Presence Of Metallic Implants Research Articles

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

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. 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. 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). Varian's HyperSight MAR reconstruction algorithm shows a reduction in metal artifact metrics, motivating the use of MAR reconstruction for patients with metal implants.

Quantitative and qualitative performance evaluation of commercial metal artifact reduction methods: Dosimetric effects on the treatment planning

The presence of metal implants within CT imaging causes severe attenuation of the X-ray beam. Due to the incomplete information recorded by CT detectors, artifacts in the form of streaks and dark bands would appear in the resulting CT images. The metal-induced artifacts would firstly affect the quantitative accuracy of CT imaging, and consequently, the radiation treatment planning and dose estimation in radiation therapy. To address this issue, CT scanner vendors have implemented metal artifact reduction (MAR) algorithms to avoid such artifacts and enhance the overall quality of CT images. The orthopedic-MAR (OMAR) and normalized MAR (NMAR) algorithms are the most well-known metal artifact reduction (MAR) algorithms, used worldwide. These algorithms have been implemented on Philips and Siemens scanners, respectively. In this study, we set out to quantitatively and qualitatively evaluate the effectiveness of these two MAR algorithms and their impact on accurate radiation treatment planning and CT-based dosimetry. The quantitative metrics measured on the simulated metal artifact dataset demonstrated superior performance of the OMAR technique over the NMAR one in metal artifact reduction. The analysis of radiation treatment planning using the OMAR and NMAR techniques in the corrected CT images showed that the OMAR technique reduced the toxicity of healthy tissues by 10% compared to the uncorrected CT images.

Characterization of spatial integrity with active and passive implants in a low-field magnetic resonance linear accelerator scanner

Background and PurposeStandard imaging protocols can guarantee the spatial integrity of magnetic resonance (MR) images utilized in radiotherapy. However, the presence of metallic implants can significantly compromise this integrity. Our proposed method aims at characterizing the geometric distortions induced by both passive and active implants commonly encountered in planning images obtained from a low-field 0.35 T MR-linear accelerator (LINAC). Materials and MethodsWe designed a spatial integrity phantom defining 1276 control points and covering a field of view of 20x20x20 cm3. This phantom was scanned in a water tank with and without different implants used in hip and shoulder arthroplasty procedures as well as with active cardiac stimulators. The images were acquired with the clinical planning sequence (balanced steady-state free-precession, resolution 1.5x1.5x1.5 mm3). Spatial integrity was assessed by the Euclidian distance between the control point detected on the image and their theoretical locations. A first plane free of artefact (FPFA) was defined to evaluate the spatial integrity beyond the larger banding artefact. ResultsIn the region extending up to 20 mm from the largest banding artefacts, the tested passive and active implants could cause distortions up to 2 mm and 3 mm, respectively. Beyond this region the spatial integrity was recovered and the image could be considered as unaffected by the implants. ConclusionsWe characterized the impact of common implants on a low field MR-LINAC planning sequence. These measurements could support the creation of extra margin while contouring organs at risk and target volumes in the vicinity of implants.

Mud-Net: multi-domain deep unrolling network for simultaneous sparse-view and metal artifact reduction in computed tomography

Sparse-view computed tomography (SVCT) is regarded as a promising technique to accelerate data acquisition and reduce radiation dose. However, in the presence of metallic implants, SVCT inevitably makes the reconstructed CT images suffer from severe metal artifacts and streaking artifacts due to the lack of sufficient projection data. Previous stand-alone SVCT and metal artifact reduction (MAR) methods to solve the problem of simultaneously sparse-view and metal artifact reduction (SVMAR) are plagued by insufficient correction accuracy. To overcome this limitation, we propose a multi-domain deep unrolling network, called Mud-Net, for SVMAR. Specifically, we establish a joint sinogram, image, artifact, and coding domains deep unrolling reconstruction model to recover high-quality CT images from the under-sampled sinograms corrupted by metallic implants. To train this multi-domain network effectively, we embed multi-domain knowledge into the network training process. Comprehensive experiments demonstrate that our method is superior to both existing MAR methods in the full-view MAR task and previous SVCT methods in the SVMAR task.

