Performance of deep-learning reconstruction combined with metal artifact reduction algorithm for dual-energy computed tomography angiography in intracranial aneurysm coil embolization

Aiming to evaluate the effects of the smart metal artifact reduction (MAR) algorithm and combinations of various scanning parameters, including radiation dose levels, tube voltage, and reconstruction algorithms, on metal artifact reduction and overall image quality, to identify the optimal protocol for clinical application. A phantom with a pacemaker was examined using standard dose (effective dose (ED): 3 mSv) and low dose (ED: 0.5 mSv), with three scan voltages (70, 100, and 120 kVp) selected for each dose. Raw data were reconstructed using 50% adaptive statistical iterative reconstruction-V (ASIR-V), ASIR-V with MAR, high-strength deep learning image reconstruction (DLIR-H), and DLIR-H with MAR. Quantitative analyses (artifact index (AI), noise, signal-to-noise ratio (SNR) of artifact-impaired pulmonary nodules (PNs), and noise power spectrum (NPS) of artifact-free regions) and qualitative evaluation were performed. Quantitatively, the deep learning image recognition (DLIR) algorithm or high tube voltages exhibited lower noise compared to the ASIR-V or low tube voltages (p<0.001). AI of images with MAR or high tube voltages was significantly lower than that of images without MAR or low tube voltages (p<0.001). No significant difference was observed in AI between low-dose images with 120 kVp DLIR-H MAR and standard-dose images with 70 kVp ASIR-V MAR (p=0.143). Only the 70 kVp 3 mSv protocol demonstrated statistically significant differences in SNR for artifact-impaired PNs (p=0.041). The fpeak and favg values were similar across various scenarios, indicating that the MAR algorithm did not alter the image texture in artifact-free regions. The qualitative results of the extent of metal artifacts, the confidence in diagnosing artifact-impaired PNs, and the overall image quality were generally consistent with the quantitative results. The MAR algorithm combined with DLIR-H can reduce metal artifacts and enhance the overall image quality, particularly at high kVp tube voltages.

Metal artifact reduction from surgical clips for intracranial aneurysms in photon-counting detector CT angiography.

This study aims to investigate the impact of combining deep learning image reconstruction (DLIR) and metal artifacts reduction (MAR) algorithms on the quality of CT images with metal implants under different scanning conditions. Four images of the maxillofacial region in pigs were taken using different metal implants for evaluation. The scans were conducted at three different dose levels (CTDIvol: 20/10/5mGy). The images were reconstructed using three different methods: filtered back projection (FBP), adaptive statistical iterative reconstruction with Veo at a 50% level (AV50), and DLIR at three levels (low, medium, and high). Regions of interest (ROIs) were identified in various tissues (near/far/reference fat, muscle, bone) both with and without metal implants and artifacts. Parameters such as standard deviation (SD), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and metal artifact index (MAI) were calculated. Additionally, two experienced radiologists evaluated the subjective image quality (IQ) using a 5-point Likert scale. (1) Both observers rated MAR generated significantly lower artifact scores than non-MAR in all types of tissues (P < 0.01), except for the far shadow and bloom in bone (phantoms 1, 3, 4) and the far bloom in muscle (phantom 3) without significant differences (P = 1.0). (2) Under the same scanning condition, DLIR at three levels produced a smaller SD than those of FBP and AV50 (P < 0.05). (3) Compared to FBP and AV50, DLIR denoted a better reduction of MAI and improvement of SNR and CNR (P < 0.05) for most tissues between the four phantoms. (4) Subjective overall IQ was superior with the increasement of DLIR level (P < 0.05) and both observers agreed that DLIR produced better artifact reductions compared with FBP and AV50. The combination of DLIR and MAR algorithms can enhance image quality, significantly reduce metal artifacts, and offer high clinical value.

