultra-high-resolution (UHR) scanning, alone or in different combinations in both phantom and clinical settings.

METHODS

An ethically approved clinical and phantom study was conducted. A modified Catphan® phantom with titanium and stainless-steel inserts was scanned with six different MAR protocols with tube voltage ranging from 80 to 150 kVp. Other scan parameters were kept identical. The differences (∆) in mean HU and standard deviation (SD) in images, with and without metal, were measured and compared. In the clinical study, three independent readers performed visual image quality assessments on eight different protocols using retrospectively acquired images.

RESULTS

Iterative MAR had the lowest ∆HU and ∆SD in the phantom study. For images of the forearm, the soft tissue noise for Sn-based 150-kVp UHR protocol with was significantly higher (p = 0.037) than for single-energy MAR protocols. All Sn-based 150-kVp protocols were rated significantly higher (p < 0.046 than the single-energy MAR protocols in the visual assessment.

CONCLUSIONS

All Sn-based 150-kVp UHR protocols showed similar objective MAR in the phantom study, and higher objective MAR and significantly improved visual image quality than single-energy MAR.

RELEVANCE STATEMENT

Images with less metal artifacts and higher visual image quality may be more clinically optimal in CT examination of musculoskeletal patients with metal implants.

KEY POINTS

• Metal artifact reduction algorithms and Sn filter combined with high kVp reduce artifacts. • Metal artifact reduction algorithms introduce new artifacts in certain metals. • Sn-based protocols alone may be considered as low metal artifact protocols.

Comprehensive evaluation of artifact reduction and tissue recovery effects of metal artifact reduction technique based on full-reference metric

For the comprehensive evaluation of metal artifact reduction (MAR) technique, not only the removal of metal artifacts but also the evaluation of the area restored by MAR is required. We propose a method to comprehensively evaluate the effect by MAR in this study. We have conducted the computed tomography scan to acquire both the evaluation image and the reference image for the full-reference based evaluation. The evaluation image and reference image were reconstructed into 24 image sets according to the tube potentials, image reconstruction method, and use of the MAR technique. Images of two different positions were selected according to the distance from metal and material (bone, tissue) distribution, and bone and tissue were automatically segmented in both evaluation and reference images. The values of full width at half the maximum (FWHM) and centroid were extracted after Gaussian modeling of each segmented region. Then, we computed four evaluation metrics (FWHMNM: non-MAR to non-metal ratio of FWHM, FWHMM: MAR to non-metal ratio of FWHM, CENTNM: non-MAR to non-metal ratio of centroid, CENTM: MAR to non-metal ratio of centroid), and the MAR image and non-MAR image were compared. The overlap ratio automatically segmented from the evaluation image and reference image were position 1 (bone: 99.61%, tissue: 99.23%) with 80 kVp, position 1 (bone: 99.32%, tissue: 99.56%) with 120 kVp, position 2 (bone: 99.20%, tissue: 99.73%) with 80 kVp, and position 2 (bone: 99.23%, tissue: 99.67%) with 120 kVp. The FWHMNM showing the change of image pixel value by metal artifact was calculated as (bone: 1.32-1.46, tissue: 1.08-1.16) at 80 kVp and (bone: 1.19-1.27, tissue: 1.02-1.05) at 120 kVp. More metal artifacts occurred at 80 kVp tube potential. Regardless of the tube potential and image reconstruction method, the MAR showed an overall artifact reduction effect (1 < FWHMM < FWHMNM). However, distortion of pixel values occurred due to the MAR in regions where metal artifacts were high in proximity to metal (1 < FWHMNM < FWHMM). Overall, the average value of the medium was maintained (CENTM: 0.98-1.03) after MAR application, but there was a change of image value in region around the metal (CENTM: 0.97-1.11). In this study, we propose a new method to evaluate the effect of metal artifacts and MAR technique using full-reference based method. Metal artifacts, effect of MAR technique, and side-effect caused by MAR technique were quantitatively analyzed through proposed method. There are some limitations in applying it to clinical imaging since our method is a reference-based evaluation. However, our experimental results were important for understanding the effects of the MAR technique and its functional properties.

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.

Current and potential applications of artificial intelligence in medical imaging practice: A narrative review

BACKGROUND AND PURPOSE

Artificial intelligence (AI) is present in many areas of our lives. Much of the digital data generated in health care can be used for building automated systems to bring improvements to existing workflows and create a more personalised healthcare experience for patients. This review outlines select current and potential AI applications in medical imaging practice and provides a view of how diagnostic imaging suites will operate in the future. Challenges associated with potential applications will be discussed and healthcare staff considerations necessary to benefit from AI-enabled solutions will be outlined.

