Artifact Reduction Research Articles

The Analysis Study of Diagnostic Imaging Performance and Accuracy of Peripheral Artery Disease: A Comprehensive Systematic Review

Background: Imaging advancements in diagnosing peripheral artery disease (PAD) are crucial for improving patient outcomes. Digital subtraction angiography (DSA) is the gold standard, but non-invasive techniques like computed tomography angiography (CTA) and magnetic resonance angiography (MRA) are preferred. Photon-counting computed tomography (PCCT) offers superior spatial resolution and reduced beam-hardening artifacts, enhancing the accuracy of detecting and quantifying stenotic disease. Methods: This systematic review adhered to the PRISMA 2020 guidelines, focusing exclusively on full-text articles published in English between 2013 and 2024. Editorials and review articles without a DOI were excluded to ensure high-quality sources. A literature search was conducted across reputable databases, including ScienceDirect, PubMed, and SagePub, to identify relevant studies. Result: This review included eight studies encompassing a total of over 1,000 patients. The methodological quality of the studies was moderate, with some risk of misclassification and verification bias. Each study was critically appraised, providing a comprehensive analysis of the diagnostic imaging performance and accuracy in the evaluation of peripheral artery disease. Conclusion: The study highlights the benefits of PCCT in diagnosing PAD, especially in patients with advanced disease, calcifications, or renal impairment. It offers superior spatial resolution, artifact reduction, and lower contrast media requirements, promising improved diagnostic accuracy.

Prospective motion correction for and susceptibility mapping using spherical navigators.

To perform prospective motion correction (PMC) for improved and susceptibility mapping using a purely navigator-based approach. Spherical navigators (SNAVs) were combined with an additional FID readout for simultaneous measurement of motion and zeroth-order field shifts. The resulting FIDSNAVs were interleaved for PMC of a multi-echo gradient echo sequence with retrospective correction. Experiments were performed on a 3T scanner with a 32-channel head coil. Performance was assessed in five volunteers with motion prompts derived from real unintentional motion trajectories. At short TEs, PMC alone was sufficient to achieve good image quality; at longer TEs, retrospective correction was often just as important for artifact reduction as motion correction. Both PMC and retrospective correction reduced error in and susceptibility maps for all participants. Residual artifacts were observed in the most severe motion case. Combining SNAVs with an additional FID readout enables simultaneous motion and field correction with no additional hardware requirements, improving the fidelity of quantitative mapping in the presence of motion.

Multi-scale perceptual modulation network for low-dose computed tomography denoising.

Low-dose computed tomography (LDCT) reduces radiation exposure, but the introduced noise and artifacts impair its diagnostic accuracy. Convolutional neural networks (CNNs) are widely used for LDCT denoising, but they suffer from a limited receptive field. The use of a larger kernel size can enlarge the receptive field and boost model performance; however, the computational cost of the model greatly increases. We aimed to develop a LDCT denoising CNN with a large receptive field and lower computational complexity. We developed a multi-scale perceptual modulation network (MSPMnet) incorporating a powerful multi-head decomposable convolution (MHDC). To address the high computational complexity of large kernel convolutions, we developed a novel MHDC module that can capture multi-scale features and efficiently expand the receptive field. The MHDC module couples maximum-pooling with three depth-wise convolutions of varying kernel sizes via a channel splitting mechanism, where, unlike conventional CNNs, the two large two-dimensional kernels are each decomposed into a set of cascaded orthogonal one-dimensional kernels to remain lightweight. Further, departing from prior methodologies that apply a uniform kernel size throughout the network, we introduced a receptive field-ramp mechanism that adeptly transitions from local to relatively long-range dependency modeling as the network depth increases, thereby achieving superior performance. The proposed MSPMnet was evaluated on a Mayo Clinic data set with a conventional iterative algorithm, two CNN models, and two Transformer models used for comparison. Compared to the competing baseline methods, the MSPMnet exhibited better performance in both the visual and quantitative assessments. Visually, the MSPMnet preserved the structure, edges, and textures with excellent noise and artifact reduction, generating the denoised images closest to normal-dose computed tomography images. Quantitatively, the MSPMnet had the lowest root mean-square error (RMSE) (8.3094±1.9325) and the highest peak signal-to-noise ratio (PSNR) (33.8525±1.8213 dB), structural similarity index (SSIM) (0.9309±0.0272), and feature similarity index (FSIM) (0.9699±0.0113), demonstrating superior denoising performance. The proposed MSPMnet excelled at LDCT denoising, effectively removing noise and artifacts while preserving edges. Compared to the state-of-the-art CNNs and Transformers, the proposed MSPMnet exhibited superior denoising performance both quantitatively and qualitatively.

