Metal implant segmentation in CT images based on diffusion model.

BACKGROUND: Computed tomography (CT) is widely in clinics and is affected by metal implants. Metal segmentation is crucial for metal artifact correction, and the common threshold method often fails to accurately segment metals. PURPOSE: This study aims to segment metal implants in CT images using a diffusion model and further validate it with clinical artifact images and phantom images of known size. METHODS: A retrospective study was conducted on 100 patients who received radiation therapy without metal artifacts, and simulated artifact data were generated using publicly available mask data. The study utilized 11,280 slices for training and verification, and 2,820 slices for testing. Metal mask segmentation was performed using DiffSeg, a diffusion model incorporating conditional dynamic coding and a global frequency parser (GFParser). Conditional dynamic coding fuses the current segmentation mask and prior images at multiple scales, while GFParser helps eliminate high-frequency noise in the mask. Clinical artifact images and phantom images are also used for model validation. RESULTS: Compared with the ground truth, the accuracy of DiffSeg for metal segmentation of simulated data was 97.89% and that of DSC was 95.45%. The mask shape obtained by threshold segmentation covered the ground truth and DSCs were 82.92% and 84.19% for threshold segmentation based on 2500 HU and 3000 HU. Evaluation metrics and visualization results show that DiffSeg performs better than other classical deep learning networks, especially for clinical CT, artifact data, and phantom data. CONCLUSION: DiffSeg efficiently and robustly segments metal masks in artifact data with conditional dynamic coding and GFParser. Future work will involve embedding the metal segmentation model in metal artifact reduction to improve the reduction effect.

Artifact suppression for breast specimen imaging in micro CBCT using deep learning.

BACKGROUND: Cone-beam computed tomography (CBCT) has been introduced for breast-specimen imaging to identify a free resection margin of abnormal tissues in breast conservation. As well-known, typical micro CT consumes long acquisition and computation times. One simple solution to reduce the acquisition scan time is to decrease of the number of projections, but this method generates streak artifacts on breast specimen images. Furthermore, the presence of a metallic-needle marker on a breast specimen causes metal artifacts that are prominently visible in the images. In this work, we propose a deep learning-based approach for suppressing both streak and metal artifacts in CBCT. METHODS: In this work, sinogram datasets acquired from CBCT and a small number of projections containing metal objects were used. The sinogram was first modified by removing metal objects and up sampling in the angular direction. Then, the modified sinogram was initialized by linear interpolation and synthesized by a modified neural network model based on a U-Net structure. To obtain the reconstructed images, the synthesized sinogram was reconstructed using the traditional filtered backprojection (FBP) approach. The remaining residual artifacts on the images were further handled by another neural network model, ResU-Net. The corresponding denoised image was combined with the extracted metal objects in the same data positions to produce the final results. RESULTS: The image quality of the reconstructed images from the proposed method was improved better than the images from the conventional FBP, iterative reconstruction (IR), sinogram with linear interpolation, denoise with ResU-Net, sinogram with U-Net. The proposed method yielded 3.6 times higher contrast-to-noise ratio, 1.3 times higher peak signal-to-noise ratio, and 1.4 times higher structural similarity index (SSIM) than the traditional technique. Soft tissues around the marker on the images showed good improvement, and the mainly severe artifacts on the images were significantly reduced and regulated by the proposed. CONCLUSIONS: Our proposed method performs well reducing streak and metal artifacts in the CBCT reconstructed images, thus improving the overall breast specimen images. This would be beneficial for clinical use.

Severe metallosis following catastrophic failure of total shoulder arthroplasty - a case report.

Metallosis is an unusual but consequential complication arising from orthopedic hardware implantation, characterized by the deposition of metallic particles in the periprosthetic soft tissues. The incidence of metallosis associated with shoulder arthroplasties is exceptionally rare since the shoulder is not a weight-bearing joint, making it less susceptible to mechanical wear and, consequently, to conditions like particle disease and metallosis. Nevertheless, anomalous metal-on-metal interactions can develop in total shoulder arthroplasties if the polyethylene component fails due to wear, fracture, or dissociation. If left unaddressed, metallosis can incite an adverse immune-mediated local tissue response, culminating in joint destruction and adjacent soft tissues and muscle necrosis. In this case report, the diagnosis of metallosis was made in a patient with an anatomic total shoulder arthroplasty using a state-of-the-art photon counting detector CT supplemented by post-processing metal artifact reduction algorithms. This advanced imaging approach was effective in discerning the source of implant failure and in identifying manifestations of severe metallosis including osteolysis and pseudotumor formation. Advanced imaging methods can accurately characterize the severity and extent of metallosis, thereby helping guide surgical planning to mitigate serious complications associated with this condition.