Feasibility study on the clinical application of CT-based synthetic brain T1-weighted MRI: comparison with conventional T1-weighted MRI.

This study aimed to examine the equivalence of computed tomography (CT)-based synthetic T1-weighted imaging (T1WI) to conventional T1WI for the quantitative assessment of brain morphology. This prospective study examined 35 adult patients undergoing brain magnetic resonance imaging (MRI) and CT scans. An image synthesis method based on a deep learning model was used to generate synthetic T1WI (sT1WI) from CT data. Two senior radiologists used sT1WI and conventional T1WI on separate occasions to independently measure clinically relevant brain morphological parameters. The reliability and consistency between conventional and synthetic T1WI were assessed using statistical consistency checks, comprising intra-reader, inter-reader, and inter-method agreement. The intra-reader, inter-reader, and inter-method reliability and variability mostly exhibited the desired performance, except for several poor agreements due to measurement differences between the radiologists. All the measurements of sT1WI were equivalent to that of T1WI at 5% equivalent intervals. This study demonstrated the equivalence of CT-based sT1WI to conventional T1WI for quantitatively assessing brain morphology, thereby providing more information on imaging diagnosis with a single CT scan. Real-time synthesis of MR images from CT scans reduces the time required to acquire MR signals, improving the efficiency of the treatment planning system and providing benefits in the clinical diagnosis of patients with contraindications such as presence of metal implants or claustrophobia. • Deep learning-based image synthesis methods generate synthetic T1-weighted imaging from CT scans. • The equivalence of synthetic T1-weighted imaging and conventional MRI for quantitative brain assessment was investigated. • Synthetic T1-weighted imaging can provide more information per scan and be used in preoperative diagnosis and radiotherapy.

Análise térmica da interface músculo-osso em corpos de prova após utilização de ultrassom terapêutico

Abstract Introduction Ultrasound used in diathermic therapies aims to achieve temperatures between 40 and 45 °C, since temperatures above 45 °C are known to cause tissue necrosis and burns. Many studies have been conducted to investigate the effect of therapeutic ultrasound in the presence of metallic implants, using phantoms (test samples) and in vivo and ex vivo animal models. In most of these studies, the ultrasound probe is fixed in one area, while in clinical practice, it is recommended that it be moved to avoid possible local overheating. Objective To analyze the thermal field at the muscle-bone interface in phantoms in the presence or absence of metallic implants after the application of therapeutic ultrasound. Methods Phantoms composed of layers simulating fat and muscle, and a layer of beef rib bone, with and without a titanium metallic implant, were prepared. The experiment involved different intensities (1.0, 1.5, and 2.0 W/cm2) and exposure times (5 and 10 minutes), common in clinics, with linear scanning of the probe. Results The experiments indicated that the muscle/implant interface heated less than the muscle/bone interface, especially at intensities of 1.5 and 2.0 W/cm2, after 5 and 10 minutes of treatment. Conclusion The results suggest the possibility of using therapeutic ultrasound in patients with metallic implants, encouraging future research to develop evidence-based protocols and safe recommendations in physiotherapy.

The role of advanced metal artifact reduction MRI in the diagnosis of periprosthetic joint infection.

Identification and diagnosis of periprosthetic joint infection (PJI) are challenging, requiring a multi-disciplinary approach involving clinical evaluation, laboratory tests, and imaging studies. MRI is advantageous to alternative imaging techniques due to superior soft tissue contrast and absence of ionizing radiation. However, the presence of metallic implants can cause signal loss and artifacts. Metal artifact suppression (MARS) MRI techniques have been developed that mitigate metal artifacts and improve periprosthetic soft tissue visualization. This paper provides a review of the various MARS MRI techniques, their clinical applicability and accuracy in PJI diagnosis and evaluation, and current challenges and future perspectives.