Design of flexible polyimide-based serpentine EMG sensor for AI-enabled fatigue detection in construction

Physical fatigue and musculoskeletal disorders are critical health issues for construction workers, stemming from repetitive motions, heavy lifting, and awkward postures. These factors compromise worker well-being, productivity, and safety while increasing the risk of accidents and long-term health problems. Recent advancements in wearable health monitoring technology offer potential solutions, but current sensors encounter significant challenges in the dynamic construction environment. These include inadequate skin contact, increased contact impedance, and vulnerability to motion artifacts all of which degrade signal quality and reduce the accuracy of fatigue detection. This paper develops a fractal-based, flexible sensor for enhanced adaptability and accurate fatigue estimation. Finite element analysis compared five space-filling designs, with the serpentine curve exhibiting the highest contact area and lowest strain, making it the preferred choice for fabrication. Evaluations demonstrated significant improvements in signal-to-noise ratio (SNR) and motion artifact reduction, with the newly developed sensor achieving a 37 % to 59 % SNR improvement over commercial electrodes across different muscle groups. The developed flexible sensor was integrated with a fatigue-detecting framework based on a vision transformer model which provided an average accuracy of 87 % for fatigue detection. The developed sensor significantly enhances EMG signal quality and reliability, promising improved health monitoring and safety for construction workers.

Development and Evaluation of a Deep Learning Model to Reduce Exomass-Related Metal Artefacts in Cone-Beam Computed Tomography of the Jaws.

To develop and evaluate a deep learning (DL) model to reduce metal artefacts originating from the exomass in cone-beam computed tomography (CBCT) of the jaws. Five porcine mandibles, each featuring six tubes filled with a radiopaque solution, were scanned using four CBCT units before and after the incremental insertion of up to three titanium, titanium-zirconium, and zirconia dental implants in the exomass of a small field of view. A conditional denoising diffusion probabilistic model, using DL techniques, was employed to correct images from exomass-related metal artefacts across the CBCT units and implant scenarios. Three examiners independently scored the image quality of all datasets, including those without an implant (ground truth), with implants in the exomass (original), and DL-generated ones. Quantitative analysis compared contrast-to-noise ratio (CNR) to validate artefact reduction using repeated measures analysis of variance in a factorial design followed by Tukey test (α = 0.05). The visualisation of the hard tissues and overall image quality was reduced in the original and increased in the DL-generated images. The score variation observed in the original images was not observed in the DL-generated images, which generally scored higher than the original images. DL-generated images revealed significantly greater CNR than both the ground truth and their corresponding original images, regardless of the material and quantity of dental implants and the CBCT unit (p < 0.05). Original images revealed significantly lower CNR than the ground truth (p < 0.05). The developed DL model demonstrated promising performance in correcting exomass-related metal artefacts in CBCT of the jaws.

3D-Printed Metacarpal Prosthesis in the Treatment of Primary Osteosarcoma of the First Metacarpal: A Novel Surgical Technique.