METHODS

Several electronic databases, including PubMed, ScienceDirect, Google Scholar, and University College Dublin Library Database, were used to identify relevant articles with a Boolean search strategy. Textbooks, government sources and vendor websites were also considered.

RESULTS/DISCUSSION

Many AI-enabled solutions in radiographic practice are available with more automation on the horizon. Traditional workflow will become faster, more effective, and more user friendly. AI can handle administrative or technical types of work, meaning it is applicable across all aspects of medical imaging practice.

CONCLUSION

AI offers significant potential to automate most of the manual tasks, ensure service consistency, and improve patient care. Radiographers, radiation therapists, and clinicians should ensure they have adequate understanding of the technology to enable ethical oversight of its implementation.

Pediatric Applications of Photon-Counting Detector CT

Photon-counting detector (PCD) CT represents the most recent generational advance in CT technology. PCD CT has the potential to reduce image noise, improve spatial resolution and contrast resolution, and provide multispectral capability, all of which may be achieved with an overall decrease in the radiation dose. These effects may be used to reduce the iodinated contrast media dose and potentially obtain multiphase images through a single-acquisition technique. The benefits of PCD CT have previously been shown primarily in phantoms and adult patients. This article describes the application of PCD CT in children, as illustrated by clinical examples from a commercially available PCD CT system.

Influence of Scan Parameters of Single and Dual-Energy CT Protocols in Combination with Metal Artifact Suppression Algorithms for THA: An ex Vivo Study

BACKGROUND

Metal artifacts caused by hip arthroplasty stems limit the diagnostic value of computed tomography (CT) in the evaluation of periprosthetic fractures or implant loosening. The aim of this ex vivo study was to evaluate the influence of different scan parameters and metal artifact algorithms on image quality in the presence of hip stems.

METHODS

Nine femoral stems, 6 uncemented and 3 cemented, that had been implanted in subjects during their lifetimes were exarticulated and investigated after death and anatomical body donation. Twelve CT protocols consisting of single-energy (SE) and single-source consecutive dual-energy (DE) scans with and without an iterative metal artifact reduction algorithm (iMAR; Siemens Healthineers) and/or monoenergetic reconstructions were compared. Streak and blooming artifacts as well as subjective image quality were evaluated for each protocol.

RESULTS

Metal artifact reduction with iMAR significantly reduced the streak artifacts in all investigated protocols (p = 0.001 to 0.01). The best subjective image quality was observed for the SE protocol with a tin filter and iMAR. The least streak artifacts were observed for monoenergetic reconstructions of 110, 160, and 190 keV with iMAR (standard deviation of the Hounsfield units: 151.1, 143.7, 144.4) as well as the SE protocol with a tin filter and iMAR (163.5). The smallest virtual growth was seen for the SE with a tin filter and without iMAR (4.40 mm) and the monoenergetic reconstruction of 190 keV without iMAR (4.67 mm).

CONCLUSIONS

This study strongly suggests that metal artifact reduction algorithms (e.g., iMAR) should be used in clinical practice for imaging of the bone-implant interface of prostheses with either an uncemented or cemented femoral stem. Among the iMAR protocols, the SE protocol with 140 kV and a tin filter produced the best subjective image quality. Furthermore, this protocol and DE monoenergetic reconstructions of 160 and 190 keV with iMAR achieved the lowest levels of streak and blooming artifacts.

LEVEL OF EVIDENCE

Diagnostic Level III. See Instructions for Authors for a complete description of levels of evidence.

A Dilated Residual Network for Turbine Blade ICT Image Artifact Removal

Artifacts are divergent strip artifacts or dark stripe artifacts in Industrial Computed Tomography (ICT) images due to large differences in density among the components of scanned objects, which can significantly distort the actual structure of scanned objects in ICT images. The presence of artifacts can seriously affect the practical application effectiveness of ICT in defect detection and dimensional measurement. In this paper, a series of convolution neural network models are designed and implemented based on preparing the ICT image artifact removal datasets. Our findings indicate that the RF (receptive field) and the spatial resolution of network can significantly impact the effectiveness of artifact removal. Therefore, we propose a dilated residual network for turbine blade ICT image artifact removal (DRAR), which enhances the RF of the network while maintaining spatial resolution with only a slight increase in computational load. Extensive experiments demonstrate that the DRAR achieves exceptional performance in artifact removal.