Evaluation of a Novel Metal Artifact Reduction Algorithm for Reconstruction of Cone Beam CT (CBCT) Images Acquired on a New Imaging System

The presence of metal implants, dental work or prosthetics can produce image artifacts and distort Hounsfield units (HU) in patient CBCT images. The purpose of this work was to characterize a novel metal artifact reduction (MAR) algorithm for reconstruction of CBCT images obtained by a new imaging system and compare improvements in image quality with a conventional iterative reconstruction algorithm. Preliminary analyses show that the novel reconstruction algorithm implemented on the new imaging system produces image quality and HU comparable to standard fan-beam CT MATERIALS/METHODS: A 30 cm (length) x 30 cm (width) x 11 cm (depth) solid water phantom was fitted with a central cavity measuring 5.5 cm x 5.5 cm x 0.5 cm. Metal inserts of copper, aluminum, stainless steel, and titanium (5.5 cm x 5.5 cm x 1 - 5 mm in thickness) and sample dental implant materials (Amalgam, Au, Co-Cr, and Ti 6Al-Yv) were sequentially used to fill the cavity. CBCT images were acquired and reconstructed using the novel MAR and conventional iterative algorithms both on the on the new system. Images were also taken with the cavity filled with solid water for comparison. Eight image quality metrics were measured in six volumes of interest (VOIs) surrounding the cavity and compared to baseline VOI metrics for solid water-only images: HU mean, signal-to-noise ratio, contrast-to-noise ratio, artifact index, standard deviation, range, skewness, and kurtosis. The mean and standard deviation of HU within a shell-structure surrounding the cavity were calculated to evaluate the dependence of these values on distance from the center of the cavity. The artifact volume of the phantom (excluding the cavity) was estimated by calculating the 5th and 95th percentiles of HU in the CBCT of the phantom with the solid water insert, then summing the volume of all voxels with HU values outside this range. The eight image quality metrics extracted from the chosen VOIs were closer to baseline when using the novel MAR compared to conventional iterative reconstruction. It was also determined that mean HU returned to expected solid water background at a shorter distance from metal sample in the novel MAR images, and the standard deviation remained lower for the novel MAR images at all distances from the insert. These experimental outcomes were obtained for all high-density material inserts except for aluminum. The artifact volume was decreased using the novel MAR algorithm for all metal inserts excluding aluminum (p = 0.00013), and all dental implants (p = 0.00028). The novel MAR reconstruction algorithm shows a much lower sensitivity to metal artifacts, and significant improvement in image quality for all metal samples except for aluminum. These results justify the use of the novel MAR reconstruction for patients with metal implants and highlight advantages of the new CBCT imaging system for more accurate imaging and targeted treatment.

Reliability and validity of a mobile application for femoral anteversion measurement in adult patients

BackgroundFemoral torsion is primarily measured by computed tomography (CT), which has cost and radiation exposure concerns. Recently, femoral anteversion measurement by a simple radiograph-based mobile application was developed for patients with cerebral palsy. This study aimed to validate the use of a mobile application that can reconstruct a three-dimensional model of the femur from conventional radiographs for adults.MethodsMedical records of 76 patients undergoing conventional femur anteroposterior/lateral radiography and femur CT were reviewed. To measure femoral anteversion on the reconstructed 3-dimensional images from both the mobile application and CT, we drew a line which connects the posterior margins of each femoral condyle and another line which passes through the center of the femoral head and the midpoint of the femoral neck. After the reliability test, a single examiner measured femoral anteversion on the mobile application and CT. Pearson’s correlation analysis was used to assess the correlation between anteversion on the mobile application and CT.ResultsFemoral anteversion measured on both CT and the mobile application showed excellent reliability (intraclass correlation coefficients: 0.808–0.910). The correlation coefficient between femoral anteversion measured using CT and the mobile application was 0.933 (p < 0.001). The correlation of femoral anteversion between CT and the mobile application was relatively higher in the absence of metallic implants (correlation coefficient: 0.963, p < 0.001) than in the presence of metallic implants (correlation coefficient: 0.878, p < 0.001).ConclusionsUsing two simple radiographs, the mobile application showed excellent validity and reliability for femoral anteversion measurement in adults as compared to CT. With the high accessibility and cost-effectiveness of this mobile application, femoral torsion measurement might be easily performed with simple radiography in clinical settings in the near future.