Osteosarcoma at the first metacarpal is extremely rare. Reconstructing the metacarpal after tumor resection is essential, as the thumb accounts for approximately 40%-50% of hand function. Although autografts, arthroplasty, and transposition have been reported as reconstruction options, their use is limited by complications such as secondary injury, nonunion, and displacement. In this study, we present a case of a patient with first metacarpal osteosarcoma who underwent tumor resection followed by reconstruction with a 3D-printed metacarpal prosthesis. We tend to introduce a novel strategy to reconstruct the first metacarpal and restore the hand function. A 30-year-old male with 5-month history of first metacarpal swelling in the left hand was admitted to our center. Imaging examinations and incision biopsy confirmed a diagnosis of intramedullary well-differentiated osteosarcoma. A 3D-printed metacarpal prosthesis was then designed to achieve carpometacarpal (CMC) joint fusion and thumb metacarpophalangeal (MCP) joint reconstruction. Postoperative evaluations included X-ray and tomosynthesis-shimadzu metal artifact reduction technology (T-SMART) imaging to assess bone-prosthesis integration. Hand function was measured using the Musculoskeletal Tumor Society (MSTS) score and the Disabilities of the Arm, Shoulder, and Hand (DASH) score. The tumor was completely resected, and a 3D-printed metacarpal prosthesis was performed to reconstruct the tumor defect. Postoperative imaging showed that the interface between bone and prosthesis was integrated and that there was no loose, displacement, or fracture of the implant. At the last follow-up, the patient had an MSTS score of 25/30 and a DASH score of 8/100. The range of motion on thumb MCP joint was 30° of flexion and 0° of extension. The Kapandji thumb opposition score was 4 points. The grip strength was 9 kg (compared to 30 kg on the contralateral side) and the key-pinch strength was 3 kg (compared to 8 kg on the contralateral side). 3D-printed metacarpal prosthesis could be an effective reconstruction option for patients with low-grade malignant tumors. Themulti-planar fixation achieved through 3D surgical planning helps maintain thumb function and restore overall hand function.

Reducing Metal Artifacts in Clinical Photon Counting Detector Computed Tomography-A Phantom Study of an Exemplary Total Hip Arthroplasty.

To examine how different photon-counting detector (PCD) CT scanning and reconstruction methods affect the volume of metal artifacts and image quality for a hip prosthesis phantom. A titanium and cobalt-chromium-molybdenum-alloy total hip prosthesis phantom was scanned using a clinical PCD-CT with a constant tube potential (140kV) and Computed-Tomography-Dose- Index (7mGy). Different scan settings were used: with/without tin-filter (Sn), with/without ultra-high resolution (UHR), both individually and combined, resulting in four modes: Quantumplus (Standard), UHR Quantumplus (HighRes), QuantumSn (Standard-Tin) and UHR QuantumSn (HighRes-Tin). Reconstructions included virtual monoenergetic images (VMI) spanning 40-190keV and polychromatic images, with/without iterative metal artifact reduction (MAR). Artifact volumes rendered in a 3D-printing software were quantified in milliliters (ml), and image quality was evaluated using a Likert score. Polychromatic reconstruction: Tin-filter reduced artifact volumes (298 (Standard-Tin) vs. 347ml (Standard) and 310 (HighRes-Tin) vs. 360ml (HighRes)). The smallest artifact volume was measured in HighRes MAR (150ml). VMI reconstruction: The smallest artifact volume was measured in Standard 130keV (150ml) and HighRes 130keV (164ml) and in Standard-Tin 120keV (169ml) and HighRes-Tin 120keV (172ml). MAR further reduced the artifact volumes to 130ml (Standard 150keV MAR) and 140ml (HighRes 160keV MAR). Image quality was rated best for Standard 65keV MAR, polychromatic HighRes MAR, Standard 100keV MAR, polychromatic Standard-tin MAR, HighRes-tin 100keV and polychromatic HighRes-tin. Combining tin-filter, UHR and MAR in VMI or polychromatic images achieve the strongest artifact reduction.

Influence of Metal Artifact Reduction Tool of Two Cone Beam CT on the Detection of Bone Graft Loss Around Titanium and Zirconium Implants-An ExVivo Diagnostic Accuracy Study.