Metal implants on abdominal CT: does split-filter dual-energy CT provide additional value over iterative metal artifact reduction?

PURPOSE

To assess image quality and metal artifact reduction in split-filter dual-energy CT (sfDECT) of the abdomen with hip or spinal implants using virtual monoenergetic images (VMI) and iterative metal artifact reduction algorithm (iMAR).

METHODS

102 portal-venous abdominal sfDECTs of patients with hip (n = 71) or spinal implants (n = 31) were included in this study. Images were reconstructed as 120kVp-equivalent images (Mixed) and VMI (40-190 keV), with and without iMAR. Quantitative artifact and image noise was measured using 12 different ROIs. Subjective image quality was rated by two readers using a five-point Likert-scale in six categories, including overall image quality and vascular contrast.

RESULTS

Lowest quantitative artifact in both hip and spinal implants was measured in VMI_190keV-iMAR. However, it was not significantly lower than in Mixed_iMAR (for all ROIs, p = 1.00), which were rated best for overall image quality (hip: 1.00 [IQR: 1.00-2.00], spine: 3.00 [IQR:2.00-3.00]). VMI_50keV-iMAR was rated best for vascular contrast (hip: 1.00 [IQR: 1.00-2.00], spine: 2.00 [IQR: 1.00-2.00]), which was significantly better than Mixed (both, p < 0.001). VMI_50keV-iMAR provided superior overall image quality compared to Mixed for hip (1.00 vs 2.00, p < 0.001) and similar diagnostic image quality for spinal implants (2.00 vs 2.00, p = 0.51).

CONCLUSION

For abdominal sfDECT with hip or spinal implants Mixed_iMAR images should be used. High keV VMI do not further improve image quality. IMAR allows the use of low keV images (VMI_50keV) to improve vascular contrast, compared to Mixed images.

Advances in Bone Joint Imaging-Metal Artifact Reduction

Numerous types of metal implants have been introduced in orthopedic surgery and are used in everyday practice. To precisely evaluate the postoperative condition of arthroplasty or trauma surgery, periprosthetic infection, and the loosening of implants, it is important to reduce artifacts induced by metal implants. In this review, we focused on technical advances in metal artifact reduction using digital tomosynthesis, computed tomography, and magnetic resonance imaging. We discussed new developments in diagnostic imaging methods and the continuous introduction of novel technologies to reduce metal artifacts; however, these innovations have not yet completely removed metal artifacts. Different algorithms need to be selected depending on the size, shape, material and implanted body parts of an implant. Future advances in metal artifact reduction algorithms and techniques and the development of new sequences may enable further reductions in metal artifacts even on original images taken previously. Moreover, the combination of different imaging modalities may contribute to further reductions in metal artifacts. Clinicians must constantly update their knowledge and work closely with radiologists to select the best diagnostic imaging method for each metal implant.

Prediction of proton beam range in phantom with metals based on monochromatic energy CT images

The purpose of the study was to evaluate the accuracy of monochromatic energy (MonoE) computed tomography (CT) images reconstructed by spectral CT in predicting the stopping power ratio $( SP{R}_w)$ of materials in the presence of metal. The CIRS062 phantom was scanned three times using spectral CT. In the first scan, a solid water insert was placed at the center of the phantom $(C{T}_{no\ metal})$. In the second scan, the solid water insert was replaced with a titanium alloy femoral head $(C{T}_{metal})$. The metal artifact reduction (MAR) algorithm was used in the last scan $(C{T}_{metal+ MAR})$. The MonoE-CT images of 40 keV and 80 keV were reconstructed. Finally, the single-energy CT method (SECT) and the dual-energy CT method (DECT) were used to calculate the $SP{R}_w$. The mean absolute error (MAE) of the $SP{R}_w$ of the inner layer inserts calculated by the SECT method were 3.19%, 13.88% and 2.71%, corresponding to $C{T}_{no\ metal}$, $C{T}_{metal}$ and $C{T}_{metal+ MAR}$, respectively. For the outer layer inserts, the MAE of $SP{R}_w$ were 3.43%, 5.42% and 2.99%, respectively. Using the DECT method, the MAE of the $SP{R}_w$ of the inner layer inserts was 1.30%, 3.69% and 1.46% and the MAE of the outer layer inserts- was 1.34%, 1.36% and 1.05%. The studies shows that, compared with the SECT method, the accuracy of the DECT method in predicting the $SP{R}_w$ of a material is more robust to the presence of metal. Using the MAR algorithm when performing CT scans can further improve the accuracy of predicting the SPR of materials in the presence of metal.