The commonly used methods for removing metal-induced beam hardening artifacts often rely on the use of high energy photons with either high tube voltage or high energy virtual monoenergetic images in dual-energy computed tomography (CT), the radiation dose was usually relatively high in order to generate adequate signals. This retrospective study is designed to evaluate the application of a metal artifact reduction (MAR) algorithm in reducing pedicle screw metal-caused beam hardening artifacts in post-surgery pediatric low radiation dose spine CT images. Seventy-seven children (3-15 years) who had undergone a low dose CT with 140 or 100 kV were enrolled. The radiation dose was 1.40 mGy for the 3-8 years old and 2.61 mGy for 9-15 years old children. There were 116 pedicle screws evaluated. The raw data were reconstructed with adaptive statistical iterative reconstruction-V (ASIR-V) at 50% strength, ASIR-V with MAR (AV-MAR), deep learning image reconstruction (DLIR) at high strength and DLIR with MAR (DL-MAR). The image quality concerning pedicle screws was evaluated objectively in terms of the length of beam-hardening artifact (LHA) and artifact index (AI), and subjectively using a 4-point scale (4 points: best, 3 points: acceptable). Both AV-MAR and DL-MAR significantly reduced metal-induced beam hardening artifacts with smaller LHA (15.76±10.12 mm, a reduction of 57.24% and 15.66±10.49 mm, a reduction of 57.40%, respectively), and AI value (62.50±33.51, a reduction of 64.65% and 61.03±32.61, a reduction of 65.01%, respectively) compared to ASIR-V and DLIR (all P<0.01), The subjective image quality scores concerning the screws were 3.37±0.49 and 3.47±0.50 with AV-MAR and DL-MAR, respectively, higher than the respective value of 1.73±0.44 and 1.76±0.43 without MAR (all P<0.01). MAR significantly reduces the low-density artifacts caused by metal screws in post-surgery pediatric low-dose spine CT images, across different tube voltages, radiation dose levels and reconstruction algorithms. Combining DL-MAR further improves the overall image quality under low radiation dose conditions.

ObjectivesTo evaluate the reduction of artifacts from cardiac implantable electronic devices (CIEDs) by virtual monoenergetic images (VMI), metal artifact reduction (MAR) algorithms, and their combination (VMIMAR) derived from spectral detector CT (SDCT) of the chest compared to conventional CT images (CI).MethodsIn this retrospective study, we included 34 patients (mean age 74.6 ± 8.6 years), who underwent a SDCT of the chest and had a CIED in place. CI, MAR, VMI, and VMIMAR (10 keV increment, range: 100–200 keV) were reconstructed. Mean and standard deviation of attenuation (HU) among hypo- and hyperdense artifacts adjacent to CIED generator and leads were determined using ROIs. Two radiologists qualitatively evaluated artifact reduction and diagnostic assessment of adjacent tissue.ResultsCompared to CI, MAR and VMIMAR ≥ 100 keV significantly increased attenuation in hypodense and significantly decreased attenuation in hyperdense artifacts at CIED generator and leads (p < 0.05). VMI ≥ 100 keV alone only significantly decreased hyperdense artifacts at the generator (p < 0.05). Qualitatively, VMI ≥ 100 keV, MAR, and VMIMAR ≥ 100 keV provided significant reduction of hyper- and hypodense artifacts resulting from the generator and improved diagnostic assessment of surrounding structures (p < 0.05). Diagnostic assessment of structures adjoining to the leads was only improved by MAR and VMIMAR 100 keV (p < 0.05), whereas keV values ≥ 140 with and without MAR significantly worsened diagnostic assessment (p < 0.05).ConclusionsThe combination of VMI and MAR as well as MAR as a standalone approach provides effective reduction of artifacts from CIEDs. Still, higher keV values should be applied with caution due to a loss of soft tissue and vessel contrast along the leads.Key Points• The combination of VMI and MAR as well as MAR as a standalone approach enables effective reduction of artifacts from CIEDs.• Higher keV values of both VMI and VMIMARat CIED leads should be applied with caution since diagnostic assessment can be hampered by a loss of soft tissue and vessel contrast.• Recommended keV values for CIED generators are between 140 and 200 keV and for leads around 100 keV.