The objective of this study is to investigate the influence of metal artifact reduction (MAR) on two cone beam computed tomography (CBCT) units in the evaluation of bone graft loss adjacent to titanium (Ti) and zirconium (Zr) implants. Twelve Ti and twelve Zr implants were placed in the posterior region of dry human mandibles. Bone graft was applied to the level of the cover screw. Bone graft loss was simulated in half of the sample (6 Ti and 6 Zr) by removing the graft material up to the third implant thread on the buccal surface. CBCT images were acquired on two units, varying the application of MAR (OP300-off and on; Eagle 3D-standard, intermediate, and extreme). The images were assessed by five evaluators that scored the presence of graft loss according to a 5-point scale. The diagnostic values were calculated and compared by non-parametric tests with a significance level of 5%. Higher diagnostic values were achieved with MAR activated in the OP300 unit, for Ti and Zr (p < 0.05). On the Eagle 3D unit, MAR in extreme mode resulted in lower diagnostic values for both types of implants (p < 0.05). The diagnostic values of Ti implants were higher than Zr implants (p < 0.05). The application of MAR influences the diagnosis of bone graft loss adjacent to Ti and Zr dental implants. However, the extreme mode of MAR in the Eagle 3D unit can impair the diagnostic task in both types of implants and should be avoided.

Dental Implant Artifacts in MRI: Compatibility and Considerations

This review investigated dental implant artifacts in magnetic resonance imaging (MRI) and their safety in clinical practice. Dental prostheses, including implants, crowns, and orthodontic appliances, cause artifacts due to their high magnetic susceptibility, particularly in materials like iron, stainless steel, and cobalt-chromium. Titanium implants are considered safe under MRI environments according to the American Society for Testing and Material (ASTM) standards, with no reported thermal injury or dislodgement during examinations. Despite limited artifacts from titanium's paramagnetic nature, minute ferromagnetic components can still affect visualization. Thus, reducing artifacts in oral and maxillofacial MRI scans is crucial. Two main categories of artifact reduction techniques are identified: improved porous titanium or alternative materials like zirconia and adjusting MR parameters with advanced sequences. Recommendations include increasing the readout bandwidth, reducing slice thickness, using spin-echo sequences instead of gradient-echo, and employing short tau inversion recovery or DIXON techniques for fat suppression. Additional methods like VAT, VAT-SEMAC combination, and MAVRIC show promise, although applicability may be restricted in specific MRI scanners. Continuous advancements in dental implant materials and MRI sequences are needed to improve imaging quality and reduce artifacts. Collaboration among dental practitioners, radiologists, and MRI technologists is essential for refining techniques and ensuring patient safety. Although overall dental implant artifacts pose challenges, safety in MRI is well-established. Ongoing developments hold significant potential to enhance MRI imaging quality in patients with dental devices

A Review of Applications of Photon-Counting Computed Tomography in Head and Neck Imaging.

Photon-counting CT (PCCT), approved for clinical practice for over two years now, both improves on features of conventional energy-integrating detector (EID) CT and introduces new capabilities such as multienergy acquisition. PCCT is already transforming all domains of radiology, including head and neck imaging, and will become increasingly utilized in the approaching years. In this review, we first concisely explain the key physical principles distinguishing PCCT from EID-CT. We then discuss how the underlying physics leads to the novel features associated with PCCT, focusing on improved artifact reduction, spatial resolution, contrast-to-noise ratio, as well as multienergy acquisition and reduced contrast and radiation doses. Next, we review head and neck PCCT applications and comparison to EID-CT in dental imaging, sinus imaging, temporal bone, tumor imaging, and vascular imaging. Within the temporal bone applications, we explore normal anatomy, pathologic anatomy, and the appearance of protheses and implants. Representative imaging is provided to highlight differences between PCCT and EID-CT. Finally, we highlight areas of ongoing research in PCCT.