Optimization of photon counting CT for cardiac imaging in patients with left ventricular assist devices: An in-depth assessment of metal artifacts.

PURPOSE: Photon counting CT (PCCT) holds promise for mitigating metal artifacts and can produce virtual mono-energetic images (VMI), while maintaining temporal resolution, making it a valuable tool for characterizing the heart. This study aimed to evaluate and optimize PCCT for cardiac imaging in patients during left ventricular assistance device (LVAD) therapy by conducting an in-depth objective assessment of metal artifacts and visual grading. METHODS: Various scan and reconstruction settings were tested on a phantom and further evaluated on a patient acquisition to identify the optimal protocol settings. The phantom comprised an empty thoracic cavity, supplemented with heart and lungs from a cadaveric lamb. The heart was implanted with an LVAD (HeartMate 3) and iodine contrast. Scans were performed on a PCCT (NAEOTOM Alpha, Siemens Healthcare). Metal artifacts were assessed by three objective methods: Hounsfield units (HU)/SD measurements (DiffHU and SDARTIFACT), Fourier analysis (AmplitudeLowFreq), and depicted LVAD volume in the images (BloomVol). Radiologists graded metal artifacts and the diagnostic interpretability in the LVAD lumen, cardiac tissue, lung tissue, and spinal cord using a 5-point rating scale. Regression and correlation analysis were conducted to determine the assessment method most closely associated with acquisition and reconstruction parameters, as well as the objective method demonstrating the highest correlation with visual grading. RESULTS: Due to blooming artifacts, the LVAD volume fluctuated between 27.0 and 92.7 cm3. This variance was primarily influenced by kVp, kernel, keV, and iMAR (R2 = 0.989). Radiologists favored pacemaker iMAR, 3 mm slice thickness, and T3D keV and kernel Bv56f for minimal metal artifacts in cardiac tissue assessment, and 110 keV and Qr40f for lung tissue interpretation. The model adequacy for DiffHU SDARTIFACT, AmplitueLowFreq, and BloomVol was 0.28, 0.76, 0.29, and 0.99 respectively for phantom data, and 0.95, 0.98, 1.00, and 0.99 for in-vivo data. For in-vivo data, the correlation between visual grading (VGSUM) and DiffHU SDARTIFACT, AmplitueLowFreq, and BloomVol was -0.16, -0.01, -0.48, and -0.40 respectively. CONCLUSION: We found that optimal scan settings for LVAD imaging involved using 120 kVp and IQ level 80. Employing T3D with pacemaker iMAR, the sharpest allowed vascular kernel (Bv56f), and VMI at 110 keV with kernel Qr40 yields images suitable for cardiac imaging during LVAD-therapy. Volumetric measurements of the LVAD for determination of the extent of blooming artifacts was shown to be the best objective method to assess metal artifacts.

Evaluation of a Metal Artifact Reduction Algorithm for Image Reconstruction on a Novel CBCT Platform.

PURPOSE: The presence of metal implants can produce artifacts and distort Hounsfield units (HU) in patient computed tomography (CT) images. The purpose of this work was to characterize a novel metal artifact reduction (MAR) algorithm for reconstruction of CBCT images obtained by the HyperSight imaging system. METHODS: Three tissue-equivalent phantoms were fitted with materials commonly used in medical applications. The first consisted of a variety of metal samples centered within a solid water block, the second was an Advanced Electron Density phantom with metal rods, and the third consisted of hip prostheses positioned within a water tank. CBCT images of all phantoms were acquired and reconstructed using the MAR and iCBCT Acuros algorithms on the HyperSight system. The signal-to-noise ratio (SNR), artifact index (AI), structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and mean-square error (MSE) were computed to assess the image quality in comparison to artifact-free reference images. The mean HU at various VOI positions around the cavity was calculated to evaluate the artifact dependence on distance and angle from the center of the cavity. The artifact volume of the phantom (excluding the cavity) was estimated by summing the volume of all voxels with HU values outside the 5th and 95th percentiles of the phantom CBCT with no artifact. RESULTS: The SNR, AI, SSIM, PSNR, and MSE metrics demonstrated significantly higher similarity to baseline when using MAR compared to iCBCT Acuros for all high-density materials, except for aluminum. Mean HU returned to expected solid water background at a shorter distance from metal sample in the MAR images, and the standard deviation remained lower for the MAR images at all distances from the insert. The artifact volume decreased using the novel MAR algorithm for all metal samples excluding aluminum (p < 0.001) and all hip prostheses (p < 0.05). CONCLUSION: Varian's HyperSight MAR reconstruction algorithm shows a reduction in metal artifact metrics, motivating the use of MAR reconstruction for patients with metal implants.

