Influence of labeling parameters and respiratory motion on velocity-selective arterial spin labeling for renal perfusion imaging

Purpose

Arterial transit time uncertainties and challenges during planning are potential issues for renal perfusion measurement using spatially selective arterial spin labeling techniques. To mitigate these potential issues, a spatially non‐selective technique, such as velocity‐selective arterial spin labeling (VSASL), could be an alternative. This article explores the influence of VSASL sequence parameters and respiratory induced motion on VS‐label generation.

Methods

VSASL data were acquired in human subjects (n = 15), with both single and dual labeling, during paced‐breathing, while essential sequence parameters were systematically varied; (1) cutoff velocity, (2) labeling gradient orientation and (3) post‐labeling delay (PLD). Pseudo‐continuous ASL was acquired as a spatially selective reference. In an additional free‐breathing single VSASL experiment (n = 9) we investigated respiratory motion influence on VS‐labeling. Absolute renal blood flow (RBF), perfusion weighted signal (PWS), and temporal signal‐to‐noise ratio (tSNR) were determined.

Results

(1) With decreasing cutoff velocity, tSNR and PWS increased. However, undesired tissue labeling occurred at low cutoff velocities (≤ 5.4 cm/s). (2) Labeling gradient orientation had little effect on tSNR and PWS. (3) For single VSASL high signal appeared in the kidney pedicle at PLD < 800 ms, and tSNR and PWS decreased with increasing PLD. For dual VSASL, maximum tSNR occurred at PLD = 1200 ms. Average cortical RBF measured with dual VSASL (264 ± 34 mL/min/100 g) at a cutoff velocity of 5.4 cm/s, and feet‐head labeling was slightly lower than with pseudo‐continuous ASL (283 ± 55 mL/min/100 g).

Conclusion

With well‐chosen sequence parameters, tissue labeling induced by respiratory motion can be minimized, allowing to obtain good quality RBF maps using planning‐free labeling with dual VSASL.

Intelligent 4D CT sequence scanning (i4DCT): First scanner prototype implementation and phantom measurements of automated breathing signal-guided 4D CT

PURPOSE

Four-dimensional (4D) computed tomography (CT) imaging is an essential part of current 4D radiotherapy treatment planning workflows, but clinical 4D CT images are often affected by artifacts. The artifacts are mainly caused by breathing irregularity during data acquisition, which leads to projection data coverage issues for currently available commercial 4D CT protocols. It was proposed to improve projection data coverage by online respiratory signal analysis and signal-guided CT tube control, but related work was always theoretical and presented as pure in silico studies. The present work demonstrates a first CT prototype implementation along with respective phantom measurements for the recently introduced intelligent 4D CT (i4DCT) sequence scanning concept (https://doi.org/10.1002/mp.13632).

METHODS

Intelligent 4D CT was implemented on the Siemens SOMATOM go platform. Four-dimensional CT measurements were performed using the CIRS motion phantom. Motion curves were programmed to systematically vary from regular to very irregular, covering typical irregular patterns that are known to result in image artifacts using standard 4D CT imaging protocols. Corresponding measurements were performed using i4DCT and routine spiral 4D CT with similar imaging parameters (e.g., mAs setting and gantry rotation time, retrospective ten-phase reconstruction) to allow for a direct comparison of the image data.

RESULTS

Following technological implementation of i4DCT on the clinical CT scanner platform, 4D CT motion artifacts were significantly reduced for all investigated levels of breathing irregularity when compared to routine spiral 4D CT scanning.

CONCLUSIONS

The present study confirms feasibility of fully automated respiratory signal-guided 4D CT scanning by means of a first implementation of i4DCT on a CT scanner. The measurements thereby support the conclusions of respective in silico studies and demonstrate that respiratory signal-guided 4D CT (here: i4DCT) is ready for integration into clinical CT scanners.

Clinical evaluation of 4D MRI in the delineation of gross and internal tumor volumes in comparison with 4DCT

Purpose

To evaluate clinical utility of respiratory‐correlated (RC) four‐dimensional magnetic resonance imaging (4DMRI) for lung tumor delineation and motion assessment, in comparison with the current clinical standard of 4D computed tomography (4DCT).

