Artifact Level Research Articles

Excluding Echo Shift Noise in Real-Time Pulse-Echo Speed-of-Sound Imaging

Computed ultrasound tomography in echo mode (CUTE) allows real-time imaging of the tissue speed of sound (SoS) using handheld ultrasound. The SoS is retrieved by inverting a forward model that relates the spatial distribution of the tissue SoS to echo shift maps detected between varying transmit and receive angles. Despite promising results, in vivo SoS maps often show artifacts due to elevated noise in echo shift maps. To minimize artifacts, we propose a technique where an individual SoS map is reconstructed for each echo shift map separately, as opposed to a single SoS map from all echo shift maps simultaneously. The final SoS map is then obtained as a weighted average over all SoS maps. Due to the partial redundancy between different angle combinations, artifacts that appear only in a subset of the individual maps can be excluded via the averaging weights. We investigate this real-time capable technique in simulations using two numerical phantoms, one with a circular inclusion and one with two layers. Our results demonstrate that the SoS maps reconstructed using the proposed technique are equivalent to the ones using simultaneous reconstruction when considering uncorrupted data but show significantly reduced artifact level for data that are corrupted by noise.

Creative ambivalence: implementing need‐solution pairs in household 3D printing

Household sector innovators often create solutions to address their own personal needs, but while need‐solution pairs can have important functions in creativity and design, the solutions that are initially devised as part of a working prototype are likely to be imperfect. This presents an adoption‐related puzzle that revolves around how an imperfect solution created to meet a unique personal need can subsequently be used by others. I develop a multilevel model to propose that creative ambivalence is an in‐between state for describing how mixed emotions, which capture reflective accounts in the creation of need‐solution pairs, can influence implementation. Using Thingiverse, a longitudinal dataset of designers in an online community, the designers' own reflective accounts and commentaries on their creative work, I find that creative ambivalence encourages implementation and that creative ambivalence at a personal level positively increases the implementation of need‐solution pairs in the production of household products, whereas ambivalence at an artefact level reveals no influence. In addition, solution adoption in follow‐on design derivatives accentuates the effect of creative ambivalence on implementation, so that the positive relationship between creative ambivalence and implementation is stronger when solution adoption is high. The multilevel model and results offer important theoretical contributions to research on affect and creativity and lead userness and practical implications for identifying lead users in online communities.

SPECIAL ISSUE: Exploring the Effects of Shared Heart-Focused Intention in Music Performance on Musicians and Audience Members

This exploratory study sought to determine if a shared group intention by members of a string quartet for two pieces of music would have an effect on themselves and audience members. An experimental design was implemented consisting of two interventions. The first intervention introduced a series of exercises focused on posture and biomechanical movements. The second intervention introduced HeartMath’s Quick Coherence and Heart Lock-In techniques. The Pachelbel Canon in D and Mozart Divertimenti in F K138 were chosen as the works to perform. For data collection, all participants (N = 12) were fitted with ambulatory Bodyguard heart rate variability (HRV) recorders. Qualitative data were collected with questionnaires, and videos recorded comments. All three sessions were video recorded. Repeated-measures analysis of variance was used to test within-subjects effects in baseline HRV measurements among three sessions. Audience participants (n = 8) and musician participants (n = 4) were analyzed as separate groups. Quantitative results showed a significant change in HRV among the musicians from baseline after the Heart Lock-In breathing technique was introduced. Qualitative results showed improved mental focus and feelings of connectivity among the musicians. Audience HRV data had a high level of artifact, and the results showed no significance. After the final performance, qualitative comments focused on the performances being more engaging and the musicians being more connected with each other.

