Distortion Artifacts Research Articles

Detection and Classification of Artifact Distortions in Optical Motion Capture Sequences

Optical motion capture systems are prone to errors connected to marker recognition (e.g., occlusion, leaving the scene, or mislabeling). These errors are then corrected in the software, but the process is not perfect, resulting in artifact distortions. In this article, we examine four existing types of artifacts and propose a method for detection and classification of the distortions. The algorithm is based on the derivative analysis, low-pass filtering, mathematical morphology, and loose predictor. The tests involved multiple simulations using synthetically-distorted sequences, performance comparisons to human operators (concerning real life data), and an applicability analysis for the distortion removal.

Simple low-light image enhancement based on Weber–Fechner law in logarithmic space

In an environment with poor illumination, such as indoor, night, and overcast conditions, the image information can be seriously lost, which affects the visual effect and degrades the performance of machine systems. However, existing methods such as retinex-based method, dehazing model-based method, and machine learning-based method usually have high computational complexity or are prone to color distortion, noise amplification, and halo artifacts. To balance the enhancement effect and processing speed, this paper applies the Weber–Fechner law to the grayscale mapping in logarithmic space and proposed an adaptive and simple color image enhancement method based on the improved logarithmic transformation. In the framework, the brightness component is extracted from the scene of the low-light image using Gaussian filtering after color space conversion. The image is logarithmically transformed by adaptively adjusting the parameters of the illumination distribution to improve the brightness of the image. The color saturation is hence compensated. The proposed algorithm adaptively reduces the impact of non-uniform illumination on the image, and the enhanced image is clear and natural. Our experimental results demonstrate improved performance to the existing image enhancement approaches.

Reproducibility and feasibility of optic nerve diffusion MRI techniques: single-shot echo-planar imaging (EPI), readout-segmented EPI, and reduced field-of-view diffusion-weighted imaging

BackgroundDiffusion-weighted imaging (DWI) is an essential technique for optic nerve diseases. However, the image quality of optic nerve DWI is decreased by the distortions and artifacts associated with conventional techniques. In order to establish this method as a critical tool in optic nerve diseases, reproducibility and feasibility of new technical and conventional approaches of DWI need to be systematically investigated.MethodsDWIs were acquired using ss-EPI, readout-segmented EPI (rs-EPI) DWI, and reduced field-of-view (rFOV) DWI. 26 volunteers (mean age 31.2 years) underwent repeated MRI examinations in order to assess scan–rescan reproducibility and accuracy. The apparent diffusion coefficient (ADC) values (three ROIs were measured on each side) were determined to evaluate the reproducibility of each sequence and the differences between the three techniques. To quantify the geometric distortion artifacts, the length of optic nerve and the maximum angle of optic nerve were defined and compared to T2-weighted imaging. In addition, two readers evaluated four different aspects of image quality on 5-point Likert scales.Resultsrs-EPI DWI (ICCs: 0.916, 0.797 and 0.781) and rFOV DWI (ICCs: 0.850, 0.595 and 0.750) showed higher reproducibility (ICCs: ROI1, ROI2 and ROI3) of mean ADC value in all three ROIs than ss-EPI DWI (ICCs: 0.810, 0.442 and 0.379). The quantitative analysis of geometric distortion yielded a higher agreement of both rs-EPI DWI and rFOV DWI with T2-weighted imaging than ss-EPI. rs-EPI DWI (2.38 ± 0.90) and rFOV DWI (2.46 ± 0.58) were superior to ss-EPI DWI (1.58 ± 0.64) with respect to overall image quality and other aspects of image quality, each with P < 0.05. The mean ADC values of rFOV DWI were significantly lower than those of rs-EPI DWI and ss-EPI DWI in all three ROIs (P < 0.001).ConclusionsBoth rs-EPI DWI and rFOV-EPI DWI are suitable techniques for the assessment of diffusion restriction and provide significantly improved image quality compared with ss-EPI DWI. For methods using the same acquisition time, rFOV DWI is superior to ss-EPI DWI, while rs-EPI showed an overall superiority, although this technique took 47% longer to perform.