Both angled bony-increased offset and metal-augmented baseplates provide satisfactory bone incorporation to the glenoid in reverse total shoulder arthroplasty: a radiographic evaluation using tomosynthesis.

BACKGROUND: Angled bony-increased offset and metal-augmented baseplate have recently been used to achieve neutral to inferior inclination of the glenoid implant. Nonetheless, bone incorporation is difficult to evaluate using computed tomography or other conventional methods owing to the presence of metal artifacts; therefore, whether bone incorporation between the grafted bone and glenoid or between the graft and baseplate implant can be achieved remains unclear. Several studies have reported the effectiveness of tomosynthesis in reducing metal artifacts for the evaluation of implant loosening, bone resorption, and spot welds. We aimed to evaluate and compare the bone incorporation rates between angled bony-increased offset and metal-augmented implants using tomosynthesis with metal artifact reduction technology. We hypothesized that a high bone incorporation rate would be obtained with angled bony-increased offset and a metal-augmented baseplate. METHODS: A total of 52 patients who underwent reverse total shoulder arthroplasty (TSA) with angled bony-increased offset and 42 patients who underwent reverse total shoulder arthroplasty with metal-augmented baseplate were assessed and followed up for a minimum of 2 years. The bone incorporation and implant loosening rates were compared between the 2 groups, and the sites of spot welds and trabeculation were recorded according to zones. Bone incorporation between the bone and prosthesis was defined as a confirmation of spot welds connecting the porous area and bone in more than three zones. Bone incorporation between the native bone and grafted bone was defined as an observation of trabeculation. Glenoid loosening was defined as the presence of at least 1 mm radiolucency around the prosthesis in more than 2 zones. RESULTS: Both the angled bony-increased offset and metal-augmented baseplate groups achieved sufficient bone incorporation rates (98% [51/52 cases] vs. 100% [42/42 cases], P = 1.0) and low implant loosening rates (2% [1/52 cases] vs. 0% [0/42 cases], P = 1.0). Spot welds and trabeculation were likely to be confirmed in the lower parts of the glenoid. CONCLUSION: The two groups did not show any significant differences regarding bone incorporation rates. Considering the complexity of performing the procedure with angled bony-increased offset, the use of a metal-augmented baseplate can serve as an alternative treatment to avoid superior inclination in reverse total shoulder arthroplasty.

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.

Semi-supervised segmentation of metal-artifact contaminated industrial CT images using improved CycleGAN.

Accurate segmentation of industrial CT images is of great significance in industrial fields such as quality inspection and defect analysis. However, reconstruction of industrial CT images often suffers from typical metal artifacts caused by factors like beam hardening, scattering, statistical noise, and partial volume effects. Traditional segmentation methods are difficult to achieve precise segmentation of CT images mainly due to the presence of these metal artifacts. Furthermore, acquiring paired CT image data required by fully supervised networks proves to be extremely challenging. To address these issues, this paper introduces an improved CycleGAN approach for achieving semi-supervised segmentation of industrial CT images. This method not only eliminates the need for removing metal artifacts and noise, but also enables the direct conversion of metal artifact-contaminated images into segmented images without the requirement of paired data. The average values of quantitative assessment of image segmentation performance can reach 0.96645 for Dice Similarity Coefficient(Dice) and 0.93718 for Intersection over Union(IoU). In comparison to traditional segmentation methods, it presents significant improvements in both quantitative metrics and visual quality, provides valuable insights for further research.

Performance of different cone-beam computed tomography scan modes with and without metal artifact reduction in detection of recurrent dental caries under various restorative materials.