Methods and Materials

A prospective T2‐weighted (T2w) RC‐4DMRI technique was applied to acquire coronal 4DMRI images for 14 lung cancer patients (16 lesions) during free breathing (FB) under an IRB‐approved protocol, together with a breath‐hold (BH) T1w 3DMRI and axial 4DMRI. Clinical simulation CT and 4DCT were acquired within 2 h. An internal navigator was applied to trigger amplitude‐binned 4DMRI acquisition whereas a bellows or real‐time position management (RPM) was used in the 4DCT reconstruction. Six radiation oncologists manually delineated the gross and internal tumor volumes (GTV and ITV) in 399 3D images using programmed clinical workflows under a tumor delineation guideline. The ITV was the union of GTVs within the breathing cycle without margin. Average GTV and motion range were assessed and ITV variation between 4DMRI and 4DCT was evaluated using the Dice similarity index, mean distance agreement (MDA), and volume difference.

Results

The mean tumor volume is similar between 4DCT (GTV^4DCT = 1.0, as the reference) and T2w‐4DMRI (GTV^T2wMR = 0.97), but smaller in T1w MRI (GTV^T1wMR = 0.76), suggesting possible peripheral edema around the tumor. Average GTV variation within the breathing cycle (22%) in 4DMRI is slightly greater than 4DCT (17%). GTV motion variation (−4 to 12 mm) and ITV variation (∆V^ITV=−25 to 95%) between 4DCT and 4DMRI are large, confirmed by relatively low ITV similarity (Dice = 0.72 ± 0.11) and large MDA = 2.9 ± 1.5 mm.

Conclusion

Average GTVs are similar between T2w‐4DMRI and 4DCT, but smaller by 25% in T1w BH MRI. Physician training and breathing coaching may be necessary to reduce ITV variability between 4DMRI and 4DCT. Four‐dimensional magnetic resonance imaging is a promising and viable technique for clinical lung tumor delineation and motion assessment.

Motion Artifact Suppression for Insulated EMG to Control Myoelectric Prostheses

Myoelectric prostheses help amputees to regain independence and a higher quality of life. These prostheses are controlled by electromyography, which measures an electrical signal at the skin surface during muscle contractions. In this contribution, the electromyography is measured with innovative flexible insulated sensors, which separate the skin and the sensor area by a dielectric layer. Electromyography sensors, and biosignal sensors in general, are striving for higher robustness against motion artifacts, which are a major obstacle in real-world environment. The motion artifact suppression algorithms presented in this article, prevent the activation of the prosthesis drive during artifacts, thereby achieving a substantial performance boost. These algorithms classify the signal into muscle contractions and artifacts. Therefore, new time domain features, such as Mean Crossing Rate are introduced and well-established time domain features (e.g., Zero-Crossing Rate, Slope Sign Change) are modified and implemented. Various artificial intelligence models, which require low calculation resources for an application in a wearable device, were investigated. These models are neural networks, recurrent neural networks, decision trees and logistic regressions. Although these models are designed for a low-power real-time embedded system, high accuracies in discriminating artifacts to contractions of up to 99.9% are achieved. The models were implemented and trained for fast response leading to a high performance in real-world environment. For highest accuracies, recurrent neural networks are suggested and for minimum runtime ( 0.99-1.15 μ s), decision trees are preferred.

A Wavelet Adaptive Cancellation Algorithm Based on Multi-Inertial Sensors for the Reduction of Motion Artifacts in Ambulatory ECGs

Wearable electrocardiogram (ECG) devices are universally used around the world for patients who have cardiovascular disease (CVD). At present, how to suppress motion artifacts is one of the most challenging issues in the field of physiological signal processing. In this paper, we propose an adaptive cancellation algorithm based on multi-inertial sensors to suppress motion artifacts in ambulatory ECGs. Firstly, this method collects information related to the electrode motion through multi-inertial sensors. Then, the part that is not related to the electrode motion is removed through wavelet transform, which improves the correlation of the reference input signal. In this way, the ability of the adaptive cancellation algorithm to remove motion artifacts is improved in the ambulatory ECG. Subsequent experimentation demonstrated that the wavelet adaptive cancellation algorithm based on multi-inertial sensors can effectively remove motion artifacts in ambulatory ECGs.

Pseudo-random Trajectory Scanning Suppresses Motion Artifacts on Gadoxetic Acid-enhanced Hepatobiliary-phase Magnetic Resonance Images

Purpose:

Hepatobiliary-phase (HBP) MRI with gadoxetic acid facilitates the differentiation between lesions with and without functional hepatocytes. Thus, high-quality HBP images are required for the detection and evaluation of hepatic lesions. However, the long scan time may increase artifacts due to intestinal peristalsis, resulting in the loss of diagnostic information. Pseudo-random acquisition order disperses artifacts into the background. The aim of this study was to investigate the clinical applicability of pseudo-random trajectory scanning for the suppression of motion artifacts on T₁-weighted images including HBP.