Comprehensive Image Quality Evaluation and Motion Phantom Studies of an Ultra-Fast (6-Second) Cone-Beam Computed Tomography Imaging System on a Ring Gantry Linear Accelerator

PurposeTo evaluate the image quality of an ultra-fast CBCT system - Varian HyperSight. MethodsIn this evaluation, five-studies were performed to assess the image quality of HyperSight CBCT. First, a HyperSight CBCT image quality evaluation was performed and compared with Siemens simulation-CT and Varian TrueBeam CBCT. Second, a visual comparison of image quality among simulation-CTs, HyperSight CBCT and TrueBeam CBCT was performed for a head-and-neck patient, and patients with metal dental fillings and prosthesis. Third, the HU vs electron density curve of HyperSight CBCT was compared with GE and Siemens simulation-CTs. Fourth, Siemens simulation-CT and HyperSight CBCT scans were acquired on the Catphan setting-up at different locations inside the bore (±10cm in all three principal directions from the center), and the HU variations for different materials were evaluated. Fifth, a 4D lung tumor phantom study was performed to assess moving tumor alignment during image registration. ResultsSignificant improvement of image contrast, HU constancy, and noise level on HyperSight CBCT was observed compared to TrueBeam CBCT. Significant image quality improvement was observed on HyperSight CBCT for patients with dental fillings and prosthesis compared to simulation-CT without metal artifact reduction. The linear fit trend-line of HU vs electron density curves for GE simulation-CT, Siemens simulation-CT and HyperSight CBCT showed 0.6% difference for HU value below 2000. The maximum HU difference for HyperSight CBCT when Catphan positioned within ±10cm in all three principal directions was ≤98 on bone 50%, and ≤29 other than bone, and was ≤31 on bone 50%, and ≤17 other than bone for Siemens simulation-CT. Both tumor shape and tumor alignment discrepancies on CBCT scans were observed in 4D phantom study. ConclusionsThis evaluation shows significant image improvement of HyperSight CBCT over conventional CBCT on image contrast, HU constancy, and noise level with scatter correction and metal artifact reduction reconstruction methods. HyperSight CBCT has similar image quality to simulation-CTs, and shows the potential application for treatment planning. The rapid acquisition of HyperSight CBCT showed both tumor shape and tumor alignment discrepancies of moving target. Careful considerations of patient respiratory motion monitoring and target matching are highly recommended.

Artifactual Subclavian Artery Thrombosis on CTA With Iterative Metal Artifact Reduction.

Artifactual Subclavian Artery Thrombosis on CTA With Iterative Metal Artifact Reduction.

Dual-encoder architecture for metal artifact reduction for kV-cone-beam CT images in head and neck cancer radiotherapy

During a radiotherapy (RT) course, geometrical variations of target volumes, organs at risk, weight changes (loss/gain), tumor regression and/or progression can significantly affect the treatment outcome. Adaptive RT has become the effective methods along with technical advancements in imaging modalities including cone-beam computed tomography (CBCT). Planning CT (pCT) can be modified via deformable image registration (DIR), which is applied to the pair of pCT and CBCT. However, the artifact existed in both pCT and CBCT is a vulnerable factor in DIR. The dose calculation on CBCT is also suggested. Missing information due to the artifacts hinders the accurate dose calculation on CBCT. In this study, we aim to develop a deep learning-based metal artifact reduction (MAR) model to reduce the metal artifacts in CBCT for head and neck cancer RT. To train the proposed MAR model, we synthesized the kV-CBCT images including metallic implants, with and without metal artifacts (simulated image data pairs) through sinogram image handling process. We propose the deep learning architecture which focuses on both artifact removal and reconstruction of anatomic structure using a dual-encoder architecture. We designed four single-encoder models and three dual-encoder models based on UNet (for an artifact removal) and FusionNet (for a tissue restoration). Each single-encoder model contains either UNet or FusionNet, while the dual-encoder models have both UNet and FusionNet architectures. In the dual-encoder models, we implemented different feature fusion methods, including simple addition, spatial attention, and spatial/channel wise attention. Among the models, a dual-encoder model with spatial/channel wise attention showed the highest scores in terms of peak signal-to-noise ratio, mean squared error, structural similarity index, and Pearson correlation coefficient. CBCT images from 34 head and neck cancer patients were used to test the developed models. The dual-encoder model with spatial/channel wise attention showed the best results in terms of artifact index. By using the proposed model to CBCT, one can achieve more accurate synthetic pCT for head and neck patients as well as better tissue recognition and structure delineation for CBCT image itself.