Purpose: We aimed to compare the diagnostic performance of different cone-beam computed tomography (CBCT) scan modes with and without the application of a metal artifact reduction (MAR) option under 5 different restorative materials. Material and methods: Our research was an in vitro study with 150 caries-free premolars and molars. The teeth were randomly divided into experimental (with artificially induced caries, n = 75) and control (without caries, n = 75) groups and were prepared based on 5 types of restorative materials, including conventional composites (Filtek Z250, Gradia), flow composite, glass ionomer, and amalgam. The teeth were examined under 2 CBCT scan modes (high-resolution [HIRes] and standard) with and without MAR application. Finally, the diagnostic accuracy index values (area under the receiver operating characteristic curve [AUC], sensitivity, and specificity) were calculated. Results: The AUC of standard scan mode with the MAR option was significantly lower than that of HIRes with MAR (p = 0.018) and without MAR option (p = 0.011) in detecting recurrent caries. Also, without MAR option, the diagnostic accuracy (AUC) of the standard mode was significantly lower than that of the HIRes (p = 0.020). Similar findings were observed for sensitivity and specificity. Moreover, diagnostic performance of standard and HIRes scan modes with and without MAR in the amalgam group was lower than that in other restorative material groups. Conclusions: Diagnostic performance of HIRes CBCT mode was higher than that of standard mode for recurrent caries and remained unaffected by MAR application. However, the accuracy in detecting recurrent caries was lower in the amalgam group compared with other restorative material groups.

Metal artifacts and artifact reduction of neurovascular coils in photon-counting detector CT versus energy-integrating detector CT - in vitro comparison of a standard brain imaging protocol.

OBJECTIVES: Photon-counting detector computed tomography (PCD-CT) is a promising new technique for CT imaging. The aim of the present study was the in vitro comparison of coil-related artifacts in PCD-CT and conventional energy-integrating detector CT (EID-CT) using a comparable standard brain imaging protocol before and after metal artifact reduction (MAR). METHODS: A nidus-shaped rubber latex, resembling an aneurysm of the cerebral arteries, was filled with neurovascular platinum coils and inserted into a brain imaging phantom. Image acquisition and reconstruction were repeatedly performed for PCD-CT and EID-CT (n = 10, respectively) using a standard brain imaging protocol. Moreover, linear interpolation MAR was performed for PCD-CT and EID-CT images. The degree of artifacts was analyzed quantitatively (standard deviation in a donut-shaped region of interest) and qualitatively (5-point scale analysis). RESULTS: Quantitative and qualitative analysis demonstrated a lower degree of metal artifacts in the EID-CT images compared to the total-energy PCD-CT images (e.g., 82.99 ± 7.89 Hounsfield units (HU) versus 90.35 ± 6.28 HU; p < 0.001) with no qualitative difference between the high-energy bin PCD-CT images and the EID-CT images (4.18 ± 0.37 and 3.70 ± 0.64; p = 0.575). After MAR, artifacts were more profoundly reduced in the PCD-CT images compared to the EID-CT images in both analyses (e.g., 2.35 ± 0.43 and 3.18 ± 0.34; p < 0.001). CONCLUSION: PCD-CT in combination with MAR have the potential to provide an improved option for reduction of coil-related artifacts in cerebral imaging in this in vitro study. KEY POINTS: • Photon-counting detector CT produces more artifacts compared to energy-integrating detector CT without metal artifact reduction in cerebral in vitro imaging after neurovascular coil-embolization. • Spectral information of PCD-CT provides the potential for new post-processing techniques, since the coil-related artifacts were lower in PCD-CT images compared to EID-CT images after linear interpolation metal artifact reduction in this in vitro study.

Compressed Sensing SEMAC MRI of Hip, Knee, and Ankle Arthroplasty Implants: A 1.5-T and 3-T Intrapatient Performance Comparison for Diagnosing Periprosthetic Abnormalities.