Methods:

Our investigation included computer simulation, phantom experiments, and a clinical study. For computer simulation and phantom experiments a region of interest (ROI) was placed on the area with motion artifact and the standard deviation inside the ROI was measured as image noise. For clinical study we subjected 62 patients to gadoxetic acid-enhanced hepatobiliary-phase imaging with a circular- and a pseudo-random trajectory (c-HBP and p-HBP); two radiologists graded the motion artifacts, sharpness of the liver edge, visibility of intrahepatic vessels, and overall image quality using a five-point scale where 1 = unacceptable and 5 = excellent. Differences in the qualitative scores were determined using the two-sided Wilcoxon signed-rank test.

Results:

The image noise was higher on the circular image compared with pseudo-random image (101.0 vs 60.9 on computer simulation image, 91.2 vs 67.7 on axial, 95.5 vs 86.9 on reformatted sagittal image for phantom experiments). For clinical study the score for motion artifacts was significantly higher with p-HBP than c-HBP imaging (left lobe: mean 3.4 vs 3.2, P < 0.01; right lobe: mean 3.6 vs 3.4, P < 0.01) as was the qualitative score for the overall image quality (mean 3.6 vs 3.3, P < 0.01).

Conclusion:

At gadoxetic acid-enhanced hepatobiliary-phase imaging, p-HBP scanning suppressed motion artifacts and yielded better image quality than c-HBP scanning.

Radiotherapy Planning and Molecular Imaging in Lung Cancer

INTRODUCTION

In patients suitable for radical chemoradiotherapy for lung cancer, 18F-FDGPET/ CT is a proposed management to improve the accuracy of high dose radiotherapy. However, there is a high rate of locoregional failure in patients with locally advanced non-small cell lung cancer (NSCLC), probably due to the fact that standard dosing may not be effective in all patients. The aim of the present review was to address some criticisms associated with the radiotherapy image-guided in NSCLC.

MATERIALS AND METHODS

A systematic literature search was conducted. Only published articles that met the following criteria were included: articles, only original papers, radiopharmaceutical ([18F]FDG and any tracer other than [18F]FDG), target, only specific for lung cancer radiotherapy planning, and experimental design (eventually "in vitro" studies were excluded). Peer-reviewed indexed journals, regardless of publication status (published, ahead of print, in press, etc.) were included. Reviews, case reports, abstracts, editorials, poster presentations, and publications in languages other than English were excluded. The decision to include or exclude an article was made by consensus and any disagreement was resolved through discussion.

RESULTS

Hundred eligible full-text articles were assessed. Diverse information is now available in the literature about the role of FDG and new alternative radiopharmaceuticals for the planning of radiotherapy in NSCLC. In particular, the role of alternative technologies for the segmentation of FDG uptake is essential, although indeterminate for RT planning. The pros and cons of the available techniques have been extensively reported.

CONCLUSION

PET/CT has a central place in the planning of radiotherapy for lung cancer and, in particular, for NSCLC assuming a substantial role in the delineation of tumor volume. The development of new radiopharmaceuticals can help overcome the problems related to the disadvantage of FDG to accumulate also in activated inflammatory cells, thus improving tumor characterization and providing new prognostic biomarkers.

Functional near-infrared spectroscopy for speech protocols: characterization of motion artifacts and guidelines for improving data analysis

Monitoring speech tasks with functional near-infrared spectroscopy (fNIRS) enables investigation of speech production mechanisms and informs treatment strategies for speech-related disorders such as stuttering. Unfortunately, due to movement of the temporalis muscle, speech production can induce relative movement between probe optodes and skin. These movements generate motion artifacts during speech tasks. In practice, spurious hemodynamic responses in functional activation signals arise from lack of information about the consequences of speech-related motion artifacts, as well as from lack of standardized processing procedures for fNIRS signals during speech tasks. To this end, we characterize the effects of speech production on fNIRS signals, and we introduce a systematic analysis to ameliorate motion artifacts. The study measured 50 healthy subjects performing jaw movement (JM) tasks and found that JM produces two different patterns of motion artifacts in fNIRS. To remove these unwanted contributions, we validate a hybrid motion-correction algorithm based sequentially on spline interpolation and then wavelet filtering. We compared performance of the hybrid algorithm with standard algorithms based on spline interpolation only and wavelet decomposition only. The hybrid algorithm corrected 94% of the artifacts produced by JM, and it did not lead to spurious responses in the data. We also validated the hybrid algorithm during a reading task performed under two different conditions: reading aloud and reading silently. For both conditions, we observed significant cortical activation in brain regions related to reading. Moreover, when comparing the two conditions, good agreement of spatial and temporal activation patterns was found only when data were analyzed using the hybrid approach. Overall, the study demonstrates a standardized processing scheme for fNIRS data during speech protocols. The scheme decreases spurious responses and intersubject variability due to motion artifacts.