A face-off of MRI research sequences by their need for de-facing

It is now widely known that research brain MRI, CT, and PET images may potentially be re-identified using face recognition, and this potential can be reduced by applying face-deidentification (“de-facing”) software. However, for research MRI sequences beyond T1-weighted (T1-w) and T2-FLAIR structural images, the potential for re-identification and quantitative effects of de-facing are both unknown, and the effects of de-facing T2-FLAIR are also unknown. In this work we examine these questions (where applicable) for T1-w, T2-w, T2*-w, T2-FLAIR, diffusion MRI (dMRI), functional MRI (fMRI), and arterial spin labelling (ASL) sequences. Among current-generation, vendor-product research-grade sequences, we found that 3D T1-w, T2-w, and T2-FLAIR were highly re-identifiable (96–98%). 2D T2-FLAIR and 3D multi-echo GRE (ME-GRE) were also moderately re-identifiable (44–45%), and our derived T2* from ME-GRE (comparable to a typical 2D T2*) matched at only 10%. Finally, diffusion, functional and ASL images were each minimally re-identifiable (0–8%). Applying de-facing with mri_reface version 0.3 reduced successful re-identification to ≤8%, while differential effects on popular quantitative pipelines for cortical volumes and thickness, white matter hyperintensities (WMH), and quantitative susceptibility mapping (QSM) measurements were all either comparable with or smaller than scan-rescan estimates. Consequently, high-quality de-facing software can greatly reduce the risk of re-identification for identifiable MRI sequences with only negligible effects on automated intracranial measurements. The current-generation echo-planar and spiral sequences (dMRI, fMRI, and ASL) each had minimal match rates, suggesting that they have a low risk of re-identification and can be shared without de-facing, but this conclusion should be re-evaluated if they are acquired without fat suppression, with a full-face scan coverage, or if newer developments reduce the current levels of artifacts and distortion around the face.

Estimating vibration artifacts in preclinical experimental assessment of actuator efficiency in bone-conduction hearing devices

ObjectivesTest feasibility of a means to distinguish artifact from relevant signal in an experimental method for pre-clinical assessment of bone conduction (BC) stimulation efficiency based on measurement of intracochlear pressure (ICP). MethodsExperiments were performed on fresh-frozen human temporal bones and cadaver heads. In a first step, fiber optic pressure sensors inserted into the cochlea through cochleostomies were intentionally vibrated to generate relative motion versus the stationary specimen, and the resulting ICP artifact recorded, before and after attaching the sensor fiber to the bone with glue. In a second step, BC stimulation was applied in the conventional location for a commercial bone anchored implant, as well as two alternative locations closer to the otic capsule. Again, ICP was recorded and compared with an estimated artifact, calculated from the previous measurements with intentional vibration of the fiber. ResultsIntentional vibration of the sensor fiber creates relative motion between fiber and bone, as intended, and causes an ICP signal. The stimulus does not create substantial promontory vibration, indicating that the measured ICP is all artifact, i.e. would not occur if the sensor were not in place. Fixating the sensor fiber to the bone with glue reduces the ICP artifact by at least 20 dB. BC stimulation also creates relative motion between sensor fiber and bone, as expected, from which an estimated ICP artifact level can be calculated. The ICP signal measured during BC stimulation is well above the estimated artifact, at least in some specimens and at some frequencies, indicating “real” cochlear stimulation, which would result in an auditory percept in a live subject. Stimulation at the alternative locations closer to the otic capsule appear to result in higher ICP (no statistical analysis performed), indicating a trend towards more efficient stimulation than at the conventional location. ConclusionsIntentional vibration of the fiber optic sensor for measurement of ICP can be used to derive an estimate of the artifact to be expected when measuring ICP during BC stimulation, and to characterize the effectiveness of glues or other means of reducing the artifact caused by relative motion of fiber and bone.

Abstract 5442: SlideQC: An AI-based tool for automated quality control of whole-slide digital pathology images