BACKGROUND. The utility of 3-T MRI for diagnosing joint disorders is established, but its performance for diagnosing abnormalities around arthroplasty implants is unclear. OBJECTIVE. The purpose of this study was to compare 1.5-T and 3-T compressed sensing slice encoding for metal artifact correction (SEMAC) MRI for diagnosing peri-prosthetic abnormalities around hip, knee, and ankle arthroplasty implants. METHODS. Forty-five participants (26 women, 19 men; mean age ± SD, 71 ± 14 years) with symptomatic lower extremity arthroplasty (hip, knee, and ankle, 15 each) prospectively underwent consecutive 1.5- and 3-T MRI examinations with intermediate-weighted (IW) and STIR compressed sensing SEMAC sequences. Using a Likert scale, three radiologists evaluated the presence or absence of periprosthetic abnormalities, including bone marrow edema-like signal, osteolysis, stress reaction/fracture, synovitis, and tendon abnormalities and collections; image quality; and visibility of anatomic structures. Statistical analysis included nonparametric comparison and interchangeability testing. RESULTS. For diagnosing periprosthetic abnormalities, 1.5-T and 3-T compressed sensing SEMAC MRI were interchangeable. Across all three joints, 3-T MRI had lower noise than 1.5-T MRI (median IW and STIR scores at 3 T vs 1.5 T, 4 and 4 [range, 2-5 and 3-5] vs 3 and 3 [range, 2-5 and 2-4]; p < .01 for both), sharper edges (median IW and STIR scores at 3 T vs 1.5 T, 4 and 4 [both ranges, 2-5] vs 3 and 3 [range, 2-4 and 2-5]; p < .02 and p < .05), and more effective metal artifact reduction (median IW and STIR scores at 3 T vs 1.5 T, 4 and 4 [range, 3-5 and 2-5] vs 4 and 4 [both ranges, 3-5]; p < .02 and p = .72). Agreement was moderate to substantial for image contrast (IW and STIR, 0.66 and 0.54 [95% CI, 0.41-0.91 and 0.29-0.80]; p = .58 and p = .16) and joint capsule visualization (IW and STIR, 0.57 and 0.70 [range, 0.32-0.81 and 0.51-0.89]; p = .16 and p = .19). The bone-implant interface was more visible at 1.5 T (median IW and STIR scores, 4 and 4 [both ranges, 2-5] at 1.5 T vs 3 and 3 [both ranges, 2-5] at 3 T; p = .08 and p = .58), but periprosthetic tissues had superior visibility at 3 T (IW and STIR, 4 and 4 [both ranges, 3-5] at 3 T vs 4 and 4 [ranges, 2-5 and 3-5] at 1.5 T; p = .07 and p = .19). CONCLUSION. Optimized 1.5-T and 3-T compressed sensing SEMAC MRI are interchangeable for diagnosing periprosthetic abnormalities, although metallic artifacts are larger at 3 T. CLINICAL IMPACT. With compressed sensing SEMAC MRI, lower extremity arthroplasty implants can be imaged at 3 T rather than 1.5 T.

Semi-automatic artifact quantification in thermal ablation probe and algorithms for the evaluation of metal artifact reduction.

OBJECTIVES: To compare metal artifacts and evaluation of metal artifact reduction algorithms during probe positioning in computed tomography (CT)-guided microwave ablation (MWA), cryoablation (CRYO), and radiofrequency ablation (RFA). MATERIALS AND METHODS: Using CT guidance, individual MWA, CRYO, and RFA ablation probes were placed into the livers of 15 pigs. CT imaging was then performed to determine the probe's position within the test subject's liver. Filtered back projection (B30f) and iterative reconstructions (I30-1) were both used with and without dedicated iterative metal artifact reduction (iMAR) to generate images from the initial data sets. Semi-automatic segmentation-based quantitative evaluation was conducted to estimate artifact percentage within the liver, while qualitative evaluation of metal artifact extent and overall image quality was performed by two observers using a 5-point Likert scale: 1-none, 2-mild, 3-moderate, 4-severe, 5-non-diagnostic. RESULTS: Among MWA, RFA, and CRYO, compared with non-iMAR in B30f reconstruction, the largest extent of artifact volume percentages were observed for CRYO (11.5-17.9%), followed by MWA (4.7-6.6%) and lastly in RFA (5.5-6.2%). iMAR significantly reduces metal artifacts for CRYO and MWA quantitatively (p = 0.0020; p = 0.0036, respectively) and qualitatively (p = 0.0001, p = 0.0005), but not for RFA. No significant reduction in metal artifact percentage was seen after applying iterative reconstructions (p > 0.05). Noise, contrast-to-noise-ratio, or overall image quality did not differ between probe types, irrespective of the application of iterative reconstruction and iMAR. CONCLUSION: A dedicated metal artifact algorithm may decrease metal artifacts and improves image quality significantly for MWA and CRYO probes. Their application alongside with dedicated metal artifact algorithm should be considered during CT-guided positioning.

State-of-the-art magnetic resonance imaging sequences for pediatric body imaging.

Longer examination time, need for anesthesia in smaller children and the inability of most children to hold their breath are major limitations of MRI in pediatric body imaging. Fortunately, with technical advances, many new and upcoming MRI sequences are overcoming these limitations. Advances in data acquisition and k-space sampling methods have enabled sequences with improved temporal and spatial resolution, and minimal artifacts. Sequences to minimize movement artifacts mainly utilize radial k-space filling, and examples include the stack-of-stars method for T1-weighted imaging and the periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER)/BLADE method for T2-weighted imaging. Similarly, the sequences with improved temporal resolution and the ability to obtain multiple phases in a single breath-hold in dynamic imaging mainly use some form of partial k-space filling method. New sequences use a variable combination of data sampling methods like compressed sensing, golden-angle radial k-space filling, parallel imaging and partial k-space filling to achieve free-breathing, faster sequences that could be useful for pediatric abdominal and thoracic imaging. Simultaneous multi-slice method has improved diffusion-weighted imaging (DWI) with reduction in scan time and artifacts. In this review, we provide an overview of data sampling methods like parallel imaging, compressed sensing, radial k-space sampling, partial k-space sampling and simultaneous multi-slice. This is followed by newer available and upcoming sequences for T1-, T2- and DWI based on these other advances. We also discuss the Dixon method and newer approaches to reducing metal artifacts.