Imaging lung tumor motion using integrated-mode proton radiography-A phantom study towards tumor tracking in proton radiotherapy.

Motion of lung tumors during radiotherapy leads to decreased accuracy of the delivered dose distribution. This is especially true for proton radiotherapy due to the finite range of the proton beam. Methods for mitigating motion rely on knowing the position of the tumor during treatment. Proton radiography uses the treatment beam, at an energy high enough to traverse the patient, to produce a radiograph. This work shows the first results of using an integrated-mode proton radiography system to track the position of moving objects in an experimental phantom study; demonstrating the potential of using this method for measuring tumor motion. Proton radiographs of an anthropomorphic lung phantom, with a motor-driven tumor insert, were acquired approximately every 1s, using tumor inserts of 10, 20, and 30mm undergoing a known periodic motion. The proton radiography system used a monolithic scintillator block and digital cameras to capture the residual range of each pencil beam passing through the phantom. These ranges were then used to produce a water equivalent thickness map of the phantom. The centroid of the tumor insert in the radiographs was used to determine its position. This measured position was then compared to the known motion of the phantom to determine theaccuracy. Submillimeter accuracy on the measurement of the tumor insert was achieved when using a 30mm tumor insert with a period of 24s and was found to be improved for decreasing motion amplitudes with a mean absolute error (MAE) of 1.0, 0.9, and 0.7mm for 20, 15, and 10mm respectively. Using smaller tumor inserts reduced the accuracy with a MAE of 1.8 and 1.9mm for a 20 and 10mm insert respectively undergoing a periodic motion with an amplitude of 20mm and a period of 24s. Using a shorter period resulted in significant motion artifacts reducing the accuracy to a MAE of 2.2mm for a 12s period and 3.1mm for a 6s period for the 30mm insert with an amplitude of20mm. This work demonstrates that the position of a lung tumor insert in a realistic anthropomorphic phantom can be measured with high accuracy using proton radiographs. Results show that the accuracy of the position measurement is the highest for slower tumor motions due to a reduction in motion artifacts. This indicates that the primary obstacle to accurate measurement is the speed of the radiograph acquisition. Although the slower tumor motions used in this study are not clinically realistic, this work demonstrates the potential for using proton radiography for measuring tumor motion with an increased scanning speed that results in a decreased acquisitiontime.

Investigation of the effectiveness of eight different metal artifact reduction algorithms in reducing extracorporeal metal artifacts: a phantom study using the Gumbel method

Investigation of the effectiveness of eight different metal artifact reduction algorithms in reducing extracorporeal metal artifacts: a phantom study using the Gumbel method

Combining different metal artifact reduction levels with sharpening filters and slice thickness for the visualization of mandibular canals perforated by implants.

To investigate the influence of combining levels of metal artifact reduction (MAR) tool with different filters and slice-thickness in the detection of mandibular canals perforated by implants on CBCT scans and to objectively assess the impact of the aforementioned combinations. Implants were placed above (8 implants) and inside (10 implants) mandibular canals of dried-mandibles. CBCT scans were obtained with the Eagle 3D unit (85 kVp, 8mA, 5 × 5cm FOV, 130μm voxel size, and off/medium/high MAR levels). Examiners evaluated the scans under each MAR level and across different conditions: no filter, Sharpen 1×, Sharpen 2×; 0mm, 1mm, and 2mm slice-thickness. The gray values on axial reconstructions were assessed. Area under the receiver operating characteristic curve (AUC), sensitivity, specificity, mean and standard deviation (SD) for gray values were calculated and compared by multi-way Analysis of Variance. Overall, the AUC and sensitivity decreased with high-level MAR (p < 0.0001), regardless other variables tested. Enabling MAR tool decreased mean gray values (p < 0.01) and increasing MAR levels reduced the SD values on scans with Sharpen 2× (p < 0.012). High-level MAR impairs the visualization of mandibular canals perforated by implants. Moreover, enabling MAR tool decreases the mean gray values. MAR tool, filters, and slice thickness influence the image quality of CBCT scans. Therefore, it is important to evaluate the impact of these parameters on the diagnosis of mandibular canals perforated by implants.