Abstract Introduction: Artifacts are often introduced during tissue collection and processing, slide preparation, and/or when generating whole slide images (WSI). The presence of artifacts has a negative impact on the digital pathology workflow as artifacts may hinder diagnostic reporting and can lead to false positive and false negative results when using image analysis algorithms or computer-aided diagnosis systems. Manual quality control of WSI is a time-consuming procedure and therefore automated quality control tools, which report and exclude artifacts, are highly desirable to streamline digital pathology workflows. To automate the quality control step, we developed SlideQC, an AI-based quality control tool that automatically detects, reports, and outlines artifacts such as air bubbles, dust/debris, folds, out-of-focus,and pen marks, in both research and clinical workflows. Methods: SlideQC was trained with a DenseNet-based network using 1984 annotations for artifacts including air bubbles, dust/debris, folds, out-of-focus, and pen markers, across 254 Haematoxylin and Eosin (H&E) stained WSI from more than 9 tissue types. A set of 2048 annotations from synthetically generated out-of-focus images was added to supplement the training data. The performance of the SlideQC was evaluated on an external test cohort of 49 WSI H&E images sourced from the open-source database ‘HistoQCRepo’, across 375 annotations (tissue and artifact), and compared with the performance of HistoQC, an open-source quality control tool for digital pathology slides. Results: On the external test cohort, SlideQC showed high sensitivity, specificity, and F1-score with average values of 0.93, 0.99, and 0.93, across the five artifact types. In the same cohort, HistoQC attained an average sensitivity, specificity, and F1-score of 0.65, 0.79, and 0.54, respectively. Conclusions: SlideQC achieved high sensitivity, specificity, and F1-score on an external test cohort. SlideQC can add efficiency gains to a workflow by performing quality control on 100% of slides rather than the currently manually performed on only a subset of the slides in clinical pathology departments. SlideQC can allowthe triaging and alerting of slides containing a high level of artifact within a digital pathology workflow. The tool can also be used to exclude the artifact region from downstream analysis by subsequent image analysis algorithms. Citation Format: Daniela Rodrigues, Stefan Reinhard, Therese Waldburger, Daniel Martin, Suzana Couto, Inti Zlobec, Peter Caie, Erik Burlingame. SlideQC: An AI-based tool for automated quality control of whole-slide digital pathology images. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5442.

Characterization of Effects of Compressed Sensing on High Spectral and Spatial Resolution (HiSS) MRI with Comparison to SENSE.

High Spectral and Spatial resolution (HiSS) MRI shows high diagnostic performance in the breast. Acceleration methods based on k-space undersampling could allow stronger T2*-based image contrast and/or higher spectral resolution, potentially increasing diagnostic performance. An agar/oil phantom was prepared with water-fat boundaries perpendicular to the readout and phase encoding directions in a breast coil. HiSS MRI was acquired at 3T, at sensitivity encoding (SENSE) acceleration factors R of up to 10, and the R = 1 dataset was used to simulate corresponding compressed sensing (CS) accelerations. Image quality was evaluated by quantifying noise and artifact levels. Effective spatial resolution was determined via modulation transfer function analysis. Dispersion vs. absorption (DISPA) analysis and full width at half maximum (FWHM) quantified spectral lineshape changes. Noise levels remained constant with R for CS but amplified with SENSE. SENSE preserved the spatial resolution of HiSS MRI, while CS reduced it in the phase encoding direction. SENSE showed no effect on FWHM or DISPA markers, while CS increased FWHM. Thus, CS might perform better in noise-limited or geometrically constrained applications, but in geometric configurations specific to breast MRI, spectral analysis might be compromised, decreasing the diagnostic performance of HiSS MRI.

Motion estimation based on projective information disentanglement for 3D reconstruction of rotational coronary angiography

The 2D projection space-based motion compensation reconstruction (2D-MCR) is a kind of representative method for 3D reconstruction of rotational coronary angiography owing to its high efficiency. However, due to the lack of accurate motion estimation of the overlapping projection pixels, existing 2D-MCR methods may still have a certain level of under-sampling artifacts or lose accuracy for cases with strong cardiac motion. To overcome this, in this study, we proposed a motion estimation approach based on projective information disentanglement (PID-ME) for 3D reconstruction of rotational coronary angiography. The reconstruction method adopts the framework of 2D-MCR, which is referred to as 2D-PID-MCR. The PID-ME consists of two parts: generation of the reference projection sequence based on the fast simplified distance driven projector (FSDDP) algorithm, motion estimation and correction based on the projective average minimal distance measure (PAMD) model. The FSDDP algorithm generates the reference projection sequence faster and accelerates the whole reconstruction greatly. The PAMD model can disentangle the projection information effectively and estimate the motion of both overlapping and non-overlapping projection pixels accurately. The main contribution of this study is the construction of 2D-PID-MCR to overcome the inherent limitations of the existing 2D-MCR method. Simulated and clinical experiments show that the PID-ME, consisting of FSDDP and PAMD, can estimate the motion of the projection sequence data accurately and efficiently. Our 2D-PID-MCR method outperforms the state-of-the-art approaches in terms of accuracy and real-time performance.