Comparison of Metal Artifact Reduction Algorithms in Patients with Hip Prostheses: Virtual Monoenergetic Images vs. Orthopedic Metal Artifact Reduction

Purpose

To assess the usefulness of various metal artifact reduction (MAR) methods in patients with hip prostheses.

Materials and Methods

This retrospective study included 47 consecutive patients who underwent hip arthroplasty and dual-energy CT. Conventional polyenergetic image (CI), orthopedic-MAR (O-MAR), and virtual monoenergetic image (VMI, 50-200 keV) were tested for MAR. Quantitative analysis was performed in seven regions around the prostheses. Qualitative assessments included evaluation of the degree of artifacts and the presence of secondary artifacts.

Results

The lowest amount of image noise was observed in the O-MAR, followed by the VMI. O-MAR also showed the lowest artifact index, followed by high-keV VMI in the range of 120-200 keV (soft tissue) or 200 keV (bone). O-MAR had the highest contrast-to-noise ratio (CNR) in regions with severe hypodense artifacts, while VMI had the highest CNR in other regions, including the periprosthetic bone. On assessment of the CI of pelvic soft tissues, VMI showed a higher structural similarity than O-MAR. Upon qualitative analysis, metal artifacts were significantly reduced in O-MAR, followed by that in VMI, while secondary artifacts were the most frequently found in the O-MAR (p < 0.001).

Conclusion

O-MAR is the best technique for severe MAR, but it can generate secondary artifacts. VMI at high keV can be advantageous for evaluating periprosthetic bone.

Validation of a deep learning-based material estimation model for Monte Carlo dose calculation in proton therapy

Objective. Computed tomography (CT) to material property conversion dominates proton range uncertainty, impacting the quality of proton treatment planning. Physics-based and machine learning-based methods have been investigated to leverage dual-energy CT (DECT) to predict proton ranges. Recent development includes physics-informed deep learning (DL) for material property inference. This paper aims to develop a framework to validate Monte Carlo dose calculation (MCDC) using CT-based material characterization models.Approach.The proposed framework includes two experiments to validatein vivodose and water equivalent thickness (WET) distributions using anthropomorphic and porcine phantoms. Phantoms were irradiated using anteroposterior proton beams, and the exit doses and residual ranges were measured by MatriXX PT and a multi-layer strip ionization chamber. Two pre-trained conventional and physics-informed residual networks (RN/PRN) were used for mass density inference from DECT. Additional two heuristic material conversion models using single-energy CT (SECT) and DECT were implemented for comparisons. The gamma index was used for dose comparisons with criteria of 3%/3 mm (10% dose threshold).Main results. The phantom study showed that MCDC with PRN achieved mean gamma passing rates of 95.9% and 97.8% for the anthropomorphic and porcine phantoms. The rates were 86.0% and 79.7% for MCDC with the empirical DECT model. WET analyses indicated that the mean WET variations between measurement and simulation were -1.66 mm, -2.48 mm, and -0.06 mm for MCDC using a Hounsfield look-up table with SECT and empirical and PRN models with DECT. Validation experiments indicated that MCDC with PRN achieved consistent dose and WET distributions with measurement.Significance. The proposed framework can be used to identify the optimal CT-based material characterization model for MCDC to improve proton range uncertainty. The framework can systematically verify the accuracy of proton treatment planning, and it can potentially be implemented in the treatment room to be instrumental in online adaptive treatment planning.

Imaging of total hip arthroplasty: Part I – Implant design, imaging techniques, and imaging of component wear and fracture

Despite being one of the most reliable procedures in orthopedic surgery, complications can occur after total hip arthroplasty, and radiology plays an essential role in their evaluation. This article will review the various types of hip arthroplasty and their normal appearances on imaging, followed by mechanisms and imaging appearances of component wear and fracture.