The value of metal artifact reduction and iterative algorithms in dual energy CT angiography in patients after complex endovascular aortic aneurysm repair

Potential of Metal Artifact Reduction (MAR) and Deep Learning-based Reconstruction (DLR) algorithms integration in CT Metal Artifact Correction: A review

Assessment of cardiac implantable electric device lead perforation using a metal artifact reduction algorithm in cardiac computed tomography

PurposeAlthough several metal artifact reduction (MAR) algorithms for computed tomography (CT) scanning are commercially available, no quantitative, rigorous, and reproducible method exists for assessing their performance. The lack of assessment methods poses a challenge to regulators, consumers, and industry. We explored a phantom‐based framework for assessing an important aspect of MAR performance: how applying MAR in the presence of metal affects model observer performance at a low‐contrast detectability (LCD) task This work is, to our knowledge, the first model observer–based framework for the evaluation of MAR algorithms in the published literature.MethodsWe designed a numerical head phantom with metal implants. In order to incorporate an element of randomness, the phantom included a rotatable inset with an inhomogeneous background. We generated simulated projection data for the phantom. We applied two variants of a simple MAR algorithm, sinogram inpainting, to the projection data, that we reconstructed using filtered backprojection. To assess how MAR affected observer performance, we examined the detectability of a signal at the center of a region of interest (ROI) by a channelized Hotelling observer (CHO). As a figure of merit, we used the area under the ROC curve (AUC).ResultsWe used simulation to test our framework on two variants of the MAR technique of sinogram inpainting. We found that our method was able to resolve the difference in two different MAR algorithms’ effect on LCD task performance, as well as the difference in task performances when MAR was applied, vs not.ConclusionWe laid out a phantom‐based framework for objective assessment of how MAR impacts low‐contrast detectability, that we tested on two MAR algorithms. Our results demonstrate the importance of testing MAR performance over a range of object and imaging parameters, since applying MAR does not always improve the quality of an image for a given diagnostic task. Our framework is an initial step toward developing a more comprehensive objective assessment method for MAR, which would require developing additional phantoms and methods specific to various clinical applications of MAR, and increasing study efficiency.

Intracranial aneurysms treated with endovascular embolization often require surveillance imaging using digital subtraction angiography, an invasive, risky, and expensive procedure. Existing non-invasive imaging modalities (standard computed tomography [CT] or magnetic resonance [MR] angiography) are often unsatisfactory for evaluating treated aneurysm due to artifacts from embolization devices. The objective of the present study was to determine whether photon-counting computer tomography (PCCT) imaging parameters could be optimized to confer satisfactory imaging resolution in an anthropomorphic phantom of treated intracranial aneurysms. Phantom studies were performed using a model of the major intracranial arteries with appropriately sized, endovascularly treated middle cerebral artery (coil embolization) and basilar artery (woven endobridge [WEB] embolization) aneurysms. A series of imaging acquisition procedures were performed using a conventional energy-integrating CT (EICT) scanner and a photon-counting CT (PCCT) scanner. Key imaging acquisition and reconstruction parameters were varied to identify the optimum protocol for treated aneurysm characterization. Artifact reduction was performed on all images using the Siemens iterative metal artifact reduction (iMAR) algorithm. Contrast-to-noise ratio and metal artifact magnitude were quantitatively analyzed and displayed in tabular form to provide objective criteria for determination of optimal processing parameters for treated aneurysm visualization. Imaging was successfully obtained in phantom studies across a range of imaging parameters. Quantitative metal artifact magnitude was greater for 100keV virtual monoenergetic images (VMIs) and lowest for 55 keV VMIs without iMAR, but this trend was reversed with iMAR applied. The 55 keV VMI was chosen as the optimal reconstruction parameter for visualization of treated intracranial aneurysms as it demonstrated low magnitude of metal artifacts and the highest contrast-to-noise ratio (CNR) in adjacent vasculature. Similarly, CNR of the largest vessel adjacent to the coil mass was increased for all images after iMAR was applied. CNR was highest in the 55 keV VMR images both before (3.61±0.14) and after (6.82±0.34) application of iMAR. Virtual monoenergetic images combined with metal artifact reduction algorithms created from PCCT scans conferred excellent visualization of previously-treated intracranial aneurysms and adjacent vasculature. It was feasible to extend these results to preliminary clinical applications in human patients.