FV 17 Wallerian degeneration of descending fiber tracts explains motor impairment after stroke

Introduction: Structural changes post-stroke have frequently been assessed using diffusion tensor imaging (DTI). While the microstructural integrity of various parts of the corticospinal tract (CST) has been linked to motor impairment 1 , it remains unclear whether the reduction in anisotropy can be attributed to descending or crossing fibers within the CST. To address this question, we used diffusion spectrum imaging (DSI), an advanced imaging approach that overcomes the shortcomings of DTI, and delineated the influence of CST-inherent crossing and descending fibers. Patients and Methods: DSI scans of 25 chronic stroke patients (mean age=66.7, sd=11.2) and 22 age-matched controls were corrected for motion-and distortion artifacts, reconstructed, and normalized using QSIPrep 2 . Lesion and individual white matter (WM) masks were applied to the resulting generalized fractional anisotropy (gfa)-maps, focusing the analysis on secondary degeneration in WM-tracts. A deterministic mask containing the number of trackable directions per voxel 3 was applied to compartmentalize whole-brain gfa-values. Compartment-wise mean gfa was extracted from motor tracts as defined in the HCP tractography atlas 4 . Using these values, we tested for differences between patients and controls and for an association with upper and lower limb motor impairment. Results: Patients featured reduced gfa-values within the affected CST compared to age-matched controls (t(45)=-3.35, p=.002). This difference was mainly attributable to voxels containing only one fiber direction (t(45)=-3.92, p<.001), i.e., descending fibers, while voxels containing two directions, i.e., crossing fibers, showed no difference (p>0.4). Mean gfa of the affected CST significantly explained upper limb impairment (R 2 =0.30, p=.005), especially for patients suffering from persistent motor deficits (R 2 =0.43, p=.01). This association was driven by unidirectional (R 2 =0.26, p=.009) and not multidirectional voxels (p>0.3). Mean-gfa within the rubrospinal tract was related to persisting lower limb deficits. Conclusion: Anisotropy measures of the CST derived from DSI were significantly reduced and explained upper limb motor impairment in chronic stroke patients. These findings were driven by voxels containing a single fiber direction suggesting that changes in microstructural integrity of motor pathways assessed via diffusion imaging originate from Wallerian degeneration of descending fibers. In non-fully recovered stroke patients with CST damage, extrapyramidal pathways such as the rubrospinal tract may contribute to leg movements, potentially reflecting a compensatory mechanism after CST damage. Our findings underline the potential of DSI to predict motor outcome, especially when focusing the analysis on descending fibers via compartmentalization. References 1 Koch et al., 2016, Annals Clin Transl Neurol. 2 Cieslak et al., 2021, Nature Methods. 3 Volz et al., 2018, Brain Struct Funct. 4 Yeh et al., 2018, NeuroImage.

Immediate postpartum assessment of the anal sphincter by endovaginal ultrasound: An experimental study

Obstetric anal sphincter injuries are frequently missed and carry a significant risk for the development of anal incontinence. Immediate postpartum endoanal ultrasound increases identification of these injuries but is rarely employed. We hypothesize that endovaginal ultrasound could be a feasible and easily available alternative sonographic tool to improve early diagnosis of anal sphincter tears. We conducted a prospective experimental study including 160 primiparous women. Shortly after vaginal delivery, patients underwent clinical and sonographic perineal examinations. We analyzed the feasibility of anal sphincter assessment by endovaginal ultrasound and its potential contribution in the early diagnosis of anal sphincter injuries. Sonographic assessment of the anal sphincter was analyzable for 136 patients (85.0%). Causes of non-analyzability included air artifacts (6.9%), lack of distinction between the external anal sphincter and surrounding tissues (9.4%) and distortion artifacts (9.4%). Patients in the non-analyzable ultrasound subgroup were less likely to have delivered in a dorsal lithotomy position (62.5% vs. 85.3 %) and more likely to have had an episiotomy (33.3% vs. 14.0%), and their risk of sphincter injury was more frequently classified as "improbable" on clinical examination (91.7% vs. 61.0%). Ultrasounds were analyzable for 96.4% of patients clinically reported as having "possible" or "certain" sphincter injuries. The incidence of anal sphincter injury was 16.9% for clinical observation and 20.0% with associated sonographic examination. Endovaginal ultrasound could be used as a complementary tool in assessment of the anal sphincter in high-risk patients. Its feasibility and easy availability make this technique a promising tool for improving the management of anal sphincter tears.