Metal artifact reduction in ultra-high-resolution cone-beam CT imaging with a twin robotic X-ray system

Cone-beam computed tomography (CBCT) has been shown to be a powerful tool for 3D imaging of the appendicular skeleton, allowing for detailed visualization of bone microarchitecture. This study was designed to compare artifacts in the presence of osteosynthetic implants between CBCT and multidetector computed tomography (MDCT) in cadaveric wrist scans. A total of 32 scan protocols with varying tube potential and current were employed: both conventional CBCT and MDCT studies were included with tube voltage ranging from 60 to 140 kVp as well as additional MDCT protocols with dedicated spectral shaping via tin prefiltration. Irrespective of scanner type, all examinations were conducted in ultra-high-resolution (UHR) scan mode. For reconstruction of UHR-CBCT scans an additional iterative metal artifact reduction algorithm was employed, an image correction tool which cannot be used in combination with UHR-MDCT. To compare applied radiation doses between both scanners, the volume computed tomography dose index for a 16 cm phantom (CTDIvol) was evaluated. Images were assessed regarding subjective and objective image quality. Without automatic tube current modulation or tube potential control, radiation doses ranged between 1.3 mGy (with 70 kVp and 50.0 effective mAs) and 75.2 mGy (with 140 kVp and 383.0 effective mAs) in UHR-MDCT. Using the pulsed image acquisition method of the CBCT scanner, CTDIvol ranged between 2.3 mGy (with 60 kVp and 0.6 mean mAs per pulse) and 61.0 mGy (with 133 kVp and 2.5 mean mAs per pulse). In essence, all UHR-CBCT protocols employing a tube potential of 80 kVp or more were found to provide superior overall image quality and artifact reduction compared to UHR-MDCT (all p < .050). Interrater reliability of seven radiologists regarding image quality was substantial for tissue assessment and moderate for artifact assessment with Fleiss kappa of 0.652 (95% confidence interval 0.618-0.686; p < 0.001) and 0.570 (95% confidence interval 0.535-0.606; p < 0.001), respectively. Our results demonstrate that the UHR-CBCT scan mode of a twin robotic X-ray system facilitates excellent visualization of the appendicular skeleton in the presence of metal implants. Achievable image quality and artifact reduction are superior to dose-comparable UHR-MDCT and even MDCT protocols employing spectral shaping with tin prefiltration do not achieve the same level of artifact reduction in adjacent soft tissue.

Reliability and benefits of single-energy projection-based metallic artifact reduction (SEMAR) in the different orthopedic hardware for the hip

OBJECTIVE

To evaluate the performance and reliability of the single-energy metal artifact reduction (SEMAR) algorithm in patients with different orthopedic hardware at the hips.

MATERIALS AND METHODS

A total of 153 patients with hip instrumentation who had undergone CT with adaptive iterative dose reduction (AIDR) 3D and SEMAR algorithms between February 2015 and October 2019 were included retrospectively. Patients were divided into 5 groups by the hardware type. Two readers with 21 and 13 years of experience blindly reviewed all image sets and graded the extent of artifacts and imaging quality using 5-point scales. To evaluate reliability, the mean densities and image noises were measured at the urinary bladder, veins, and fat in images with artifacts and the reference images.

RESULTS

No significant differences were found in the mean densities of the urinary bladder, veins, and fat between the SEMAR images with artifacts (7.57 ± 9.49, 40.29 ± 23.07, 86.78 ± 38.34) and the reference images (7.77 ± 6.2, 40.27 ± 8.66, 89.10 ± 20.70) (P = .860, .994, .392). Image noises of the urinary bladder in the SEMAR images with artifacts (14.25 ± 4.50) and the SEMAR reference images (9.69 ± 1.29) were significantly higher than those in the AIDR 3D reference images (9.11 ± 1.12) (P < .001; P < .001). All AIDR 3D images were non-diagnostic (overall quality ≤ 3) and less than a quarter of the SEMAR images were non-diagnostic (16.7-23.7%), mainly in patients with prostheses [reader 1: 91.7% (22/24); reader 2: 92.6% (25/27)].

CONCLUSION

The SEMAR algorithm significantly reduces metal artifacts in CT images, more in patients with internal fixations than in patients with prostheses, and provides reliable attenuation of soft tissues.

Combining gantry-free cone-beam computed tomography with iterative metal artefact reduction for surgical follow-up imaging of the appendicular skeleton

PURPOSE

Post-surgical evaluation of osteosynthesis material and adjacent tissue can be challenging in both radiography and cross-sectional imaging. This study investigates the performance of a multi-purpose X-ray scanner with cone-beam CT (CBCT) function and iterative metal artefact reduction capabilities in patients after osteoplasty of the appendicular skeleton.