The objective of this study was to compare the capability of two algorithms for metal artifact reduction and virtual monoenergetic imaging (VME), a metal artifact reduction application for dual-source CT. A bovine vertebra phantom with 16 artificial osteolyses and two 20 × 4.5 mm stainless steel screws was scanned on two single-source CT scanner and one dual-source CT scanner at a dose identical to the single-source acquisitions. Datasets were reconstructed with a metal artifact reduction algorithm for orthopedic implants (O-MAR, Philips Healthcare), an iterative metal artifact reduction algorithm (iMAR, Siemens Healthineers), and VME. Blinded to the method used for artifact reduction, three independent observers evaluated datasets regarding the extent of metal artifacts using a 4-point scale. Depicted osteolyses were counted and screw diameters measured for each reconstruction. Interobserver variability was evaluated using the Kendall coefficient of concordance for ordinal variables and the intraclass correlation coefficient for continuous data. VME showed the best metal artifact reduction capability among evaluated methods; overall artifacts were rated 1.08 ± 0.29 for VME, 3.33 ± 0.65 for iMAR, and 3.91 ± 0.29 for O-MAR (p < 0.01). VME resulted in better representation of the cortical bone, trabecular structure, and soft tissue compared with the other two algorithms. VME provided the most realistic reconstruction of screw diameter. However, VME missed osteolyses. Good to almost perfect agreement was achieved for nearly all evaluated attributes. In our vertebral phantom, VME led to the most detailed representation of the osteosynthesis screw, caused the lowest amount of artifact, and represented the adjacent tissue best. Thus, VME should be considered as an alternative method to evaluate implants when other algorithms fail.

BackgroundMetal artifacts arising around high-density components are a widely known problem in X-ray computed tomography (XCT) for both medical and industrial applications. Although phase contrast imaging XCT (PCI-XCT) is known to be less prone to metal artifacts caused by beam hardening, so far only little effort was made for its comparison to other, more established methods.ObjectiveIn the course of this work, this absence in literature is addressed by a quantitative comparison of PCI-XCT to attenuation contrast XCT (AC-XCT).MethodsA polymer specimen including four Ti6Al4V inserts was investigated by PCI- and AC-XCT with different pre-filter settings and metal artifact reduction (MAR) algorithm. Artifacts and image quality were evaluated by a streak index which provides a quantitative metric for the assessment of streak artifacts and contrast-to-noise ratio (CNR).ResultsResults showed that streak artifacts are significantly reduced in PCI-XCT and only matched by AC-XCT in combination with hardware pre-filtering of the X-ray beam and post-processing by a MAR algorithm. However, hardware pre-filtering leads to worse CNR and artifacts close to the surface of metal inserts could not be removed sufficiently by the MAR algorithm.ConclusionsThis work demonstrates the potential of PCI-XCT for the reduction of metal artifacts and presents the first quantitative comparison to established AC-XCT methods.

We evaluated the effect of a single-energy metal artefact reduction (SEMAR) algorithm for metallic coil artefact reduction in body imaging. Computed tomography angiography (CTA) was performed in 30 patients with metallic coils (10 men, 20 women; mean age, 67.9 ± 11years). Non-SEMAR images were reconstructed with iterative reconstruction alone, and SEMAR images were reconstructed with the iterative reconstruction plus SEMAR algorithms. We compared image noise around metallic coils and the maximum diameters of artefacts from coils between the non-SEMAR and SEMAR images. Two radiologists visually evaluated the metallic coil artefacts utilizing a four-point scale: 1 = extensive; 2 = strong; 3 = mild; 4 = minimal artefacts. The image noise and maximum diameters of the artefacts of the SEMAR images were significantly lower than those of the non-SEMAR images (65.1 ± 33.0 HU vs. 29.7 ± 10.3 HU; 163.9 ± 54.8mm vs. 10.3 ± 19.0mm, respectively; P < 0.001). Better visual scores were obtained with the SEMAR technique (3.4 ± 0.6 vs. 1.0 ± 0.0, P < 0.001). The SEMAR algorithm significantly reduced artefacts caused by metallic coils compared with the non-SEMAR algorithm. This technique can potentially increase CT performance for the evaluation of post-coil embolization complications. • The new algorithm involves a raw data- and image-based reconstruction technique. • The new algorithm mitigates artefacts from metallic coils on body CT images. • The new algorithm significantly reduced artefacts caused by metallic coils. • The metal artefact reduction algorithm improves CT image quality after coil embolization.