Weight-Bearing CT Hounsfield Unit Algorithm Assessment of Calcaneal Osteotomy Healing: A Prospective Study Comparing Metallic and Bio-Integrative Screws

Category: Basic Sciences/Biologics; Hindfoot; Other Introduction/Purpose: The use of bio-integrative implants in orthopedic surgery is growing rapidly. While many biomechanical and histological studies have been able to demonstrate their structural and biological properties, few clinical reports are available to support their advantages, such as good osteosynthesis, lower rates of removal, and diminished implant-related artifact in imaging studies. This clinical information is vital to providers when choosing the proper material and planning postoperative treatment. Hounsfield Units (HU) algorithms have been used as an objective assessment of joint space width. This pilot data analysis intends to test the capacity of the bio-integrative screws in reaching similar radiographical outcomes of the current metallic screws when analyzing medial displacement calcaneus osteotomies (MDCO). Our hypothesis is that both types of implants would present similar results. Methods: In this prospective comparative IRB-approved study, three patients undergoing MDCO with bio-integrative screws were compared to two patients undergoing the same surgery with metallic screws. Surgeon, primary diagnosis, technique, and displacement were the same for both groups. Patients were assessed using weight-bearing computed tomography at weeks 2, 6, and 12 postoperatively. Using a dedicated software, a 40x40x40mm cube, which defines a volume of interest (VOI), is centered at the osteotomy site. Within the VOI, initial computational analysis focused on image intensity (Hounsfield Units) profiles along lines perpendicular to the osteotomy line, crossing the osteotomy line and spanning approximately 8mm on either side. The HU intensity profiles were recorded, and graphical plots of the HU distributions were generated for each line. The plots were then used to calculate the HU contrast, a proxy for bone healing at the osteotomy site. Results: At 2 weeks, mean HU intensity in the metallic and bio-integrative were respectively 403.25 and 416.28 at the centerline (p=0.312), 513.24 and 386.57 at the inferior (p&lt;0.001), 438.97 and 487.92 at the superior line (p=0.020). With 6 weeks, a mean HU intensity of 318.40 and 414.22 was observed at the centerline (p&lt;0.001), 340.41 to 356.86 (p=0.315) at the inferior, and 401.72 and 449.88 at the superior (p=0.018). At 12 weeks, HU intensity of -85.01 and 64.59 was found at the center (p&lt;0.001), - 111.36 and 139.19 at the inferior (p&lt;0.001), and 225.95 and 166.05 at the superior line respectively (p=0.010). Overall HU units decrease from the second to the twelfth week in both groups (ps&lt;0.001). The contrast was higher in the metallic patients (0.66 to 0.26). Conclusion: Comparison among bone healing between metallic and bio-integrative screws through HU algorithms found similar results. The absence of valleys on the HU graphical plots at 2 weeks postoperatively could be a direct sign of osteotomy compression. Diffuse osteopenia might explain lower amounts at the 12-week evaluation. Maximum HU values were similar, indicating equivalent results at the osteotomy sites, a finding compatible with consolidation. Presence of metallic implants across the osteotomy site hindered both HU intensity and contrast evaluation, presenting a challenge when calculating bone healing through indirect and direct assessments.

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.

The future of PSMA PET and WB MRI as next-generation imaging tools in prostate cancer

Radiolabelled prostate-specific membrane antigen (PSMA)-based PET-CT has been shown in numerous studies to be superior to conventional imaging in the detection of nodal or distant metastatic lesions. 68Ga-PSMA PET-CT is now recommended by many guidelines for the detection of biochemically relapsed disease after radical local therapy. PSMA radioligands can also function as radiotheranostics, and Lu-PSMA has been shown to be a potential new line of treatment for metastatic castration-resistant prostate cancer. Whole-body (WB) MRI has been shown to have a high diagnostic performance in the detection and monitoring of metastatic bone disease. Prospective, randomized, multicentre studies comparing 68Ga-PSMA PET-CT and WB MRI for pelvic nodal and metastatic disease detection are yet to be performed. Challenges for interpretation of PSMA include tracer trapping in non-target tissues and also urinary excretion of tracers, which confounds image interpretation at the vesicoureteral junction. Additionally, studies have shown how long-term androgen deprivation therapy (ADT) affects PSMA expression and could, therefore, reduce tracer uptake and visibility of PSMA+ lesions. Furthermore, ADT of short duration might increase PSMA expression, leading to the PSMA flare phenomenon, which makes the accurate monitoring of treatment response to ADT with PSMA PET challenging. Scan duration, detection of incidentalomas and presence of metallic implants are some of the major challenges with WB MRI. Emerging data support the wider adoption of PSMA PET and WB MRI for diagnosis, staging, disease burden evaluation and response monitoring, although their relative roles in the standard-of-care management of patients are yet to be fully defined.