Calibrationless reconstruction of uniformly-undersampled multi-channel MR data with deep learning estimated ESPIRiT maps.

To develop a truly calibrationless reconstruction method that derives An Eigenvalue Approach to Autocalibrating Parallel MRI (ESPIRiT) maps from uniformly-undersampled multi-channel MR data by deep learning. ESPIRiT, one commonly used parallel imaging reconstruction technique, forms the images from undersampled MR k-space data using ESPIRiT maps that effectively represents coil sensitivity information. Accurate ESPIRiT map estimation requires quality coil sensitivity calibration or autocalibration data. We present a U-Net based deep learning model to estimate the multi-channel ESPIRiT maps directly from uniformly-undersampled multi-channel multi-slice MR data. The model is trained using fully-sampled multi-slice axial brain datasets from the same MR receiving coil system. To utilize subject-coil geometric parameters available for each dataset, the training imposes a hybrid loss on ESPIRiT maps at the original locations as well as their corresponding locations within the standard reference multi-slice axial stack. The performance of the approach was evaluated using publicly available T1-weighed brain and cardiac data. The proposed model robustly predicted multi-channel ESPIRiT maps from uniformly-undersampled k-space data. They were highly comparable to the reference ESPIRiT maps directly computed from 24 consecutive central k-space lines. Further, they led to excellent ESPIRiT reconstruction performance even at high acceleration, exhibiting a similar level of errors and artifacts to that by using reference ESPIRiT maps. A new deep learning approach is developed to estimate ESPIRiT maps directly from uniformly-undersampled MR data. It presents a general strategy for calibrationless parallel imaging reconstruction through learning from the coil and protocol-specific data.

The Nature of Metal Artifacts in X-ray Computed Tomography and Their Reduction by Optimization of Tomography Systems Parameters

A significant gap in the known methods for assessing the levels of metal artifacts in X-ray computed tomography and approaches to their reduction is an almost complete disregard for the physical nature of this artifact—the proximity to zero of radioscopic transparency. The proposed work fills this gap. A mathematical model has been developed for evaluating metal artifacts in X-ray computed tomography as applied to the geometry of a parallel beam. The simulation model was transformed into an algorithm, and a Mathcad program was designed to simulate images of the internal structure of the test objects. The algorithm for estimating the studied artifact includes the stages of generating sinograms and estimating the distributions of the linear coefficient over the sections of the object based on the back projection method with filtering. The efficiency of the metal artifacts simulation algorithm is demonstrated in the example of symmetric and asymmetric objects with low- and high-density inclusions and inclusions from materials with high atomic number values. The possibility of reducing metal artifacts with the help of a rational choice of the maximum energy of X-ray radiation and the ADC bit depth is illustrated. For example, for an aluminum cylinder 200 mm in diameter with a central cylindrical cavity 80 mm in diameter, cylindrical inserts 12 mm in diameter with material densities from 1.5 g/cm3 to 10 g/cm3, and effective atomic numbers of materials from 13 to 47, the numerical simulation method proved the following: it is practically unattainable to significantly reduce the level of metal artifacts by increasing the ADC capacity to the maximum X-ray energy of 160 keV; the desired effect is achieved by simultaneously increasing the maximum X-ray energy to 225 keV and the ADC capacity to 24 or 32; increasing the maximum X-ray energy from 160 keV to 225 keV leads to an increase in the energy absorbed in the material of the test object by 26%. The results of this research can be used at the design stage of X-ray computed tomography systems designed to control objects with fragments of low radiation transparency.