Rigid-aware self-supervised GAN for camera ego-motion estimation

Learning-based camera ego-motion estimation has attracted increasing attention and has made impressive improvements. However, the accuracy of unsupervised paradigm is still limited, especially in complex dynamic environment. In this paper, we propose a rigid-aware self-supervised generative adversarial network (GAN) for camera ego-motion estimation, which can effectively learn the rigidity of the scene and improve the accuracy of ego-motion estimation by combining the pixel- and the structure-level perception. Specifically, a rigid-aware generator is first designed for joint unsupervised learning of optical flow, stereo depth and camera pose from two consecutive frames. Then, an iterative pose refinement strategy with rigidity learning is presented to reduce the impact of moving objects in scenes. To overcome the limitation of the purely pixel-wised photometric methods, a rigidity mask embedded discriminator is attached to perceive structural distortion artifacts in synthesized fake images, which encourages the generator to learn additional structure-level information to improve the accuracy of pose estimation. Experiments on the benchmark datasets show that our model achieves the state-of-the-art performance in terms of both RPE and ATE compared to recent GAN-based methods.

Automated analysis of microplastics based on vibrational spectroscopy: are we measuring the same metrics?

The traditional manual analysis of microplastics has been criticized for its labor-intensive, inaccurate identification of small microplastics, and the lack of uniformity. There are already three automated analysis strategies for microplastics based on vibrational spectroscopy: laser direct infrared (LDIR)-based particle analysis, Raman-based particle analysis, and focal plane array-Fourier transform infrared (FPA-FTIR) imaging. We compared their performances in terms of quantification, detection limit, size measurement, and material identification accuracy and speed by analyzing the same standard and environmental samples. LDIR-based particle analysis provides the fastest analysis speed, but potentially questionable material identification and quantification results. The number of particles smaller than 60μm recognized by LDIR-based particle analysis is much less than that recognized by Raman-based particle analysis. Misidentification could occur due to the narrow tuning range from 1800 to 975cm-1 and dispersive artifact distortion of infrared spectra collected in reflection mode. Raman-based particle analysis has a submicrometer detection limit but should be cautiously used in the automated analysis of microplastics in environmental samples because of the strong fluorescence interference. FPA-FTIR imaging provides relatively reliable quantification and material identification for microplastics in environmental samples greater than 20μm but might provide an imprecise description of the particle shapes. Optical photothermal infrared (O-PTIR) spectroscopy can detect submicron-sized environmental microplastics (0.5-5μm) intermingled with a substantial amount of biological matrix; the resulting spectra are searchable in infrared databases without the influence of fluorescence interference, but the process would need to be fully automated.

Efficient and Accurate Stitching for 360° Dual-Fisheye Images and Videos.

Back-to-back dual-fisheye cameras are the most cost-effective devices to capture 360° visual content. However, image and video stitching for such cameras often suffer from the effect of fisheye distortion, photometric inconsistency between the two views, and non-collocated optical centers. In this paper, we present algorithms for geometric calibration, photometric compensation, and seamless stitching to address these issues for back-to-back dual-fisheye cameras. Specifically, we develop a co-centric trajectory model for geometric calibration to characterize both intrinsic and extrinsic parameters of the fisheye camera to fifth-order precision, a photometric correction model for intensity and color compensation to provide efficient and accurate local color transfer, and a mesh deformation model along with an adaptive seam carving method for image stitching to reduce geometric distortion and ensure optimal spatiotemporal alignment. The stitching algorithm and the compensation algorithm can run efficiently for 1920×960 images. Quantitative evaluation of geometric distortion, color discontinuity, jitter, and ghost artifact of the resulting image and video shows that our solution outperforms the state-of-the-art techniques.

Development of an Integrated C-Arm Interventional Imaging System With a Strip Photon Counting Detector and a Flat Panel Detector.

Modern interventional x-ray systems are often equipped with flat-panel detector-based cone-beam CT (FPD-CBCT) to provide tomographic, volumetric, and high spatial resolution imaging of interventional devices, iodinated vessels, and other objects. The purpose of this work was to bring an interchangeable strip photon-counting detector (PCD) to C-arm systems to supplement (instead of retiring) the existing FPD-CBCT with a high quality, spectral, and affordable PCD-CT imaging option. With minimal modification to the existing C-arm, a 51×0.6 cm2 PCD with a 0.75 mm CdTe layer, two energy thresholds, and 0.1 mm pixels was integrated with a Siemens Artis Zee interventional imaging system. The PCD can be translated in and out of the field-of-view to allow the system to switch between FPD and PCD-CT imaging modes. A dedicated phantom and a new algorithm were developed to calibrate the projection geometry of the narrow-beam PCD-CT system and correct the gantry wobbling-induced geometric distortion artifacts. In addition, a detector response calibration procedure was performed for each PCD pixel using materials with known radiological pathlengths to address concentric artifacts in PCD-CT images. Both phantom and human cadaver experiments were performed at a high gantry rotation speed and clinically relevant radiation dose level to evaluate the spectral and non-spectral imaging performance of the prototype system. Results show that the PCD-CT system has excellent image quality with negligible artifacts after the proposed corrections. Compared with FPD-CBCT images acquired at the same dose level, PCD-CT images demonstrated a 53% reduction in noise variance and additional quantitative imaging capability.