A semi-supervised learning method of latent features based on convolutional neural networks for CT metal artifact reduction.

X-ray computed tomography (CT) has become a convenient and efficient clinical medical technique. However, in the presence of metal implants, CT images may be corrupted by metal artifacts. The metal artifact reduction (MAR) methods based on deep learning are mostly supervised methods trained with labeled synthetic-artifact CT images. However, this causes the neural network to be biased toward learning specific synthetic-artifact patterns and leads to a poor generalization for unlabeled real-artifact CT images. In this study, a semi-supervised learning method of latent features based on convolutional neural networks (SLF-CNN) is developed to remove metal artifacts while ensuring a good generalization ability for real-artifact CT images. The proposed semi-supervised method extracts CT image features in alternate iterations of a synthetic-artifact learning stage and a real-artifact learning stage. In the synthetic-artifact learning stage, SLF-CNN is fed with paired synthetic-artifact CT images and is constrained using mean-squared-error (MSE) loss and perceptual loss in a supervised learning fashion. In the real-artifact learning stage, the network weight is updated by minimizing the error between the pseudo-ground truths and the predicted latent features. The feature level pseudo-ground truths are obtained by modeling latent features using the Gaussian process. The overall framework of SLF-CNN adopts an encoder-decoder structure. The encoder is composed of artifact information collection groups to map the input artifact-affected synthetic-artifact CT images and real-artifact CT images into latent features. The decoder is composed of stacked ResNeXt blocks and is responsible for decoding latent features with high-level semantic information to reconstruct artifact-free CT images. The performance of the proposed method is evaluated through contrast experiments and ablation experiments. The contrast experimental results indicate that the artifact-free CT images obtained by SLF-CNN have good metrics values, which are close to or better than those of typical supervised MAR methods. The metal artifacts in artifact-affected CT images are eliminated and the tissue structure details are preserved using SLF-CNN. The ablation experiment shows that adding real-artifact CT images greatly improves the generalization ability of the network. The proposed semi-supervised learning method of latent features for MAR effectively suppresses metal artifacts and improves the generalization ability of the network.

DuDoDR-Net: Dual-domain data consistent recurrent network for simultaneous sparse view and metal artifact reduction in computed tomography.

Sparse-view computed tomography (SVCT) aims to reconstruct a cross-sectional image using a reduced number of x-ray projections. While SVCT can efficiently reduce the radiation dose, the reconstruction suffers from severe streak artifacts, and the artifacts are further amplified with the presence of metallic implants, which could adversely impact the medical diagnosis and other downstream applications. Previous methods have extensively explored either SVCT reconstruction without metallic implants, or full-view CT metal artifact reduction (MAR). The issue of simultaneous sparse-view and metal artifact reduction (SVMAR) remains under-explored, and it is infeasible to directly apply previous SVCT and MAR methods to SVMAR which may yield non-ideal reconstruction quality. In this work, we propose a dual-domain data consistent recurrent network, called DuDoDR-Net, for SVMAR. Our DuDoDR-Net aims to reconstruct an artifact-free image by recurrent image domain and sinogram domain restorations. To ensure the metal-free part of acquired projection data is preserved, we also develop the image data consistent layer (iDCL) and sinogram data consistent layer (sDCL) that are interleaved in our recurrent framework. Our experimental results demonstrate that our DuDoDR-Net is able to produce superior artifact-reduced results while preserving the anatomical structures, that outperforming previous SVCT and SVMAR methods, under different sparse-view acquisition settings.