METHOD

Eighty individuals who underwent both conventional X-ray imaging and CBCT after osteoplasty of the hand/wrist (48), elbow (14), or ankle/foot (18) with the gantry-free twin robotic system were retrospectively enrolled. Radiological reports from clinical routine for both imaging modalities were retrospectively analyzed and compared with consensus expert reading by two musculoskeletal specialists serving as the standard of reference. Findings of screw dislocation or implant loosening, fragment displacement, and delayed healing were compared between X-ray and CBCT reports using the McNemar test.

RESULTS

The median dose-area-product of CBCT and X-ray scans amounted to 27.98 and 0.2 dGy*cm², respectively. Diagnostic accuracy for screw dislocation was superior in CBCT compared to standard radiograms (98.8 % vs 83.8 %; p = 0.002). Implant loosening (98.8 % vs 86.3 %; p = 0.006), fragment displacement (98.8 % vs 85.0 %; p < 0.001), and delayed healing (97.5 % vs 88.8 %; p = 0.016) were also more reliably detected in CBCT. Employing CBCT, postoperative complications were detected with a sensitivity and specificity of at least 95.8 % and 98.1 %, compared to 33.3 % and 92.86 % in radiography.

CONCLUSIONS

With superior accuracy for various osteoplasty-related complications, the CBCT scan mode of a gantry-free twin robotic X-ray system with iterative metal artefact reduction aids post-surgical assessment in the appendicular skeleton.

Dual-energy CT based mass density and relative stopping power estimation for proton therapy using physics-informed deep learning

Proton therapy requires accurate dose calculation for treatment planning to ensure the conformal doses are precisely delivered to the targets. The conversion of CT numbers to material properties is a significant source of uncertainty for dose calculation. The aim of this study is to develop a physics-informed deep learning (PIDL) framework to derive accurate mass density and relative stopping power maps from dual-energy computed tomography (DECT) images. The PIDL framework allows deep learning (DL) models to be trained with a physics loss function, which includes a physics model to constrain DL models. Five DL models were implemented including a fully connected neural network (FCNN), dual-FCNN (DFCNN), and three variants of residual networks (ResNet): ResNet-v1 (RN-v1), ResNet-v2 (RN-v2), and dual-ResNet-v2 (DRN-v2). An artificial neural network (ANN) and the five DL models trained with and without physics loss were explored to evaluate the PIDL framework. Two empirical DECT models were implemented to compare with the PIDL method. DL training data were from CIRS electron density phantom 062M (Computerized Imaging Reference Systems, Inc., Norfolk, VA). The performance of DL models was tested by CIRS adult male, adult female, and 5-year-old child anthropomorphic phantoms. For density map inference, the physics-informed RN-v2 was 3.3%, 2.9% and 1.9% more accurate than ANN for the adult male, adult female, and child phantoms. The physics-informed DRN-v2 was 0.7%, 0.6%, and 0.8% more accurate than DRN-v2 without physics training for the three phantoms, respectfully. The results indicated that physics-informed training could reduce uncertainty when ANN/DL models without physics training were insufficient to capture data structures or derived significant errors. DL models could also achieve better image noise control compared to the empirical DECT parametric mapping methods. The proposed PIDL framework can potentially improve proton range uncertainty by offering accurate material properties conversion from DECT.

Universal orbit design for metal artifact elimination

Objective.Metal artifacts are a persistent problem in CT and cone-beam CT. In this work, we propose to reduce or even eliminate metal artifacts by providing better sampling of data using non-circular orbits.Approach.We treat any measurements intersecting metal as missing data, and aim to design a universal orbit that can generally accommodate arbitrary metal shapes and locations. We adapted a local sampling completeness metric based on Tuy's condition to quantify the extent of sampling in the presence of metal. A maxi-min objective over all possible metal locations was used for orbit design. A simple class of sinusoidal orbits was evaluated as a function of frequencies, maximum tilt angles, and orbital extents. Experimental implementation of these orbits were performed on an imaging bench and evaluated on two phantoms, one containing metal balls and the other containing a pedicle screw assembly for spine fixation. Metal artifact reduction (MAR) performance was compared amongst three approaches: non-circular orbits only, algorithmic correction only, and a combined approach.Main results.Theoretical evaluations of the objective favor sinusoidal orbits with large tilt angles and large orbital extents. Furthermore, orbits that leverage redundant azimuthal angles to sample non-redundant data have better performance, e.g. even or non-integer frequency sinusoids for a 360° acquisition. Experimental data support the trends observed in theoretical evaluations. Reconstructions using even or non-integer frequency orbits present less streaking artifacts and background details with finer resolution, even when multiple metal objects are present and even in the absence of MAR algorithms. The combined approach of non-circular orbits and MAR algorithm yields the best performance. The observed trend in image quality is supported by quantitative measures of sampling and severity of streaking artifact.Significance.This work demonstrates that sinusoidal orbits are generally robust against metal artifacts and can provide an avenue for improved image quality in interventional imaging.