Turbo Gradient and Spin Echo PROPELLER-Diffusion Weighted Imaging for Orbital Tumors: A Comparative Study With Readout-Segmented Echo-Planar Imaging.

Purpose: To qualitatively and quantitatively compare the image quality and diagnostic performance of turbo gradient and spin echo PROPELLER diffusion-weighted imaging (TGSE-PROPELLER-DWI) vs. readout-segmented echo-planar imaging (rs-EPI) in the evaluation of orbital tumors.Materials and Methods: A total of 43 patients with suspected orbital tumors were enrolled to perform the two DWIs with comparable spatial resolution on 3T. The overall image qualities, geometric distortions, susceptibility artifacts, and lesion conspicuities were scored by using a four-point scale (1, poor; 4, excellent). Quantitative measurements, including contrast-to-noise ratios (CNRs), apparent diffusion coefficients (ADCs), geometric distortion rates (GDRs), and lesion sizes, were calculated and compared. The two ADCs for differentiating malignant from benign orbital tumors were evaluated. Wilcoxon signed-rank test, Kappa statistic, and receiver operating characteristics (ROC) curves were used.Results: TGSE-PROPELLER-DWI performed superior in all subjective scores and quantitative GDR evaluation than rs-EPI (p < 0.001), and excellent interobserver agreement was obtained for Kappa value ranging from 0.876 to 1.000. ADClesion of TGSE-PROPELLER-DWI was significantly higher than those of rs-EPI (p < 0.001). Mean ADC of malignant tumors was significantly lower than that of benign tumors both in two DWIs. However, the AUC for differentiating malignant and benign tumors showed no significant difference in the two DWIs (0.860 vs. 0.854, p = 0.7448). Sensitivity and specificity could achieve 92.86% and 72.73% for TGSE-PROPELLER-DWI with a cutoff value of 1.23 × 10–3 mm2/s, and 85.71% and 81.82% for rs-EPI with a cutoff value of 0.99 × 10–3 mm2/s.Conclusion: Compared with rs-EPI, TGSE-PROPELLER-DWI showed minimized geometric distortion and susceptibility artifacts significantly improved the image quality for orbital tumors and achieved comparable diagnostic performance in differentiating malignant and benign orbital tumors.

A boundary finding-based spatiotemporal fusion model for vegetation index

ABSTRACT The spatiotemporal image fusion technique is regarded as an effective and efficient remedy to blend two or more geometrically registered remote sensing images of the same scene acquired from different sensors into a single image with both high temporal and high spatial resolutions. A number of existing fusion methods developed for generating Landsat-like images from the Moderate Resolution Imaging Spectroradiometer (MODIS) have successfully solved limitations on vegetation dynamics monitoring. However, these methods either treat the weighting parameters as constants or adopt insufficient linear regression coefficients (considering only spatial, temporal or spectral weights), which may contribute to blocky artefacts and dissimilar pixels in the predicted image. In this study, a boundary finding-based vegetation index spatiotemporal fusion model (BESFM) is newly proposed to generate high spatial and high temporal resolutions Enhanced Vegetation Index (EVI) images in homogeneous and heterogeneous regions with rapid vegetation changes. The cloud-free Landsat-8 Operational Land Imager (OLI) and the MODIS (MOD09GA) data acquired from two study sites, located in Queensland and New South Wales and Australia, were selected to evaluate the performance of the proposed method. Compared with the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and flexible spatiotemporal data fusion (FSDAF), the BESFM outperforms in both visible image quality (reducing the spatial distortion and blocky artefacts in prediction caused by abrupt land cover changes) and quantitative indices (with RSME minimum to 0.011 and R2 maximum to 0.956). Since the BESFM method not only uses adjustable linear regression assumptions for heterogeneous and homogeneous regions differently but also implements boundary detection-based spatial weight with temporal and spectral weights together to enhance the predicted spatial details, it has the potential to increase the availability of fine spatial resolution data.