Dosimetric Comparison and Clinical Implications of Dose Calculation Algorithms for Stereotactic Body Radiation Therapy in Spine Metastases

Dose calculation algorithms for radiotherapy differ in their ability to handle patient-specific anatomy, tissue heterogeneity, and artificial implants, which is crucial in spine stereotactic body radiation therapy (SBRT), where a large dose gradient exists between normal organs and the tumor. We evaluate the dosimetric differences in 3 calculation algorithms: Collapsed Cone Convolution (CCC), Anisotropic Analytic Algorithm (AAA), and Acuros XB (AXB) for spine SBRT with and without metal implants. We hypothesize that clinical outcomes can be correlated with the accuracy of the algorithm and will show this through 3 clinical scenarios.We retrospectively analyzed 41 spine SBRT plans from 27 patients, 15 with metal hardware. CCC plans (17) were recalculated with AAA and AXB and AXB plans (24) were recalculated with AAA. Planning target volume (PTV) coverage and maximum dose (Dmax) for spinal cord were compared with AXB as the standard, as it is the most accurate in accounting for tissue heterogeneity of the three algorithms. Chart review was completed to determine clinical correlations.Tumors of various histologies including Lung (22.0%), breast (19.5%), head & neck (17.1%), Renal Cell (14.6%) were studied. Treatments were delivered in 1-5 fractions (median 3) with Rx dose of 16-40 Gy (median 27), corresponding to BED10 of 37.5-60 Gy (median 51.3). Overall (see Table), AAA tended to overestimate Dmax while CCC underestimated. Both algorithms overestimated the dose to 95% of PTV (D95). Those with metal near the target had a larger dose discrepancy, especially with CCC. With median follow up of 14.7 mo, poor local control was noted in 14 (34%) plans. Compared to plans with local control, these were more likely to have metal implants near the PTV (50%), with a larger degree of overestimation of PTV coverage compared to AXB, especially in plans calculated using CCC (Avg. 7.3% overestimation of D95).AAA tends to overestimate both Dmax to spinal cord and PTV coverage compared to ABX. CCC tends to underestimate Dmax to the cord while also overestimating coverage. The differences were larger in the presence of metal implants. Larger dose differences were also noted in lesions that showed poor local control, especially with CCC. The clinical use of more accurate calculation algorithms such as AXB may result in improved outcomes of spine SBRT, especially in the post-operative setting.

Metal artifact reduction in 2D CT images with self-supervised cross-domain learning

The presence of metallic implants often introduces severe metal artifacts in the x-ray computed tomography (CT) images, which could adversely influence clinical diagnosis or dose calculation in radiation therapy. In this work, we present a novel deep-learning-based approach for metal artifact reduction (MAR). In order to alleviate the need for anatomically identical CT image pairs (i.e. metal artifact-corrupted CT image and metal artifact-free CT image) for network learning, we propose a self-supervised cross-domain learning framework. Specifically, we train a neural network to restore the metal trace region values in the given metal-free sinogram, where the metal trace is identified by the forward projection of metal masks. We then design a novel filtered backward projection (FBP) reconstruction loss to encourage the network to generate more perfect completion results and a residual-learning-based image refinement module to reduce the secondary artifacts in the reconstructed CT images. To preserve the fine structure details and fidelity of the final MAR image, instead of directly adopting convolutional neural network (CNN)-refined images as output, we incorporate the metal trace replacement into our framework and replace the metal-affected projections of the original sinogram with the prior sinogram generated by the forward projection of the CNN output. We then use the FBP algorithms for final MAR image reconstruction. We conduct an extensive evaluation on simulated and real artifact data to show the effectiveness of our design. Our method produces superior MAR results and outperforms other compelling methods. We also demonstrate the potential of our framework for other organ sites.

The CADMUS trial: A paired cohort, blinded study comparing multiparametric ultrasound targeted biopsies with multiparametric MRI targeted biopsies in the detection of clinically significant prostate cancer.