Joint suppression of cardiac bSSFP cine banding and flow artifacts using twofold phase-cycling and a dual-encoder neural network

BackgroundCardiac bSSFP cine imaging suffers from banding and flow artifacts induced by off-resonance. The work aimed to develop a twofold phase cycling sequence with a neural network-based reconstruction (2P-SSFP+Network) for a joint suppression of banding and flow artifacts in cardiac cine imaging. MethodsA dual-encoder neural network was trained on 1620 pairs of phase-cycled left ventricular (LV) cine images collected from 18 healthy subjects. Twenty healthy subjects and 25 patients were prospectively scanned using the proposed 2P-SSFP sequence. bSSFP cine of a single RF phase increment (1P-SSFP), bSSFP cine of a single RF phase increment with a network-based artifact reduction (1P-SSFP+Network), the averaging of the two phase-cycled images (2P-SSFP+Average), and the proposed method were mutually compared, in terms of artifact suppression performance in the LV, generalizability over altered scan parameters and scanners, suppression of large-area banding artifacts in the left atrium (LA), and accuracy of downstream segmentation tasks. ResultsIn the healthy subjects, 2P-SSFP+Network showed robust suppressions of artifacts across a range of phase combinations. Compared with 1P-SSFP and 2P-SSFP+Average, 2P-SSFP+Network improved banding artifacts (3.85±0.67 and 4.50±0.45 vs 5.00±0.00, P<0.01 and P=0.02, respectively), flow artifacts (3.35±0.78 and 2.10±0.77 vs 4.90±0.20, both P<0.01), and overall image quality (3.25±0.51 and 2.30±0.60 vs 4.75±0.25, both P<0.01). 1P-SSFP+Network and 2P-SSFP+Network achieved a similar artifact suppression performance, yet the latter had fewer hallucinations (two-chamber, 4.25±0.51 vs 4.85±0.45, P=0.04; four-chamber, 3.45±1.21 vs 4.65±0.50, P=0.03; and LA, 3.35±1.00 vs 4.65±0.45, P<0.01). Furthermore, in the pulmonary veins and LA, 1P-SSFP+Network could not eliminate banding artifacts since they occupied a large area, whereas 2P-SSFP+Network reliably suppressed the artifacts. In the downstream automated myocardial segmentation task, 2P-SSFP+Network achieved more accurate segmentations than 1P-SSFP with different phase increments. Conclusions2P-SSFP+Network jointly suppresses banding and flow artifacts while manifesting a good generalizability against variations of anatomy and scan parameters. It provides a feasible solution for robust suppression of the two types of artifacts in bSSFP cine imaging.

Pulsation artifact reduction using compressed sensitivity encoding in Gd-EOB-DTPA contrast-enhanced magnetic resonance imaging.