Functional Activities Detected in the Olfactory Bulb and Associated Olfactory Regions in the Human Brain Using T2-Prepared BOLD Functional MRI at 7T.

Olfaction is a fundamental sense that plays a vital role in daily life in humans, and can be altered in neuropsychiatric and neurodegenerative diseases. Blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) using conventional echo-planar-imaging (EPI) based sequences can be challenging in brain regions important for olfactory processing, such as the olfactory bulb (OB) and orbitofrontal cortex, mainly due to the signal dropout and distortion artifacts caused by large susceptibility effects from the sinonasal cavity and temporal bone. To date, few studies have demonstrated successful fMRI in the OB in humans. T2-prepared (T2prep) BOLD fMRI is an alternative approach developed especially for performing fMRI in regions affected by large susceptibility artifacts. The purpose of this technical study is to evaluate T2prep BOLD fMRI for olfactory functional experiments in humans. Olfactory fMRI scans were performed on 7T in 14 healthy participants. T2prep BOLD showed greater sensitivity than GRE EPI BOLD in the OB, orbitofrontal cortex and the temporal pole. Functional activation was detected using T2prep BOLD in the OB and associated olfactory regions. Habituation effects and a bi-phasic pattern of fMRI signal changes during olfactory stimulation were observed in all regions. Both positively and negatively activated regions were observed during olfactory stimulation. These signal characteristics are generally consistent with literature and showed a good intra-subject reproducibility comparable to previous human BOLD fMRI studies. In conclusion, the methodology demonstrated in this study holds promise for future olfactory fMRI studies in the OB and other brain regions that suffer from large susceptibility artifacts.

Diffusion-weighted MRI of solid pancreatic lesions: Comparison between reduced field-of-view and large field-of-view sequences

PurposeTo compare the image quality, presence of artifacts and apparent diffusion coefficient (ADC) values of reduced field-of-view (rFOV) and large FOV (lFOV) single-shot spin-echo echo-planar diffusion-weighted imaging (DWI) in the evaluation of solid pancreatic lesion. MethodThe 3T MR examinations of 60 patients with solid pancreatic lesions were examined. Two Readers independently performed qualitative analysis and quantitative measurements of the ADC values of the solid pancreatic lesions in both rFOV and lFOV DWI sequence. The qualitative analysis parameters included: 1) Sharpness, 2) Distortion, Ghosting, Motion and Susceptibility artifacts, 3) Lesion Conspicuity and 4) Overall Image Quality. These parameters were evaluated using a 4-point scale. The T-test for paired data was used to compare qualitative scores and the ADC values of the rFOV and lFOV DWI sequences, and to assess inter-reader agreement. ResultsThe qualitative analysis yielded scores for the rFOV DWI sequence, which were better for sharpness, artifacts, and overall image quality as compared to the lFOV DWI sequence according to the only Reader 2 (the most experienced) (p ≤ 0.001). As to lesion conspicuity, no significant difference was found by either Reader (p ≥ 0.245). As to quantitative analysis, both Readers found no significant difference between the two sequences in the ADC values of various solid pancreatic lesions (p ≥ 0.156). ConclusionsThe rFOV DWI sequence of the pancreas provides better anatomic structure visualization, reduced artifacts, and better overall image quality as compared to the lFOV DWI sequence according to the Reader with the more experience in abdominal MRI. The ADC values were not significantly different in the two sequences. The rFOV DWI sequence could be included in the standard MRI protocol for the pancreas.

Removal of cross-phase modulation artifacts in ultrafast pump–probe dynamics by deep learning