Significantly Worse Fixation of Cemented Patellar Components on Multiacquisition Variable-Resonance Image Combination Magnetic Resonance Imaging Compared to Femoral and Tibial Components: A Cause for Concern?

BACKGROUND: The etiology of anterior knee pain after total knee arthroplasty (TKA) remains unclear. Few studies have examined patellar fixation quality. The purpose of the present study was to evaluate the patellar cement-bone interface after TKA on magnetic resonance imaging (MRI) and to correlate the patella fixation grade with the incidence of anterior knee pain. METHODS: We retrospectively reviewed 279 knees undergoing metal artifact reduction MRI for either anterior or generalized knee pain at least 6 months after cemented, posterior-stabilized TKA with patellar resurfacing with one implant manufacturer. MRI cement-bone interfaces and percent-integration of the patella, femur, and tibia were assessed by a fellowship-trained senior musculoskeletal radiologist. The grade and character of the patella interface were compared to the femur and tibia. Regression analyses were used to determine the association between patella integration with anterior knee pain. RESULTS: There were more patellar components with ≥75% zones of fibrous tissue (50%) compared to the femur (18%) or tibia (5%) (P < .001). There were a greater number of patellar implants with poor cement integration (18%) compared to the femur (1%) or tibia (1%) (P < .001). MRI findings showed more evidence of patellar component loosening (8%) compared to the femur (1%) or tibia (1%) (P < .001). Anterior knee pain was correlated with worse patella cement integration (P = .01), with women predicted to have better integration (P < .001). CONCLUSION: The quality of the patellar cement-bone interface after TKA is worse compared to the femoral or tibial component interface. Poor patellar cement-bone interface may be a source of anterior knee pain after TKA, but further investigation is required.

Metal Artifact Reduction in Dental CBCT Images Using Direct Sinogram Correction Combined with Metal Path-Length Weighting.

Metal artifacts in dental computed tomography (CT) images, caused by highly X-ray absorbing objects, such as dental implants or crowns, often more severely compromise image readability than in medical CT images. Since lower tube voltages are used for dental CTs in spite of the more frequent presence of metallic objects in the patient, metal artifacts appear more severely in dental CT images, and the artifacts often persist even after metal artifact correction. The direct sinogram correction (DSC) method, which directly corrects the sinogram using the mapping function derived by minimizing the sinogram inconsistency, works well in the case of mild metal artifacts, but it often fails to correct severe metal artifacts. We propose a modified DSC method to reduce severe metal artifacts, and we have tested it on human dental images. We first segment the metallic objects in the CT image, and then we forward-project the segmented metal mask to identify the metal traces in the projection data with computing the metal path length for the rays penetrating the metal mask. In the sinogram correction with the DSC mapping function, we apply the weighting proportional to the metal path length. We have applied the proposed method to the phantom and patient images taken at the X-ray tube voltage of 90 kVp. We observed that the proposed method outperforms the original DSC method when metal artifacts were severe. However, we need further extensive studies to verify the proposed method for various CT scan conditions with many more patient images.

The anatomical location of cystic pseudotumors and muscle atrophy in metal-on-metal resurfacing hip arthroplasty is related to the surgical approach used for implantation. A subgroup analyses of a randomized controlled trial.