Clinical Evaluation of an Innovative Metal-Artifact-Reduction Algorithm in FD-CT Angiography in Cerebral Aneurysms Treated by Endovascular Coiling or Surgical Clipping

Treated cerebral aneurysms (IA) require follow-up imaging to ensure occlusion. Metal artifacts complicate radiologic assessment. Our aim was to evaluate an innovative metal-artifact-reduction (iMAR) algorithm for flat-detector computed tomography angiography (FD-CTA) regarding image quality (IQ) and detection of aneurysm residua/reperfusion in comparison to 2D digital subtraction angiography (DSA). Patients with IAs treated by endovascular coiling or clipping underwent both FD-CTA and DSA. FD-CTA datasets were postprocessed with/without iMAR algorithm (MAR+/MAR−). Evaluation of all FD-CTA and DSA datasets regarding qualitative (IQ, MAR) and quantitative (coil package diameter/CPD) parameters was performed. Aneurysm occlusion was assessed for each dataset and compared to DSA findings. In total, 40 IAs were analyzed (ncoiling = 24; nclipping = 16). All iMAR+ datasets demonstrated significantly better IQ (pIQ coiling < 0.0001; pIQ clipping < 0.0001). iMAR significantly reduced the metal-artifact burden but did not affect the CPD. iMAR significantly improved the detection of aneurysm residua/reperfusion with excellent agreement with DSA (naneurysm detection MAR+/MAR−/DSA = 22/1/26). The iMAR algorithm significantly improves IQ by effective reduction of metal artifacts in FD-CTA datasets. The proposed algorithm enables reliable detection of aneurysm residua/reperfusion with good agreement to DSA. Thus, iMAR can help to reduce the need for invasive follow-up in treated IAs.

Classification and Segmentation Algorithm in Benign and Malignant Pulmonary Nodules under Different CT Reconstruction

Methods

The imaging data of 55 patients with chest CT plain scan in the Xuancheng People's Hospital were collected retrospectively. The data of each patient included lung window reconstruction, mediastinum reconstruction, and bone window reconstruction. The depth neural network and 3D convolution neural network were used to construct the model and train the classification and segmentation algorithm. The pathological results were the gold standard for benign and malignant pulmonary nodules. The classification and segmentation algorithms under three CT reconstruction algorithms were compared and analyzed by analysis of variance.

Results

Under the three CT reconstruction algorithms, the classification accuracy of pulmonary nodule density types was 98.2%, 96.4%, and 94.5%, respectively. The Dice coefficients of all nodule segmentation were 80.32% ± 5.91%, 79.83% ± 6.12%, and 80.17% ± 5.89%, respectively. The diagnostic accuracy between benign and malignant pulmonary nodules under different reconstruction algorithms was 98.2%, 96.4%, and 94.5%, respectively. There was no significant difference in the classification accuracy, Dice coefficients, and diagnostic accuracy of pulmonary nodules under three different reconstruction algorithms (all P > 0.05).

Conclusion

The depth neural network algorithm combined with 3D convolution neural network has a good efficiency in identifying benign and malignant pulmonary nodules under different CT reconstruction classification and segmentation algorithms.

Addressing CT metal artifacts using photon-counting detectors and one-step spectral CT image reconstruction

PURPOSE

The constrained one-step spectral CT image reconstruction (cOSSCIR) algorithm with a nonconvex alternating direction method of multipliers optimizer is proposed for addressing computed tomography (CT) metal artifacts caused by beam hardening, noise, and photon starvation. The quantitative performance of cOSSCIR is investigated through a series of photon-counting CT simulations.