Pump–probe spectroscopy is a gold standard technique to investigate ultrafast electronic dynamics of material systems. Pulsed laser sources employed to pump and probe samples feature typically high peak power, which may give rise to coherent artifacts under a wide range of experimental conditions. Among those, the Cross-Phase Modulation (XPM) artifact has gathered particular attention as it produces particularly high signal distortions, in some cases hiding a relevant portion of the dynamics of interest. Here, we present a novel approach for the removal of XPM coherent artifacts in ultrafast pump–probe spectroscopy, based on deep learning. We developed XPMnet, a convolutional neural network able to reconstruct electronic relaxation dynamics otherwise embedded in artifact distortions, thus enabling the retrieval of fundamental information to characterize the material system under investigation. We validated XPMnet on Indium Tin Oxide (ITO), a heavily doped semiconductor displaying a plasmon resonance in the near-infrared, which is a key material for the development of infrared plasmonic devices. Pump–probe measurements of ITO show strong XPM artifacts that overwhelm the electronic cooling dynamics of interest due to the low optical density of the material at near-infrared photon energies. XPMnet retrieved ITO electronic dynamics in excellent agreement with expected outcomes in terms of material-specific time constants. This artificial intelligence method constitutes a powerful solution for XPM artifact removal, providing high accuracy and short execution time. We believe that this model could be integrated in real time in pump–probe setups to increase the amount of information one can derive from ultrafast spectroscopy measurements.

Improving CBCT quality to CT level using deep learning with generative adversarial network.

To improve image quality and computed tomography (CT) number accuracy of daily cone beam CT (CBCT) through a deep learning methodology with generative adversarial network. One hundred and fifty paired pelvic CT and CBCT scans were used for model training and validation. An unsupervised deep learning method, 2.5D pixel-to-pixel generative adversarial network (GAN) model with feature mapping was proposed. A total of 12000 slice pairs of CT and CBCT were used for model training, while ten-fold cross validation was applied to verify model robustness. Paired CT-CBCT scans from an additional 15 pelvic patients and 10 head-and-neck (HN) patients with CBCT images collected at a different machine were used for independent testing purpose. Besides the proposed method above, other network architectures were also tested as: 2D vs 2.5D; GAN model with vs without feature mapping; GAN model with vs without additional perceptual loss; and previously reported models as U-net and cycleGAN with or without identity loss. Image quality of deep-learning generated synthetic CT (sCT) images was quantitatively compared against the reference CT (rCT) image using mean absolute error (MAE) of Hounsfield units (HU) and peak signal-to-noise ratio (PSNR). The dosimetric calculation accuracy was further evaluated with both photon and proton beams. The deep-learning generated sCTs showed improved image quality with reduced artifact distortion and improved soft tissue contrast. The proposed algorithm of 2.5 Pix2pix GAN with feature matching (FM) was shown to be the best model among all tested methods producing the highest PSNR and the lowest MAE to rCT. The dose distribution demonstrated a high accuracy in the scope of photon-based planning, yet more work is needed for proton-based treatment. Once the model was trained, it took 11-12ms to process one slice, and could generate a 3D volume of dCBCT (80 slices) in less than a second using a NVIDIA GeForce GTX Titan X GPU (12GB, Maxwell architecture). The proposed deep learning algorithm is promising to improve CBCT image quality in an efficient way, thus has a potential to support online CBCT-based adaptive radiotherapy.

Comparison of simultaneous multi-slice single-shot DWI to readout-segmented DWI for evaluation of breast lesions at 3T MRI

PurposeTo compare diffusion-weighted imaging of the breast performed with a conventional readout-segmented echo-planar imaging (rs-EPI) sequence to when using a prototype simultaneous multi-slice single-shot EPI (SMS-ss-EPI) acquisition. MethodFrom September 2017 to December 2018, 26 women with histologically proven breast cancer were scanned with the conventional rs-EPI and the SMS-ss-EPI at 3 T during the same imaging examination. Four breast radiologists (4–13 years of experience) independently scored both acquired series of 25 women (one case was used for training) for overall image quality (1: extremely poor to 9: excellent) and artifacts (1: very disturbing to 5: not present). All lesions (n = 52; 40 malignant, 12 benign) were also evaluated for visibility (1: not visible, 2: visible if location is given, 3: visible). In addition, lesion characteristics were rated, and a BI-RADS score was given. Results were analyzed using visual grading characteristics and the resulting area under the curve (AUCVGC), weighted kappa, McNemar test, and dependent-samples t-test when appropriate. ResultsOverall, radiologists significantly preferred the image quality in rs-EPI over that of SMS-ss-EPI (AUCVGC: 0.698, P = 0.002). Infolding and ghosting, and distortion artifacts were significantly less apparent in the rs-EPI (AUCVGC: 0.660, P = 0.022 and AUCVGC: 0.700 P = 0.002, respectively). Lesions were, however, significantly better visible on the SMS-ss-EPI images (AUCVGC: 0.427, P = 0.016). Malignant lesions had significantly higher visibility with SMS-ss-EPI (P = 0.035). Sensitivity and specificity were comparable between both sequences (P = 0.760 and P = 0.549, respectively). ConclusionsDespite the perceived lower image quality and the increased presence of artifacts in the SMS-ss-EPI sequence, malignant lesions are better visualized using this sequence.