In Gd-EOB-DTPA-enhanced MRI, cardiac pulsation artifacts in the left lobe often hinder diagnosis, the image quality need to improve. This study aimed to reduce cardiac pulsation artifacts in Gd-EOB-DTPA-enhanced three-dimensional (3D) T1-weighted turbo-field echo (3D-T1TFE) using compressed sensitivity encoding (CS).For phantom evaluation, the cardiac phantom was manually operated using a metronome-synchronized apparatus, comprising a bag-valve mask, a breathing circuit, and a Jackson-Rees system. Transverse images of a liver phantom were acquired using enhanced T1 high-resolution isotropic volumetric excitation with CS (CS-eTHRIVE) and sensitivity encoding (S-eTHRIVE). For evaluation, images obtained during cardiac phantom operation were subtracted from those obtained when the phantom was stationary. Standard deviation (SD) of the difference images was used as the evaluation metric, and assessments were conducted based on changes in heart rate and TFE factor. For clinical image evaluation, artifacts in hepatobiliary phase images acquired 15min after Gd-EOB-DTPA injection in the order of S-eTHRIVE and CS-eTHRIVE were visually evaluated at four levels. In heart-rate evaluation (40, 60, and 80 beats/min), CS-eTHRIVE revealed significantly lower SD values compared to S-eTHRIVE across all heart rates (P < 0.01), with no significant differences between heart rates. For TFE factor evaluation, CS-eTHRIVE with a factor of 35 exhibited the lowest SD, which was significantly different from all other groups (P < 0.01). In clinical image evaluation, CS-eTHRIVE achieved higher visual scores (mean ± SD: 3.72 ± 0.46) compared with S-eTHRIVE (2.72 ± 0.98, P < 0.01).CS reduced pulsation artifacts in Gd-EOB-DTPA-enhanced 3D-T1TFE.

Dual Energy Metal Artifact Reduction for Iodine-125 Seed Identification in Postimplant CT after Prostate Brachytherapy.

To assess the metal artifact reductions of dual-energy computed tomography (DECT) high-energy virtual monochromatic images (VMI) combined with the single energy metal artifact reduction (SEMER) (CANON MEDICAL SYSTEMS, Otawara, Japan) processing techniques for iodine (I)-125 seed identification in postimplant computed tomography (CT) after prostate brachytherapy. Dual-energy acquisition with fast tube voltage switching was performed on a prostate phantom with simulated seeds and six clinical cases treated with I-125 prostate brachytherapy. The images were retrospectively reconstructed at VMI energy levels of 65-200 keV and with and without SEMAR (SEMAR and non-SEMAR images). To estimate seed swelling, the caliber of iodine-125 seed was calculated as the full width at half maximum. The metal artifacts were evaluated using the artifact index (AI). The dose distributions were calculated and were compared among the high-energy VMI (SEMAR and non-SEMAR images) and low-energy VMI (SEMAR images). The blooming artifacts decreased at higher energy levels. In addition, the SEMAR process markedly reduced AI, which helped reduce overestimation of high dose ranges in the treatment planning dose map. The locations and number of iodine-125 seed were clearly identified in the dose distribution map of the treatment planning using 200keV VMI with SEMAR. The high-energy VMI of the dual energy CT in combination with SEMAR is appropriate for the postimplant planning process of I-125 prostate brachytherapy.

Non-iterative model-based inversion for low channel-count optical ultrasound imaginga).

Ultrasound image reconstruction is typically performed using the computationally efficient delay-and-sum algorithm. However, this algorithm is suboptimal for systems of low channel counts, where it causes significant image artefacts. These artefacts can be suppressed through model-based inversion approaches; however, their computational costs typically prohibit real-time implementations. In this work, the emerging optical ultrasound (OpUS) modality is considered, where ultrasound waves are both generated and detected using light. With this modality, imaging probes comprise very low channel counts, resulting in significant image artefacts that limit the imaging dynamic range. However, this low channel counts offer an opportunity for non-iterative ("direct") model-based inversion (DMI) on modest computational resources available in a typical workstation. When applied to both synthetic and experimental OpUS data, the presented DMI method achieved substantial reduction in image artefacts and noise, improved recovery of image amplitudes, and-after one-off pre-computation of the system matrices-significantly reduced reconstruction time, even in imaging scenarios exhibiting mild spatial inhomogeneity. Whilst here applied to an OpUS imaging system, DMI can be applied to other low channel-count imaging systems, and is therefore expected to achieve better image quality, reduce system complexity, or both, in a wide range of settings.