BACKGROUND: Pseudotumors and muscle atrophy have been associated with metal-on-metal (MoM) resurfacing hip arthroplasty (RHA). We aimed to investigate the influence of the anterolateral (AntLat) and the posterior (Post) surgical approach on the location, grade and prevalence of pseudotumors and muscle atrophy in MoM RHA. PATIENTS AND METHODS: Forty-nine patients were randomized to MoM RHA by the AntLat (n = 25) or the Post (n = 24) approach at Aarhus University Hospital. Patients underwent metal artifact reduction sequence (MARS) magnetic resonance imaging (MRI) scans for investigation of location, grade and prevalence of pseudotumors and muscle atrophy. Plain radiographs, metal-ions concentrations and clinical outcome scores were evaluated to compare outcomes of the surgical approaches. RESULTS: MRI-detected pseudotumors were seen in 7 of 18 patients (39%) in the AntLat group and in 12 of 22 patients (55%) in the Post group (p = 0.33). Pseudotumors were mainly located anterolaterally to the hip joint in the AntLat group and postero-lateral to the hip joint in the Post group. Higher grades of muscle atrophy of the caudal part of the gluteus medius and minimus (p < 0.004) were seen in the AntLat group, and higher grades of muscle atrophy of the small external rotators were seen in the Post group (p < 0.001). The AntLat group had higher anteversion angles of mean 15.3° (range 6.1-7.5) versus mean 11.5° (range 4.9-22.5) in the Post group (p = 0.02). Metal-ion concentrations and clinical outcome scores were similar between groups (p > 0.08). CONCLUSION: Muscle atrophy and pseudotumor location after MoM RHA follow the surgical approach used for implantation. This knowledge may help differentiate between "normal postoperative appearance" and "MoM disease."

MRI-guided attenuation correction in torso PET/MRI: Assessment of segmentation-, atlas-, and deep learning-based approaches in the presence of outliers.

PURPOSE: We compare the performance of three commonly used MRI-guided attenuation correction approaches in torso PET/MRI, namely segmentation-, atlas-, and deep learning-based algorithms. METHODS: Twenty-five co-registered torso 18 F-FDG PET/CT and PET/MR images were enrolled. PET attenuation maps were generated from in-phase Dixon MRI using a three-tissue class segmentation-based approach (soft-tissue, lung, and background air), voxel-wise weighting atlas-based approach, and a residual convolutional neural network. The bias in standardized uptake value (SUV) was calculated for each approach considering CT-based attenuation corrected PET images as reference. In addition to the overall performance assessment of these approaches, the primary focus of this work was on recognizing the origins of potential outliers, notably body truncation, metal-artifacts, abnormal anatomy, and small malignant lesions in the lungs. RESULTS: The deep learning approach outperformed both atlas- and segmentation-based methods resulting in less than 4% SUV bias across 25 patients compared to the segmentation-based method with up to 20% SUV bias in bony structures and the atlas-based method with 9% bias in the lung. The deep learning-based method exhibited superior performance. Yet, in case of sever truncation and metallic-artifacts in the input MRI, this approach was outperformed by the atlas-based method, exhibiting suboptimal performance in the affected regions. Conversely, for abnormal anatomies, such as a patient presenting with one lung or small malignant lesion in the lung, the deep learning algorithm exhibited promising performance compared to other methods. CONCLUSION: The deep learning-based method provides promising outcome for synthetic CT generation from MRI. However, metal-artifact and body truncation should be specifically addressed.

Combination of Iterative Metal Artifact Reduction and Virtual Monoenergetic Reconstruction Using Split-Filter Dual-Energy CT in Patients With Dental Artifact on Head and Neck CT.

BACKGROUND. Head and neck CT can be limited by dental hardware artifact. Both postprocessing-based iterative metal artifact reduction (IMAR) and virtual monoenergetic imaging (VMI) reconstruction in dual-energy CT (DECT) can reduce metal artifact. Their combination is poorly described for single-source DECT systems. OBJECTIVE. The purpose of this study was to compare metal artifact reduction between VMI, IMAR, and their combination (VMIIMAR) in split-filter single-source DECT of patients with severe dental hardware artifact. METHODS. This retrospective study included 44 patients (nine woman, 35 men; mean age, 66.0 ± 10.4 years) who underwent head and neck CT and had severe dental hardware artifact. Standard, VMI, IMAR, and VMIIMAR images were generated; VMI and VMIIMAR were performed at 40, 70, 100, 120, 150, and 190 keV. ROIs were placed to measure corrected attenuation in pronounced hyperattenuating and hypoattenuating artifacts and artifact-impaired soft tissue and to measure corrected artifact-impaired soft-tissue noise. Two radiologists independently assessed soft-tissue interpretability (1-5 scale), and pooled ratings were analyzed. Readers selected the preferred reconstruction for each patient. RESULTS. Mean hyperattenuating artifact-corrected attenuation was 521.0 HU for standard imaging, 496.4-892.2 HU for VMI, 48.2 HU for IMAR, and 32.8-91.0 HU for VMIIMAR. Mean hypoattenuating artifact-corrected attenuation was -455.1 HU for standard imaging, -408.5 to -679.9 HU for VMI, -37.3 for IMAR, and -17.8 to -36.9 HU for VMIIMAR. Mean artifact-impaired soft tissue-corrected attenuation was 10.8 HU for standard imaging, -0.6 to 24.9 HU for VMI, 4.3 HU for IMAR, and -2.0 to 7.8 HU for VMIIMAR. Mean artifact-impaired soft tissue-corrected noise was 58.7 HU for standard imaging, 38.2 to 129.7 HU for VMI, 11.0 HU for IMAR, and 5.8 to 45.6 HU for VMIIMAR. Median soft-tissue interpretability was 1.2 for standard imaging, 1.1-1.2 for VMI, 3.7 for IMAR, and 2.0-3.8 for VMIIMAR. Artifact-impaired soft tissue-corrected attenuation and soft-tissue interpretability significantly improved (p < .05) for VMIIMAR versus IMAR only at 100 keV. The two readers preferred VMIIMAR at 100 keV in 56.8% and 59.1% of examinations. CONCLUSION. For reducing severe artifact due to dental material, IMAR has greater effect than VMI. Though the results for IMAR and VMIIMAR were similar overall, VMIIMAR had a small benefit at 100 keV. CLINICAL IMPACT. VMI and IMAR techniques in split-filter DECT may be combined for clinical head and neck imaging to reduce artifact from dental hardware and improve image quality.