Correction of Artifacts Induced by B0 Inhomogeneities in Breast MRI Using Reduced-Field-of-View Echo-Planar Imaging and Enhanced Reversed Polarity Gradient Method.

Diffusion-weighted (DW) echo-planar imaging (EPI) is prone to geometric distortions due to B0 inhomogeneities. Both prospective and retrospective approaches have been developed to decrease and correct such distortions. The purpose of this work was to evaluate the performance of reduced-field-of-view (FOV) acquisition and retrospective distortion correction methods in decreasing distortion artifacts for breast imaging. Coverage of the axilla in reduced-FOV DW magnetic resonance imaging (MRI) and residual distortion were also assessed. Retrospective. Breast phantom and 169 women (52.4 ± 13.4 years old) undergoing clinical breast MRI. A 3.0 T/ full- and reduced-FOV DW gradient-echo EPI sequence. Performance of reversed polarity gradient (RPG) and FSL topup in correcting breast full- and reduced-FOV EPI data was evaluated using the mutual information (MI) metric between EPI and anatomical images. Two independent breast radiologists determined if coverage on both EPI data sets was adequate to evaluate axillary nodes and identified residual nipple distortion artifacts. Two-way repeated-measures analyses of variance and post hoc tests were used to identify differences between EPI modality and distortion correction method. Generalized linear mixed effects models were used to evaluate differences in axillary coverage and residual nipple distortion. In a breast phantom, residual distortions were 0.16 ± 0.07 cm and 0.22 ± 0.13 cm in reduced- and full-FOV EPI with both methods, respectively. In patients, MI significantly increased after distortion correction of full-FOV (11 ± 5% and 18 ± 9%, RPG and topup) and reduced-FOV (8 ± 4% both) EPI data. Axillary nodes were observed in 99% and 69% of the cases in full- and reduced-FOV EPI images. Residual distortion was observed in 93% and 0% of the cases in full- and reduced-FOV images. Minimal distortion was achieved with RPG applied to reduced-FOV EPI data. RPG improved distortions for full-FOV images but with more modest improvements and limited correction near the nipple. 3 TECHNICAL EFFICACY: Stage 1.

Deep learning reconstruction for contrast-enhanced CT of the upper abdomen: similar image quality with lower radiation dose in direct comparison with iterative reconstruction.

To evaluate the effect of a commercial deep learning algorithm on the image quality of chest CT, focusing on the upper abdomen. One hundred consecutive patients who simultaneously underwent contrast-enhanced chest and abdominal CT were collected. The radiation dose was optimized for each scan (mean CTDIvol: chest CT, 3.19 ± 1.53 mGy; abdominal CT, 7.10 ± 1.88 mGy). Three image sets were collected: chest CT reconstructed with an adaptive statistical iterative reconstruction (ASiR-CHT; 50% blending), chest CT with a deep learning algorithm (DLIR-CHT), and abdominal CT with ASiR (ASiR-ABD; 40% blending). Afterwards, the images covering the upper abdomen were extracted, and image noise, the signal-to-noise ratio (SNR), and the contrast-to-noise ratio (CNR) were measured. For subjective evaluation, three radiologists independently assessed noise, spatial resolution, presence of artifacts, and overall image quality. Additionally, readers selected the most preferable reconstruction technique among three image sets for each case. The average measured noise for DLIR-CHT, ASiR-CHT, and ASiR-ABD was 8.01 ± 2.81, 14.8 ± 2.56, and 12.3 ± 2.28, respectively (p < .001). Deep learning-based image reconstruction (DLIR) also showed the best SNR and CNR (p < .001). However, in the subjective analysis, ASiR-ABD showed less subjective noise than DLIR (2.94 ± 0.23 vs. 2.87 ± 0.26; p < .001), while DLIR showed better spatial resolution (2.60 ± 0.34 vs. 2.44 ± 0.31; p = .02). ASiR-ABD showed a better overall image quality (p = .001), but two of the three readers preferred DLIR more frequently. With < 50% of the radiation dose, DLIR chest CT showed comparable image quality in the upper abdomen to that of dedicated abdominal CT and was preferred by most readers. • With < 50% radiation dose, a deep learning algorithm applied to contrast-enhanced chest CT exhibited better image noise and signal-to-noise ratio than standard abdominal CT with the ASiR technique. • Pooled readers mostly preferred deep learning algorithm-reconstructed contrast-enhanced chest CT reconstructed using a standard ASiR-reconstructed abdominal CT. • Reconstruction algorithm-induced distortion artifacts were more frequently observed on deep learning algorithm-reconstructed images, but diagnostic difficulty was reported in only 0.3% of cases.