5008 Background: Multiparametric MRI (mpMRI) of the prostate followed by targeted biopsy is recommended in men at risk of prostate cancer. Dissemination of this pathway may be limited by cost, variable scan and reporting quality, and contraindicated in the presence of metallic implants and claustrophobia. Multi-parametric ultrasound (mpUSS) is a point of care test with low cost that combines b-mode, colour Doppler, elastography and contrast enhancement. CADMUS compared the diagnostic performance of mpUSS to mpMRI. Methods: CADMUS recruited 370 patients from seven sites to a prospective, multicentre, paired-cohort trial (ISRCTN 38541912). Ethics committee approval was obtained. Patients underwent both mpUSS and mpMRI independently, each with a positive test defined as a Likert score of &gt;3. Those with either a positive mpUSS or mpMRI, or both, were advised to undergo targeted biopsies. Reporting of each scan was carried out blind to the other and prior to biopsy; patients advised for biopsy were blinded to which test was positive. The order of mpUSS and mpMRI targeting was randomised. Primary outcomes were proportion of positive tests and detection of clinically significant cancer (csPCa) defined as Gleason &gt;4+3 of any length and/or maximum cancer core length of &gt;6mm of any grade [PROMIS definition1]. Results: 306 completed both mpUSS and mpMRI. Agreement in lesion detection between mpUSS and mpMRI was 73.2% (kappa 0.06, p = 0.14). 257 with positive results on mpUSS, mpMRI or both had targeted biopsies. Agreement on detection of csPCa was 91.1% (expected 59.8%, kappa 0.78, p &lt; 0.01). Overall, mpUSS detected 4.3% fewer csPCa than mpMRI (95% CI = [-8.3%, -1.5%]; p = 0.042 [Bonferroni correction]). mpUSS detected 7.2% (6/83) csPCa missed by mpMRI; mpMRI detected 20.5% (17/83) csPCa that mpUSS missed. At a less stringent definition of significant cancer, Gleason grade &gt;3+4 of any length (definition 3), agreement was 89.1% (expected 55.6%, kappa 0.75, p &lt; 0.01) mpUSS detected 5.4% fewer definition 3 cancers than mpMRI overall. mpUSS detected 7% (7/99) definition 3 cancers that mpMRI missed; mpMRI detected 21% (21/99) definition 3 cancers that mpUSS missed. Conclusions: The CADMUS trial shows mpUSS has a diagnostic performance approaching that of mpMRI and significant cancer detection is improved by the use of both scans over mpMRI alone. Clinical trial information: 38541912. [Table: see text]

NanoDot™ OSLDs in verifying radiotherapy dose calculations in the presence of metal implants: A Monte Carlo assisted research

The goal of this work is to evaluate the dosimetric accuracy of nanoDot optically stimulated luminescence dosimeters (OSLDs) for small field sizes both at phantom-implant interfaces and deeper depths of a phantom. The central axis percentage depth-dose (PDD) curves were obtained by 6 MV flattening-filter-free (FFF) photon beams in conjunction with the homogeneous and nonhomogeneous RW3 slab phantoms with aluminum (Al), titanium alloy grade 5 (Ti5), and stainless steel grade 316 L (SS) implant material sheets inserted into, in small fields: 2 × 2 cm2 and 4 × 4 cm2. Three different depths were defined within the heterogeneous phantoms created to compare the measured and calculated doses: The soft tissue/metal implant upper interface (level I), metal implant/soft tissue lower interface (level II), and deeper depth from the implant (level III). The assessment of OSLDs was performed at defined depths comparing by Monte Carlo simulation (EGSnrc-based DOSXYZnrc/BEAMnrc) and Acuros XB (AXB) algorithm with GafChromic™ EBT3 film and PTW 31014 pinpoint ion chamber (IC) measurements. The PDDs using OSLDs were compared to those predicted by MC, and AXB, and those measured by EBT3 and IC and found to agree with uncertainty (<3%) in a homogeneous medium. The mean relative differences between OSLD and MC simulation at the level I interface of the Al, Ti5, and SS implants were analyzed as 2.5%, 5.6%, and 9.8% for 2 × 2 cm2 and 3.0%, 5.2%, and 8.8% for 4 × 4 cm2, respectively. When using implant material with lower electron density such as Al, the OSLD measurement values were found to agree with each measurement and calculation technique. At level II, the relative doses measured using OSLDs were similar to those of MC calculations for both field sizes. The maximum difference was found at 3.2% for SS implant. Except for the vicinity of the metal implant/RW3 phantom slab interfaces, the OSLD relative dose values were similar to those of PDD measurements in homogeneous media at depths away from the implant material. The OSLDs present a high capacity for radiotherapy dosimetry in homogeneous medium.But we can not adequately consider the dose to the interface between tissue and high Zeff materials in the case of the Ti5 and SS implant materials for small fields using OSLDs.