Impact of movement and motion-artefact correction on image quality and interpretability in CBCT units with aligned and lateral-offset detectors

OBJECTIVES

To evaluate the impact of movement and motion-artefact correction systems on CBCT image quality and interpretability of simulated diagnostic tasks for aligned and lateral-offset detectors.

METHODS

A human skull simulating three diagnostic tasks (implant planning in the anterior maxilla, implant planning in the left-side-mandible and mandibular molar furcation assessment in the right-side-mandible) was mounted on a robot performing six movement types. Four CBCT units were used: Cranex 3Dx (CRA), Ortophos SL (ORT), Promax 3D Mid (PRO), and X1. Protocols were tested with aligned (CRA, ORT, PRO, and X1) and lateral-offset (CRA and PRO) detectors and two motion-artefact correction systems (PRO and X1). Movements were performed at one moment-in-time (t₁), for units with an aligned detector, and three moments-in-time (t₁-first-half of the acquisition, t₂-second-half, t₃-both) for the units with a lateral-offset detector. 98 volumes were acquired. Images were scored by three observers, blinded to the unit and presence of movement, for motion-related stripe artefacts, overall unsharpness, and interpretability. Fleiss' κ was used to assess interobserver agreement.

RESULTS

Interobserver agreement was substantial for all parameters (0.66-0.68). For aligned detectors, in all diagnostic tasks a motion-artefact correction system influenced image interpretability. For lateral-offset detectors, the interpretability varied according to the unit and moment-in-time, in which the movement was performed. PRO motion-artefact correction system was less effective for the offset detector than its aligned counterpart.

CONCLUSION

Motion-artefact correction systems enhanced image quality and interpretability for units with aligned detectors but were less effective for those with lateral-offset detectors.

Resting State fMRI: Going Through the Motions

Resting state functional magnetic resonance imaging (rs-fMRI) has become an indispensable tool in neuroscience research. Despite this, rs-fMRI signals are easily contaminated by artifacts arising from movement of the head during data collection. The artifacts can be problematic even for motions on the millimeter scale, with complex spatiotemporal properties that can lead to substantial errors in functional connectivity estimates. Effective correction methods must be employed, therefore, to distinguish true functional networks from motion-related noise. Research over the last three decades has produced numerous correction methods, many of which must be applied in combination to achieve satisfactory data quality. Subject instruction, training, and mild restraints are helpful at the outset, but usually insufficient. Improvements come from applying multiple motion correction algorithms retrospectively after rs-fMRI data are collected, although residual artifacts can still remain in cases of elevated motion, which are especially prevalent in patient populations. Although not commonly adopted at present, “real-time” correction methods are emerging that can be combined with retrospective methods and that promise better correction and increased rs-fMRI signal sensitivity. While the search for the ideal motion correction protocol continues, rs-fMRI research will benefit from good disclosure practices, such as: (1) reporting motion-related quality control metrics to provide better comparison between studies; and (2) including motion covariates in group-level analyses to limit the extent of motion-related confounds when studying group differences.

Combining Prospective Acquisition CorrEction (PACE) with retrospective correction to reduce motion artifacts in resting state fMRI data

BACKGROUND

Head movement in the scanner causes spurious signal changes in the blood-oxygen-level-dependent (BOLD) signal, confounding resting state functional connectivity (RSFC) estimates obtained from functional magnetic resonance imaging (fMRI). We examined the effectiveness of Prospective Acquisition CorrEction (PACE) in reducing motion artifacts in BOLD data.

METHODS

Using PACE-corrected RS-fMRI data obtained from 44 subjects and subdividing them into low- and high-motion cohorts, we investigated voxel-wise motion-BOLD relationships, the distance-dependent functional connectivity artifact and the correlation between head motion and connectivity metrics such as posterior cingulate seed-based connectivity and network degree centrality.

RESULTS

Our results indicate that, when PACE is used in combination with standard retrospective motion correction strategies, it provides two principal advantages over conventional echo-planar imaging (EPI) RS-fMRI data: (a) PACE was effective in eliminating significant negative motion-BOLD relationships, shown to be associated with signal dropouts caused by head motion, and (b) Censoring with a lower threshold (framewise displacement >0.5 mm) and a smaller window around the motion corrupted time point provided qualitatively equivalent reductions in the motion artifact with PACE when compared to a more conservative threshold of 0.2 mm required with conventional EPI data.