Image quality of ultralow-dose chest CT using deep learning techniques: potential superiority of vendor-agnostic post-processing over vendor-specific techniques.

To compare the image quality between the vendor-agnostic and vendor-specific algorithms on ultralow-dose chest CT. Vendor-agnostic deep learning post-processing model (DLM), vendor-specific deep learning image reconstruction (DLIR, high level), and adaptive statistical iterative reconstruction (ASiR, 70%) algorithms were employed. One hundred consecutive ultralow-dose noncontrast CT scans (CTDIvol; mean, 0.33 ± 0.056 mGy) were reconstructed with five algorithms: DLM-stnd (standard kernel), DLM-shrp (sharp kernel), DLIR, ASiR-stnd, and ASiR-shrp. Three thoracic radiologists blinded to the reconstruction algorithms reviewed five sets of 100 images and assessed subjective noise, spatial resolution, distortion artifact, and overall image quality. They selected the most preferred algorithm among five image sets for each case. Image noise and signal-to-noise ratio were measured. Edge-rise-distance was measured at a pulmonary vessel, i.e., the distance between two points where attenuation was 10% and 90% of maximal intravascular intensity. The skewness of attenuation was calculated in homogeneous areas. DLM-stnd, followed by DLIR, showed the best subjective noise on both lung and mediastinal windows, while DLIR yielded the least measured noise (ps < .0001). Compared to DLM-stnd, DLIR showed inferior subjective spatial resolution on lung window and higher edge-rise-distance (ps < .0001). Additionally, DLIR showed the most frequent distortion artifacts and deviated skewness (ps < .0001). DLM-stnd scored the best overall image quality, followed by DLM-shrp and DLIR (mean score 3.89 ± 0.19, 3.68 ± 0.24, and 3.53 ± 0.33; ps < .001). Two among three readers preferred DLM-stnd on both windows. Although DLIR provided the best quantitative noise profile, DLM-stnd showed the best overall image quality with fewer artifacts and was preferred by two among three readers. • A vendor-agnostic deep learning post-processing algorithm applied to ultralow-dose chest CT exhibited the best image quality compared to vendor-specific deep learning algorithm and ASiR techniques. • Two out of three readers preferred a vendor-agnostic deep learning post-processing algorithm in comparison to vendor-specific deep learning algorithm and ASiR techniques. • A vendor-specific deep learning reconstruction algorithm yielded the least image noise, but showed significantly more frequent specific distortion artifacts and increased skewness of attenuation compared to a vendor-agnostic algorithm.

Influence of CBCT-based volumetric distortion and beam hardening artefacts on the assessment of root canal filling quality in isthmus-containing molars.

To evaluate the influence of artefacts in cone beam CT (CBCT) images of filled root canals in isthmus-containing molars. 10 teeth presenting canals with an isthmus were instrumented and filled with a thermoplasticised obturation technique. The teeth were scanned using a micro-CT device and two CBCT devices: 3D Accuitomo 170 (ACC) and NewTom VGi evo (NT), with different acquisition protocols: larger and smaller voxel size. Three examiners assessed the CBCT images for: (1) detection of filling voids; (2) assessment of under- or overestimation of the filling material and (3) resemblance of CBCT images to the reference standard. Analyses of Task 1 yielded accuracy, sensitivity and specificity for detection of filling voids. For tasks 2 and 3, statistical analysis was performed using Wilcoxon test. The level of significance was set at p < .05. For Task 1, ACC showed higher sensitivity, whereas NT presented higher specificity. No significant difference was found between the protocols in ACC, however, for NT, differences between protocols were significant for all diagnostic values. In Task 2, visualisation of the filling was overestimated for NT, while for ACC, underestimation was observed. For Task 3, images with smaller voxel size were more similar to the reference image, for both CBCT devices. Different artefacts compromise the detection of filling voids on CBCT images of canals in mandibular molars with isthmus. ACC and NT present rather similar diagnostic accuracy, even though artefact expression remains device-specific.