X-ray dark-field chest radiography: a reader study to evaluate the diagnostic quality of attenuation chest X-rays from a dual-contrast scanning prototype

ObjectivesTo compare the visibility of anatomical structures and overall quality of the attenuation images obtained with a dark-field X-ray radiography prototype with those from a commercial radiography system.MethodsEach of the 65 patients recruited for this study obtained a thorax radiograph at the prototype and a reference radiograph at the commercial system. Five radiologists independently assessed the visibility of anatomical structures, the level of motion artifacts, and the overall image quality of all attenuation images on a five-point scale, with 5 points being the highest rating. The average scores were compared between the two image types. The differences were evaluated using an area under the curve (AUC) based z-test with a significance level of p ≤ 0.05. To assess the variability among the images, the distributions of the average scores per image were compared between the systems.ResultsThe overall image quality was rated high for both devices, 4.2 for the prototype and 4.6 for the commercial system. The rating scores varied only slightly between both image types, especially for structures relevant to lung assessment, where the images from the commercial system were graded slightly higher. The differences were statistically significant for all criteria except for the bronchial structures, the cardiophrenic recess, and the carina.ConclusionsThe attenuation images acquired with the prototype were assigned a high diagnostic quality despite a lower resolution and the presence of motion artifacts. Thus, the attenuation-based radiographs from the prototype can be used for diagnosis, eliminating the need for an additional conventional radiograph.Key Points• Despite a low tube voltage (70 kVp) and comparably long acquisition time, the attenuation images from the dark-field chest radiography system achieved diagnostic quality for lung assessment.• Commercial chest radiographs obtained a mean rating score regarding their diagnostic quality of 4.6 out of 5, and the grating-based images had a slightly lower mean rating score of 4.2 out of 5.• The difference in rating scores for anatomical structures relevant to lung assessment is below 5%.

MP-RAVE: IR-Prepared T1 -Weighted Radial Stack-of-Stars 3D GRE imaging with retrospective motion correction.

To describe an inversion-recovery T1 -weighted radial stack-of-stars 3D gradient echo (GRE) sequence with comparable image quality to conventional MP-RAGE and to demonstrate how the radial acquisition scheme can be utilized for additional retrospective motion correction to improve robustness to head motion. The proposed sequence, named MP-RAVE, has been derived from a previously described radial stack-of-stars 3D GRE sequence (RAVE) and includes a 180° inversion recovery pulse that is generated once for every stack of radial views. The sequence is combined with retrospective 3D motion correction to improve robustness. The effectiveness has been evaluated in phantoms and healthy volunteers and compared to conventional MP-RAGE acquisition. MP-RAGE and MP-RAVE anatomical images were rated "good" to "excellent" in overall image quality, with artifact level between "mild" and "no artifacts", and with no statistically significant difference between methods. During head motion, MP-RAVE showed higher inherent robustness with artifacts confined to local brain regions. In combination with motion correction, MP-RAVE provided noticeably improved image quality during different head motion and showed statistically significant improvement in image sharpness. MP-RAVE provides comparable image quality and contrast to conventional MP-RAGE with improved robustness to head motion. In combination with retrospective 3D motion correction, MP-RAVE can be a useful alternative to MP-RAGE, especially in non-cooperative or pediatric patients.

Highly accelerated free-breathing real-time myocardial tagging for exercise cardiovascular magnetic resonance