METHODS

cOSSCIR directly estimates basis material maps from photon-counting data using a physics-based forward model that accounts for beam hardening. The cOSSCIR optimization framework places constraints on the basis maps, which we hypothesize will stabilize the decomposition and reduce streaks caused by noise and photon starvation. Another advantage of cOSSCIR is that the spectral data need not be registered, so that a ray can be used even if some energy window measurements are unavailable. Photon-counting CT acquisitions of a virtual pelvic phantom with low-contrast soft tissue texture and bilateral hip prostheses were simulated. Bone and water basis maps were estimated using the cOSSCIR algorithm and combined to form a virtual monoenergetic image for the evaluation of metal artifacts. The cOSSCIR images were compared to a "two-step" decomposition approach that first estimated basis sinograms using a maximum likelihood algorithm and then reconstructed basis maps using an iterative total variation constrained least-squares optimization (MLE+TV min $_{\text{min}}$ ). Images were also compared to a nonspectral TV min $_{\text{min}}$ reconstruction of the total number of counts detected for each ray with and without normalized metal artifact reduction (NMAR) applied. The simulated metal density was increased to investigate the effects of increasing photon starvation. The quantitative error and standard deviation in regions of the phantom were compared across the investigated algorithms. The ability of cOSSCIR to reproduce the soft-tissue texture, while reducing metal artifacts, was quantitatively evaluated.

RESULTS

Noiseless simulations demonstrated the convergence of the cOSSCIR and MLE+TV min $_{\text{min}}$ algorithms to the correct basis maps in the presence of beam-hardening effects. When noise was simulated, cOSSCIR demonstrated a quantitative error of -1 HU, compared to 2 HU error for the MLE+TV min $_{\text{min}}$ algorithm and -154 HU error for the nonspectral TV min $_{\text{min}}$ +NMAR algorithm. For the cOSSCIR algorithm, the standard deviation in the central iodine region of interest was 20 HU, compared to 299 HU for the MLE+TV min $_{\text{min}}$ algorithm, 41 HU for the MLE+TV min $_{\text{min}}$ +Mask algorithm that excluded rays through metal, and 55 HU for the nonspectral TV min $_{\text{min}}$ +NMAR algorithm. Increasing levels of photon starvation did not impact the bias or standard deviation of the cOSSCIR images. cOSSCIR was able to reproduce the soft-tissue texture when an appropriate regularization constraint value was selected.

CONCLUSIONS

By directly inverting photon-counting CT data into basis maps using an accurate physics-based forward model and a constrained optimization algorithm, cOSSCIR avoids metal artifacts due to beam hardening, noise, and photon starvation. The cOSSCIR algorithm demonstrated improved stability and accuracy compared to a two-step method of decomposition followed by reconstruction.

Impact of Different Metal Artifact Reduction Techniques on Attenuation Correction of Normal Organs in 18F-FDG-PET/CT

Purpose: To evaluate the impact of different metal artifact reduction algorithms on Hounsfield units (HU) and the standardized uptake value (SUV) in normal organs in patients with different metal implants. Methods: This study prospectively included 66 patients (mean age of 66.02 ± 13.1 years) with 87 different metal implants. CT image reconstructions were performed using weighted filtered back projection (WFBP) as the standard method, metal artifact reduction in image space (MARIS), and an iterative metal artifacts reduction (iMAR) algorithm for large implants. These datasets were used for PET attenuation correction. HU and SUV measurements were performed in nine predefined anatomical locations: liver, lower lung lobes, descending aorta, thoracic vertebral body, autochthonous back muscles, pectoral muscles, and internal jugular vein. Differences between HU and SUV measurements were compared using paired t-tests. The significance level was determined as p = 0.017 using Bonferroni correction. Results: No significant differences were observed between reconstructed images using iMAR and WFBP concerning HU and SUV measurements in liver (HU: p = 0.055; SUVmax: p = 0.586), lung (HU: p = 0.276; SUVmax: p = 1.0 for the right side and HU: p = 0.630; SUVmax: p = 0.109 for the left side), descending aorta (HU: p = 0.333; SUVmax: p = 0.083), thoracic vertebral body (HU: p = 0.725; SUVmax: p = 0.392), autochthonous back muscles (HU: p = 0.281; SUVmax: p = 0.839), pectoral muscles (HU: p = 0.481; SUVmax: p = 0.277 for the right side and HU: p = 0.313; SUVmax: p = 0.859 for the left side), or the internal jugular vein (HU: p = 0.343; SUVmax: p = 0.194). Conclusion: Metal artifact reduction algorithms such as iMAR do not alter the data information of normal organs not affected by artifacts.

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13244-021-01111-5.