Truncation effect reduction for fast iterative reconstruction in cone-beam CT.

BACKGROUND: Iterative reconstruction for cone-beam computed tomography (CBCT) has been applied to improve image quality and reduce radiation dose. In a case where an object's actual projection is larger than a flat panel detector, CBCT images contain truncated data or incomplete projections, which degrade image quality inside the field of view (FOV). In this work, we propose truncation effect reduction for fast iterative reconstruction in CBCT imaging. METHODS: The volume matrix size of the FOV and the height of projection images were extrapolated to a suitable size. These extended projections were reconstructed by fast iterative reconstruction. Moreover, a smoothing parameter for noise regularization in iterative reconstruction was modified to reduce the accumulated error while processing. The proposed work was evaluated by image quality measurements and compared with conventional filtered backprojection (FBP). To validate the proposed method, we used a head phantom for evaluation and preliminarily tested on a human dataset. RESULTS: In the experimental results, the reconstructed images from the head phantom showed enhanced image quality. In addition, fast iterative reconstruction can be run continuously while maintaining a consistent mean-percentage-error value for many iterations. The contrast-to-noise ratio of the soft-tissue images was improved. Visualization of low contrast in the ventricle and soft-tissue images was much improved compared to those from FBP using the same dose index of 5 mGy. CONCLUSIONS: Our proposed method showed satisfactory performance to reduce the truncation effect, especially inside the FOV with better image quality for soft-tissue imaging. The convergence of fast iterative reconstruction tends to be stable for many iterations.

Impact of metal artifact reduction algorithm on gross tumor volume delineation in tonsillar cancer: reducing the interobserver variation.

BACKGROUND: Patients with tonsillar cancer (TC) often have dental fillings that can significantly degrade the quality of computed tomography (CT) simulator images due to metal artifacts. We evaluated whether the use of the metal artifact reduction (MAR) algorithm reduced the interobserver variation in delineating gross tumor volume (GTV) of TC. METHODS: Eighteen patients with TC with dental fillings were enrolled in this study. Contrast-enhanced CT simulator images were reconstructed using the conventional (CTCONV) and MAR algorithm (CTMAR). Four board-certified radiation oncologists delineated the GTV of primary tumors using routine clinical data first on CTCONV image datasets (GTVCONV), followed by CTCONV and CTMAR fused image datasets (GTVMAR) at least 2 weeks apart. Intermodality differences in GTV values and Dice similarity coefficient (DSC) were compared using Wilcoxon's signed-rank test. RESULTS: GTVMAR was significantly smaller than GTVCONV for three observers. The other observer showed no significant difference between GTVCONV and GTVMAR values. For all four observers, the mean GTVCONV and GTVMAR values were 14.0 (standard deviation [SD]: 7.4) cm3 and 12.1 (SD: 6.4) cm3, respectively, with the latter significantly lower than the former (p < 0.001). The mean DSC of GTVCONV and GTVMAR was 0.74 (SD: 0.10) and 0.77 (SD: 0.10), respectively, with the latter significantly higher than that of the former (p < 0.001). CONCLUSIONS: The use of the MAR algorithm led to the delineation of smaller GTVs and reduced interobserver variations in delineating GTV of the primary tumors in patients with TC.