BackgroundExercise cardiovascular magnetic resonance (Ex-CMR) myocardial tagging would enable quantification of myocardial deformation after exercise. However, current electrocardiogram (ECG)-segmented sequences are limited for Ex-CMR. MethodsWe developed a highly accelerated balanced steady-state free-precession real-time tagging technique for 3 T. A 12-fold acceleration was achieved using incoherent sixfold random Cartesian sampling, twofold truncated outer phase encoding, and a deep learning resolution enhancement model. The technique was tested in two prospective studies. In a rest study of 27 patients referred for clinical CMR and 19 healthy subjects, a set of ECG-segmented for comparison and two sets of real-time tagging images for repeatability assessment were collected in 2-chamber and short-axis views with spatiotemporal resolution 2.0 × 2.0 mm2 and 29 ms. In an Ex-CMR study of 26 patients with known or suspected cardiac disease and 23 healthy subjects, real-time images were collected before and after exercise. Deformation was quantified using measures of short-axis global circumferential strain (GCS). Two experienced CMR readers evaluated the image quality of all real-time data pooled from both studies using a 4-point Likert scale for tagline quality (1-excellent; 2-good; 3-moderate; 4-poor) and artifact level (1-none; 2-minimal; 3-moderate; 4-significant). Statistical evaluation included Pearson correlation coefficient (r), intraclass correlation coefficient (ICC), and coefficient of variation (CoV). ResultsIn the rest study, deformation was successfully quantified in 90% of cases. There was a good correlation (r = 0.71) between ECG-segmented and real-time measures of GCS, and repeatability was good to excellent (ICC = 0.86 [0.71, 0.94]) with a CoV of 4.7%. In the Ex-CMR study, deformation was successfully quantified in 96% of subjects pre-exercise and 84% of subjects post-exercise. Short-axis and 2-chamber tagline quality were 1.6 ± 0.7 and 1.9 ± 0.8 at rest and 1.9 ± 0.7 and 2.5 ± 0.8 after exercise, respectively. Short-axis and 2-chamber artifact level was 1.2 ± 0.5 and 1.4 ± 0.7 at rest and 1.3 ± 0.6 and 1.5 ± 0.8 post-exercise, respectively. ConclusionWe developed a highly accelerated real-time tagging technique and demonstrated its potential for Ex-CMR quantification of myocardial deformation. Further studies are needed to assess the clinical utility of our technique.

High-resolution motion- and phase-corrected functional MRI at 7 T using shuttered multishot echo-planar imaging.

To achieve high-resolution multishot echo-planar imaging (EPI) for functional MRI (fMRI) with reduced sensitivity to in-plane motion and between-shot phase variations. Two-dimensional radiofrequency pulses were incorporated in a multishot EPI sequence at 7T which selectively excited a set of in-plane bands (shutters) in the phase encoding direction, which moved between shots to cover the entire slice. A phase- and motion-corrected reconstruction was implemented for the acquisition. Brain imaging experiments were performed with instructed motion to evaluate image quality for conventional multishot and shuttered EPI. Temporal stability was assessed in three subjects by quantifying temporal SNR (tSNR) and artifact levels, and fMRI activation experiments using visual stimulation were performed to assess the strength and distribution of activation, using both conventional multishot and shuttered EPI. In the instructed motion experiment, ghosting was lower in shuttered EPI images without or with corrections and image quality metrics were improved with motion correction. tSNR was improved by phase correction in both conventional multishot and shuttered EPI and the acquisitions had similar tSNR without and with phase correction. However, while phase correction was necessary to maximize tSNR in conventional multishot EPI, it also increased intermittent ghosting, but did not increase intermittent ghosting in shuttered EPI. Phase correction increased activation strength in both conventional multishot and shuttered EPI, but caused increased spurious activation outside the brain and in frontal brain regions in conventional multishot EPI. Shuttered EPI supports multishot segmented EPI acquisitions with lower sensitivity to artifacts from motion for high-resolution fMRI.

Heartbeat detection from single-lead ECG contaminated with simulated EMG at different intensity levels: A comparative study

Electrocardiogram (ECG) signals quality can be seriously affected by artifacts such as electromyogram (EMG), negatively impacting its clinical value. Various denoising methods have been proposed to address this issue in the literature. We examined how simulated EMG artifacts affect the heartbeat detection performance of different ECG denoising pipelines at four EMG intensity levels (N1,…,N4), by adding simulated EMG to ECG signals from two different datasets: MIT-BIH arrhythmia dataset (dataset 1) and data we collected from 20 healthy participants (dataset 2). Five different pipelines were investigated: CEEMDAN (Cd), CEEMDAN-ICA (Cd-I), SWT(WT), CEEMDAN-SWT (Cd-W), and CEEMDAN-ICA-SWT (Cd-I-W). For dataset 1, Cd and WT provided the best heartbeat detection performance at noise levels N1–N2 and N3–N4, respectively. For dataset 2, WT and Cd_I performed best at N1, while WT performed best at N2–N4. We further investigated the robustness of the pipelines against varying EMG intensity. We observed that the pipelines involving SWT (WT, Cd-W, Cd-I-W) considerably outperformed others in robustness against noise. Cd-I-W and WT were the most robust pipelines for dataset 1 and 2, respectively. As such, the potential artifact level in the environment of ECG should be assessed for selecting an appropriate denoising method.