Neutron microtomography to investigate the bone-implant interface—comparison with histological analysis

Bone properties and especially its microstructure around implants are crucial to evaluate the osseointegration of prostheses in orthopaedic, maxillofacial and dental surgeries. Given the intrinsic heterogeneous nature of the bone microstructure, an ideal probing tool to understand and quantify bone formation must be spatially resolved. X-ray imaging has often been employed, but is limited in the presence of metallic implants, where severe artifacts generally arise from the high attenuation of metals to x-rays. Neutron tomography has recently been proposed as a promising technique to study bone-implant interfaces, thanks to its lower interaction with metals. The aim of this study is to assess the potential of neutron tomography for the characterisation of bone tissue in the vicinity of a metallic implant. A standardised implant with a bone chamber was implanted in rabbit bone. Four specimens were imaged with neutron tomography and subsequently compared to non-decalcified histology to stain soft and mineralised bone tissues, used here as a ground-truth reference. An intensity-based image registration procedure was performed to place the 12 histological slices within the corresponding 3D neutron volume. Significant correlations (p < 0.01) were obtained between the two modalities for the bone-implant contact (BIC) ratio (R = 0.77) and the bone content inside the chamber (R = 0.89). The results indicate that mineralised bone tissue can be reliably detected by neutron tomography. However, the BIC ratio and bone content were found to be overestimated with neutron imaging, which may be explained by its sensitivity to non-mineralised soft tissues, as revealed by histological staining. This study highlights the suitability of neutron tomography for the analysis of the bone-implant interface. Future work will focus on further distinguishing soft tissues from bone tissue, which could be aided by the adoption of contrast agents.

Deep learning\u2013based metal artefact reduction in PET/CT imaging

ObjectivesThe susceptibility of CT imaging to metallic objects gives rise to strong streak artefacts and skewed information about the attenuation medium around the metallic implants. This metal-induced artefact in CT images leads to inaccurate attenuation correction in PET/CT imaging. This study investigates the potential of deep learning–based metal artefact reduction (MAR) in quantitative PET/CT imaging.MethodsDeep learning–based metal artefact reduction approaches were implemented in the image (DLI-MAR) and projection (DLP-MAR) domains. The proposed algorithms were quantitatively compared to the normalized MAR (NMAR) method using simulated and clinical studies. Eighty metal-free CT images were employed for simulation of metal artefact as well as training and evaluation of the aforementioned MAR approaches. Thirty 18F-FDG PET/CT images affected by the presence of metallic implants were retrospectively employed for clinical assessment of the MAR techniques.ResultsThe evaluation of MAR techniques on the simulation dataset demonstrated the superior performance of the DLI-MAR approach (structural similarity (SSIM) = 0.95 ± 0.2 compared to 0.94 ± 0.2 and 0.93 ± 0.3 obtained using DLP-MAR and NMAR, respectively) in minimizing metal artefacts in CT images. The presence of metallic artefacts in CT images or PET attenuation correction maps led to quantitative bias, image artefacts and under- and overestimation of scatter correction of PET images. The DLI-MAR technique led to a quantitative PET bias of 1.3 ± 3% compared to 10.5 ± 6% without MAR and 3.2 ± 0.5% achieved by NMAR.ConclusionThe DLI-MAR technique was able to reduce the adverse effects of metal artefacts on PET images through the generation of accurate attenuation maps from corrupted CT images.Key Points• The presence of metallic objects, such as dental implants, gives rise to severe photon starvation, beam hardening and scattering, thus leading to adverse artefacts in reconstructed CT images.• The aim of this work is to develop and evaluate a deep learning–based MAR to improve CT-based attenuation and scatter correction in PET/CT imaging.• Deep learning–based MAR in the image (DLI-MAR) domain outperformed its counterpart implemented in the projection (DLP-MAR) domain. The DLI-MAR approach minimized the adverse impact of metal artefacts on whole-body PET images through generating accurate attenuation maps from corrupted CT images.