This study compares metal artifact (MA) reduction in imaging of total hip replacements (THR) using virtual monoenergetic images (VMI), for MA-reduction-specialized reconstructions (MAR) and conventional CT images (CI) from detector-based dual-energy computed tomography (SDCT). Twenty-seven SDCT-datasets of patients carrying THR were included. CI, MAR and VMI with different energy-levels (60-200 keV) were reconstructed from the same scans. MA width was measured. Attenuation (HU), noise (SD) and contrast-to-noise ratio (CNR) were determined in: extinction artifact, adjacent bone, muscle and bladder. Two radiologists assessed MA-reduction and image quality visually. In comparison to CI, VMI (200 keV) and MAR showed a strong artifact reduction (MA width: CI 29.9±6.8 mm, VMI 17.6±13.6 mm, p<0.001; MAR 16.5±14.9 mm, p<0.001; MA density: CI -412.1±204.5 HU, VMI -279.7±283.7 HU; p<0.01; MAR -116.74±105.6 HU, p<0.001). In strong artifacts reduction was superior by MAR. In moderate artifacts VMI was more effective. MAR showed best noise reduction and CNR in bladder and muscle (p<0.05), whereas VMI were superior for depiction of bone (p<0.05). Visual assessment confirmed that VMI and MAR improve artifact reduction and image quality (p<0.001). MAR and VMI (200 keV) yielded significant MA reduction. Each showed distinct advantages both regarding effectiveness of artifact reduction, MAR regarding assessment of soft tissue and VMI regarding assessment of bone. • Spectral-detector computed tomography improves assessment of total hip replacements and surrounding tissue. • Virtual monoenergetic images and MAR reduce metal artifacts and enhance image quality. • Evaluation of bone, muscle and pelvic organs can be improved by SDCT.

Purpose: To quantify the dosimetric impact of a metal artifacts reduction algorithm for CT images in the framework of Intensity Modulated Treatment dose delivery. Materials and methods: A Gammex 467 Phantom featuring 16 tissue equivalent materials was imaged with a GE Lightspeed RT16 scanner. Three scans were acquired: 1) phantom without metal inserts (Ground Truth, GT), 2) phantom filled with two 3 cm diameter cerrobend inserts (Large Metal Artifacts, LMA) and 3) phantom filled with two 1 cm diameter titanium inserts (Small Metal Artifacts, SMA). The image sinograms, affected from metal artifacts, were processed and restored by an in‐house Metal Artifact Reduction (MAR) algorithm thus obtaining other two image set: LMA_MAR and SMA_MAR. A 5 field IMRT plan was designed on GT to cover 3 cm diameter lung density PTV. Monte Carlo dose calculation was run on the 6 image set. Comparison between SMA vs. SMA_MAR and LMA vs. LMA_MAR was performed in terms of PTV and Volume of Interest (VOI) dose coverage. Results: The initial dose error at the 95% of PTV between GT and LMA and SMA was 12.59% and 0.34% respectively. After MAR correction it was reduced to 0.29% and 0.09%. Two VOIs representing breast and brain tissue respectively, were affected by dose variations caused by LMA. In this case dose underestimation up to 0.5 Gy was found. These inaccuracies were completely corrected after applying the MAR algorithm. Conclusions: This preliminary phantom study showed the importance of restoring CT images, affected by metal artifacts, not only to increase the image quality for diagnosis and contouring purposes, but mainly to avoid large dose prediction errors in radiotherapy delivery. The MAR algorithm will be evaluated for patient IMRT treatments with metal artifacts to assess the related clinical benefit.