CONCLUSIONS

PACE when used in conjunction with retrospective motion correction methods including nuisance signal and motion parameter regression, and censoring, did prove effective in almost eliminating head motion artifacts, even with a lower censoring threshold. Use of a lower censoring threshold could provide substantial savings in data that would otherwise be lost to censoring. Three-dimensional PACE has negligible overhead in terms of scan time, sequence modifications or additional and hence presents an attractive option for head motion correction in high-throughput resting-state BOLD imaging.

Enabling free-breathing background suppressed renal pCASL using fat imaging and retrospective motion correction

Purpose

For free‐breathing renal perfusion imaging using arterial spin labeling (ASL), retrospective image realignment has been found essential to reduce subtraction artifacts and, independently, background suppression has been demonstrated to reduce physiologic noise. However, negative results on ASL precision and accuracy have been reported for the combination of both. In this study, the effect of background suppression ‐level in combination with image registration on free‐breathing renal ASL signal quality, with registration either on ASL‐images themselves or guided by additionally acquired fat‐images, was investigated. The results from free‐breathing acquisitions were compared with the reference paced‐breathing motion compensation strategy.

Methods

Pseudocontinuous ASL (pCASL) data with additional fat‐images were acquired from 10 subjects at 1.5T with varying background suppression levels during free‐breathing and paced‐breathing. Images were registered using the ASL‐images themselves (ASLReg) or using their corresponding fat‐images (FatReg). Temporal signal‐to‐noise ratio (tSNR) served to evaluate precision and perfusion weighted signal (PWS) to assess accuracy.

Results

In combination with image registration, background suppression significantly improved tSNR by 50% (P < .05). For heavy suppression, ASLReg and FatReg showed similar performance in terms of tSNR and PWS. Background suppression with two inversion pulses induced a small, nonsignificant (P > .05) PWS reduction, but increased PWS accuracy. When applying heavy background suppression, free‐breathing acquisitions resulted in similar ASL‐quality to paced‐breathing acquisitions.

Conclusion

Background suppression was found beneficial for free‐breathing renal pCASL precision without compromising accuracy, despite motion challenges. In combination with ASLReg or FatReg, background suppression enabled clinically viable free‐breathing renal pCASL.

Motion Artifact Reduction for Wrist-Worn Photoplethysmograph Sensors Based on Different Wavelengths

Long-term heart rate (HR) monitoring by wrist-worn photoplethysmograph (PPG) sensors enables the assessment of health conditions during daily life with high user comfort. However, PPG signals are vulnerable to motion artifacts (MAs), which significantly affect the accuracy of estimated physiological parameters such as HR. This paper proposes a novel modular algorithm framework for MA removal based on different wavelengths for wrist-worn PPG sensors. The framework uses a green PPG signal for HR monitoring and an infrared PPG signal as the motion reference. The proposed framework includes four main steps: motion detection, motion removal using continuous wavelet transform, approximate HR estimation and signal reconstruction. The proposed algorithm is evaluated against an electrocardiogram (ECG) in terms of HR error for a dataset of 6 healthy subjects performing 21 types of motion. The proposed MA removal method reduced the average error in HR estimation from 4.3, 3.0 and 3.8 bpm to 0.6, 1.0 and 2.1 bpm in periodic, random, and continuous non-periodic motion situations, respectively.

Whole-heart coronary MR angiography using a 3D cones phyllotaxis trajectory

PURPOSE

To develop a 3D cones steady-state free precession sequence with improved robustness to respiratory motion while mitigating eddy current artifacts for free-breathing whole-heart coronary magnetic resonance angiography.

METHOD

The proposed sequence collects cone interleaves using a phyllotaxis pattern, which allows for more distributed k-space sampling for each heartbeat compared to a typical sequential collection pattern. A Fibonacci number of segments is chosen to minimize eddy current effects with the trade-off of an increased number of acquisition heartbeats. For verification, phyllotaxis-cones is compared to sequential-cones through simulations, phantom studies, and in vivo coronary scans with 8 subjects using 2D image-based navigators for retrospective motion correction.

RESULTS

Simulated point spread functions and moving phantom results show less coherent motion artifacts for phyllotaxis-cones compared to sequential-cones. Assessment of the right and left coronary arteries using reader scores and the image edge profile acutance vessel sharpness metric indicate superior image quality and sharpness for phyllotaxis-cones.