View-sharing for 4D magnetic resonance imaging with randomized projection-encoding enables improvements of respiratory motion imaging for treatment planning in abdominothoracic radiotherapy.

The accuracy and precision of radiation therapy are dependent on the characterization of organ-at-risk and target motion. This work aims to demonstrate a 4D magnetic resonance imaging (MRI) method for improving spatial and temporal resolution in respiratory motion imaging for treatment planning in abdominothoracic radiotherapy. The spatial and temporal resolution of phase-resolved respiratory imaging is improved by considering a novel sampling function based on quasi-random projection-encoding and peripheral k-space view-sharing. The respiratory signal is determined directly from k-space, obviating the need for an external surrogate marker. The average breathing curve is used to optimize spatial resolution and temporal blurring by limiting the extent of data sharing in the Fourier domain. Improvements in image quality are characterized by evaluating changes in signal-to-noise ratio (SNR), resolution, target detection, and level of artifact. The method is validated in simulations, in a dynamic phantom, and in-vivo imaging. Sharing of high-frequency k-space data, driven by the average breathing curve, improves spatial resolution and reduces artifacts. Although equal sharing of k-space data improves resolution and SNR in stationary features, phases with large temporal changes accumulate significant artifacts due to averaging of high frequency features. In the absence of view-sharing, no averaging and detection artifacts are observed while spatial resolution is degraded. The use of a quasi-random sampling function, with view-sharing driven by the average breathing curve, provides a feasible method for self-navigated 4D-MRI at improved spatial resolution.

Open Educational Practices

Open educational practices (OEP) is an umbrella term that includes the creation, use, and reuse of open educational resources (OER); pedagogical practices encouraging peer learning, collaborative knowledge creation, sharing, and empowerment of learners; and systemic and structural initiatives to support and embed openness. The underlying values of OEP match those of open education more broadly, i.e. enabling educational access, ensuring inclusivity, and furthering equity. Examples of OEP include using OER, renewable/non-disposable assignments (where students publish work openly), collaborative annotation, Wikipedia editing, open courses, and engaging in open learning/teaching communities, among many others. Some people use the terms 'OEP' and 'open pedagogy' interchangeably, while others consider OEP to be a broader concept, inclusive of open pedagogy, as the latter focuses primarily on teaching practices (see Open Pedagogy). OEP can be enacted at the level of individual artifacts, modules or programs (via OER, open pedagogy, open textbooks, open learning design) as well as systemically across institutional structures (via open education policies, open publishing practices, reward/recognition structures). Recent OEP research focuses on the importance of critical and social justice approaches, reflecting wider trends in digital and higher education. Such approaches acknowledge the importance of context and power relations and encourage diverse, inclusive, and equitable approaches to openness.

Quantification of Artifacts and Image Distortions in 1.5 Tesla Magnetic Resonance Images of a Commercial Multi-Channel Vaginal Cylinder Brachytherapy Applicator Set.