CONCLUSION

Phyllotaxis 3D cones results in improved qualitative image scores and coronary vessel sharpness for free-breathing whole-heart coronary magnetic resonance angiography compared to standard sequential ordering when using a steady-state free precession sequence.

Rigid motion artifact reduction in CT using extended difference function

BACKGROUND

In computed tomography (CT), a patient motion would result in degraded spatial resolution and image artifacts.

OBJECTIVE

To eliminate motion artifacts, this study presents a method to estimate the motion parameters from sinograms based on extended difference function.

METHODS

Based on our previous work, we first divide the projection data into two parts according to view angles and take Radon transform. Then, we calculate the extended difference functions and search for the minimum points. The relative displacements can be determined by the minimum points, and the motion can be estimated by the relationships between the relative displacements and motion parameters. Finally, we introduce the estimated parameters into the reconstruction process to compensate for the motion effects.

RESULTS

The simulation results show that the running times can reduce by about 30% than our previous work. In phantom experiments, the relative mean rotation excursion (RMRE) and relative mean translation excursion (RMTE) of the new method are lower than the conventional Helgason-Ludwig consistency condition (HLCC) based method and comparable to our previous work. Compare with the HLCC method, the root mean square error (RMSE) of the new method also reduces, while the Pearson correlation coefficient (CC) and mean structural similarity index (MSSIM) increase.

CONCLUSIONS

The proposed new method yields the improved performance on accuracy of motion estimation with higher computational efficiency, and thus it can produce high-quality images.

Real-Time Processing of Two-Photon Calcium Imaging Data Including Lateral Motion Artifact Correction

Two-photon calcium imaging has been extensively used to record neural activity in the brain. It has been long used solely with post-hoc analysis, but the recent efforts began to include closed-loop experiments. Closed-loop experiments pose new challenges because they require fast, real-time image processing without iterative parameter tuning. When imaging awake animals, one of the crucial steps of post hoc image analysis is correction of lateral motion artifacts. In most of the closed-loop experiments, this step has not been implemented and ignored due to technical difficulties. We recently reported the first experiments with real-time processing of calcium imaging that included lateral motion correction. Here, we report the details of the implementation of fast motion correction and present performance analysis across several algorithms with different parameters. Additionally, we introduce a novel method to estimate baseline calcium signal using kernel density estimate, which reduces the number of parameters to be tuned. Combined, we propose a novel software pipeline of real-time image processing suited for closed-loop experiments. The pipeline is also useful for rapid post hoc image processing.

Enhanced Quantification of Retinal Perfusion by Improved Discrimination of Blood Flow From Bulk Motion Signal in OCTA

Purpose

Quantification of optical coherence tomography angiography (OCTA) is confounded by the prevalence of bulk motion. We have previously developed a regression-based bulk motion subtraction (rb-BMS) algorithm that estimates bulk motion velocity and corrects for its effect on flow signal. Here, we aim to investigate its ability to improve the reliability of capillary density (CD) quantification.

Methods

Two spectral-domain systems (70-kHz Avanti/AngioVue and 68-kHz Cirrus/AngioPlex) acquired 6 × 6-mm OCTA scans. The rb-BMS algorithm was applied on each OCTA volume. Regression analysis of angiographic versus reflectance signal of avascular A-lines in B-frames was used to set an optimized reflectance-adjusted threshold for discriminating vascular versus nonvascular voxels. The CD was calculated from en face maximum projections of the superficial vascular complex in macular scans and the nerve fiber layer plexus in disc scans, excluding large vessels. The retinal signal strength (RSS) was calculated by averaging the logarithmic-scale OCT reflectance signal, and its correlation with CD was investigated.

Results

Eight healthy eyes were scanned with each instrument on 2 separate days. The rb-BMS algorithm improved within-visit repeatability and between-visit reproducibility of CD compared with a global-threshold measurement algorithm. Using the rb-BMS algorithm, the CD results were less affected by RSS and the population variation was reduced. Motion-induced line artifacts were also reduced.

Conclusions

The rb-BMS algorithm improved the reliability of perfusion quantification in OCTA on both Food and Drug Administration-cleared spectral-domain OCTA systems.

Translational Relevance

The rb-BMS method helped reduce the inter-scan variability by generating accurate vessel maps, improving the reliability of retinal perfusion quantification.