The BEBIG Portio multi-channel applicator provides better target dose coverage and sparing organs-at-risk compared to a single-channel cylinder. However, artifacts and distortions of Portio in magnetic resonance images (MRI) have not yet been reported. We aimed to quantify the artifacts and distortions in its 1.5-Tesla MR images before clinical use. In this experimental study, we employed a gelatin-filled phantom to conduct our measurements. T2-weighted (T2W) images were examined for artifacts and distortions. Computed tomography (CT) images were used as a reference to assess image distortions. Artifact severity was measured by recording the full-width-at-half-maximum (FWHM) image pixel values at various positions along the length of the applicator/channels. CT and MRI-based applicator reconstruction accuracy were then compared, and signal-to-noise ratio (SNR) and contrast were also determined for the applicator images. The applicator distortion level for the Portio applicator was less than the image spatial resolution (0.5±0.5 pixels). The average FWHM for the tandem applicator images was 5.23±0.39 mm, while it was 3.21±0.37 mm for all channels (compared to their actual diameters of 5.0 mm and 3.0 mm, respectively). The average applicator reconstruction difference between CT and MR images was 0.75±0.30 mm overall source dwell positions. The image SNR and contrast were both acceptable. These findings indicate that the Portio applicator has a satisfactory low level of artifacts and image distortions in 1.5-Tesla, T2W images. It may, therefore, be a promising option for MRI-guided multi-channel vaginal brachytherapy.

Interactive Visualization of Large Point Clouds Using an Autotuning Multiresolution Out-Of-Core Strategy

Abstract Due to the increasingly large amount of data acquired into point clouds, from LiDAR (Light Detection and Ranging) sensors and 2D/3D sensors, massive point clouds processing has become a topic with high interest for several fields. Current client-server applications usually use multiresolution out-of-core proposals; nevertheless, the construction of the data structures required is very time-consuming. Furthermore, these multiresolution approaches present problems regarding point density changes between different levels of detail and artifacts due to the rendering of elements entering and leaving the field of view. We present an autotuning multiresolution out-of-core strategy to avoid these problems. Other objectives are reducing loading times while maintaining low memory requirements, high visualization quality and achieving interactive visualization of massive point clouds. This strategy identifies certain parameters, called performance parameters, and defines a set of premises to obtain the goals mentioned above. The optimal parameter values depend on the number of points per cell in the multiresolution structure. We test our proposal in our web-based visualization software designed to work with the structures and storage format used and display massive point clouds achieving interactive visualization of point clouds with more than 27 billion points.

Wavelet subband discriminator for efficient unsupervised chest X-ray image restoration.

Chest X-ray (CXR) images are commonly used to show the internal structure of the human body without invasive intervention. The quality of CXR is an important factor as it affects the accuracy of a clinical diagnosis. Unfortunately, it is difficult to always get good quality CXR scans due to noises andscatters. Recently, wavelet directional CycleGAN (WavCycleGAN) has shown promising results in image restoration tasks by removing noise and artifacts without sacrificing high-frequency components of the input image. Unfortunately, WavCycleGAN directly reconstructs wavelet directional images that require a wavelet transform in both the training and test phases, resulting in additional processing steps and unnatural artifacts originating from the wavelet domain image. In addition, WavCycleGAN can only process artifact-related subbands, so it is difficult to apply WavCycleGAN when different levels of artifacts are present in all subbands. To address this, here we present a novel unsupervised CXR image restoration scheme with similar or even better artifact removal performance than WavCycleGAN in spite of wavelet transform being only applied in the trainingphase. We introduce a novel wavelet subband discriminator which can be combined with CycleGAN or switchable CycleGAN, where wavelet transform is applied only in the training phase for discriminators to match the distribution of wavelet subband components. In our framework, the image restoration network can be still applied in the image domain to prevent unnatural artifacts of the wavelet domain image with the help of the image-domain cycle-consistency loss. In addition, using wavelet subband discriminator makes it possible to remove artifacts in all subbands by utilizing frequency-specific wavelet subbanddiscriminators. Through extensive experiments for noise and scatter removal in CXRs, we confirm that our method provides competitive performance compared to existing approaches without additional processing steps in the test phase. Furthermore, we show that our wavelet subband discriminator combined with the switchable CycleGAN can provide the flexibility by generating different levels of artifact removal. The proposed wavelet subband discriminator can be combined with the existing CycleGAN or switchable CycleGAN structures to construct an efficient unsupervised CXR image reconstruction. The advantage of our wavelet subband discriminator-based CXR image restoration is that, unlike traditional WavCycleGAN, it does not require any additional processing steps in the testing phase and does not generate unnatural artifacts originating from the wavelet domain image. We believe that our wavelet subband discriminator can be applied to various CXR imageapplications.