A quantitative comparison of five optical coherence tomography angiography systems in clinical performance

AIM

To compare the clinical performance of 4 spectral-domain (SD) optical coherence tomography angiography (OCTA) systems: AngioVue™, AngioPlex™, Spectralis^® OCTA, AngioScan, and 1 swept-source (SS) OCTA SS OCT Angio™.

METHODS

Twenty-seven undilated right eyes of 27 participants underwent OCTA examination using five different systems respectively for both 3×3 and 6×6 mm² scan pattern (Spectralis OCTA for 3×3 mm² scan only). Image quality, including vessel valid visibility and the number of motion artifacts, and acquisition time were evaluated. Repeated measures analysis of variance (ANOVA) with Bonferroni's post-test and Friedman test with Dunn's post-test were used to compare measurements.

RESULTS

The age of the subjects was 28.19±5.55y (range, 23-49y). The spherical equivalent refraction was -2.55±1.84 D (range, 0.00 to -5.25 D). Significant difference was observed in the evaluation of vessel valid visibility (AngioVue the highest: 0.111±0.031 for 3×3 mm² scan and 0.128±0.020 for 6×6 mm² scan), number of motion artifacts (AngioVue the fewest: 0.778±1.086 for 3×3 mm² scan and 0.333±0.620 for 6×6 mm² scan) and acquisition time (AngioPlex the shortest: 8.537±1.921s for 3×3 mm² scan and 8.298±1.741s for 6×6 mm² scan; all P<0.001).

CONCLUSION

There is poor agreement of measurements among systems. AngioVue provides images with the highest vessel valid visibility and the fewest motion artifacts. AngioPlex achieves the shortest acquisition.

Utility of the luminance standard deviation to quantify magnetic resonance imaging motion artifact induced by tongue movement

Clear magnetic resonance imaging (MRI) is required to diagnose tongue cancer. However, the absence of occlusal support may cause tongue movements which are known to introduce artifacts on the MR image. This pilot study compared the manifest of artifacts from the tongue at rest and during motion using luminance standard deviation (LSD) to quantify the artifacts, in dentulous subjects. Participants were ten dentulous participants (5 males, 5 females; age 31.50 ± 8.38 years) with occlusal support. MRI was conducted with the tongue at rest and during lateral movement. The LSD was measured in the regions of interest (ROI) in the axial and sagittal planes. Tongue movement evoked unclear MR images, compared with the images taken at rest. Statistical analysis revealed that the LSD significantly differed between the tongue at rest and in motion in the axial (P = 0.004) and sagittal planes (ROI-A: P = 0.002, ROI-P: P = 0.006). These findings suggest that tongue movement introduces motion artifact and the LSD responds quantitatively to the magnitude of artifacts. Future studies will evaluate whether a prosthetic device used to provide occlusive support can decrease these artifacts when analyzed using LSD.

Abdominal DCE-MRI reconstruction with deformable motion correction for liver perfusion quantification

PURPOSE

Abdominal dynamic contrast-enhanced (DCE) MRI suffers from motion-induced artifacts that can blur images and distort contrast-agent uptake curves. For liver perfusion analysis, image reconstruction with rigid-body motion correction (RMC) can restore distorted portal-venous input functions (PVIF) to higher peak amplitudes. However, RMC cannot correct for liver deformation during breathing. We present a reconstruction algorithm with deformable motion correction (DMC) that enables correction of breathing-induced deformation in the whole abdomen.

METHODS

Raw data from a golden-angle stack-of-stars gradient-echo sequence were collected for 54 DCE-MRI examinations of 31 patients. For each examination, a respiratory motion signal was extracted from the data and used to reconstruct 21 breathing states from inhale to exhale. The states were aligned with deformable image registration to the end-exhale state. Resulting deformation fields were used to correct back-projection images before reconstruction with view sharing. Images with DMC were compared to uncorrected images and images with RMC.

RESULTS

DMC significantly increased the PVIF peak amplitude compared to uncorrected images (P << 0.01, mean increase: 8%) but not compared to RMC. The increased PVIF peak amplitude significantly decreased estimated portal-venous perfusion in the liver (P << 0.01, mean decrease: 8 ml/(100 ml·min)). DMC also removed artifacts in perfusion maps at the liver edge and reduced blurring of liver tumors for some patients.

CONCLUSIONS

DCE-MRI reconstruction with DMC can restore motion-distorted uptake curves in the abdomen and remove motion artifacts from reconstructed images and parameter maps but does not significantly improve perfusion quantification in the liver compared to RMC.

Adaptive Noise Reduction Algorithm to Improve R Peak Detection in ECG Measured by Capacitive ECG Sensors

Electrocardiograms (ECGs) can be conveniently obtained using capacitive ECG sensors. However, motion noise in measured ECGs can degrade R peak detection. To reduce noise, properties of reference signal and ECG measured by the sensors are analyzed and a new method of active noise cancellation (ANC) is proposed in this study. In the proposed algorithm, the original ECG signal at QRS interval is regarded as impulsive noise because the adaptive filter updates its weight as if impulsive noise is added. As the proposed algorithm does not affect impulsive noise, the original signal is not reduced during ANC. Therefore, the proposed algorithm can conserve the power of the original signal within the QRS interval and reduce only the power of noise at other intervals. The proposed algorithm was verified through comparisons with recent research using data from both indoor and outdoor experiments. The proposed algorithm will benefit a noise reduction of noisy biomedical signal measured from sensors.

Effects of Cable Sway, Electrode Surface Area, and Electrode Mass on Electroencephalography Signal Quality during Motion

More neuroscience researchers are using scalp electroencephalography (EEG) to measure electrocortical dynamics during human locomotion and other types of movement. Motion artifacts corrupt the EEG and mask underlying neural signals of interest. The cause of motion artifacts in EEG is often attributed to electrode motion relative to the skin, but few studies have examined EEG signals under head motion. In the current study, we tested how motion artifacts are affected by the overall mass and surface area of commercially available electrodes, as well as how cable sway contributes to motion artifacts. To provide a ground-truth signal, we used a gelatin head phantom with embedded antennas broadcasting electrical signals, and recorded EEG with a commercially available electrode system. A robotic platform moved the phantom head through sinusoidal displacements at different frequencies (0–2 Hz). Results showed that a larger electrode surface area can have a small but significant effect on improving EEG signal quality during motion and that cable sway is a major contributor to motion artifacts. These results have implications in the development of future hardware for mobile brain imaging with EEG.

Quantitative evaluation of the performance of different deformable image registration algorithms in helical, axial, and cone-beam CT images using a mobile phantom

The goal of this project is to investigate quantitatively the performance of different deformable image registration algorithms (DIR) with helical (HCT), axial (ACT), and cone-beam CT (CBCT). The variations in the CT-number values and lengths of well-known targets moving with controlled motion were evaluated. Four DIR algorithms: Demons, Fast-Demons, Horn-Schunck and Lucas-Kanade were used to register intramodality CT images of a mobile phantom scanned with different imaging techniques. The phantom had three water-equivalent targets inserted in a low-density foam with different lengths (10-40 mm) and moved with adjustable motion amplitudes (0-20 mm) and frequencies (0-0.5 Hz). The variations in the CT-number level, volumes and shapes of these targets were measured from the spread-out of the CT-number distributions. In CBCT, most of the DIR algorithms were able to produce the actual lengths of the mobile targets; however, the CT-number values obtained from the DIR algorithms deviated from the actual CT-number of the targets. In HCT, the DIR algorithms were successful in deforming the images of the mobile targets to the images of the stationary targets producing the CT-number values and lengths of the targets for motion amplitudes <20 mm. Similarly in ACT, all DIR algorithms produced the actual CT-number values and lengths of the stationary targets for low-motion amplitudes <15 mm. The optical flow-based DIR algorithms such as the Horn-Schunck and Lucas-Kanade performed better than the Demons and Fast-Demons that are based on attraction forces particularly at large motion amplitudes. In conclusion, most of the DIR algorithms did not reproduce well the CT-number values and lengths of the targets in images that have artifacts induced by large motion amplitudes. The deviations in the CT-number values and variations in the volume of the mobile targets in the deformed CT images produced by the different DIR algorithms need to be considered carefully in the treatment planning for accurate dose calculation dose coverage of the tumor, and sparing of critical structures.

Evaluation of motion artifact metrics for coronary CT angiography

PURPOSE

This study quantified the performance of coronary artery motion artifact metrics relative to human observer ratings. Motion artifact metrics have been used as part of motion correction and best-phase selection algorithms for Coronary Computed Tomography Angiography (CCTA). However, the lack of ground truth makes it difficult to validate how well the metrics quantify the level of motion artifact. This study investigated five motion artifact metrics, including two novel metrics, using a dynamic phantom, clinical CCTA images, and an observer study that provided ground-truth motion artifact scores from a series of pairwise comparisons.

METHOD

Five motion artifact metrics were calculated for the coronary artery regions on both phantom and clinical CCTA images: positivity, entropy, normalized circularity, Fold Overlap Ratio (FOR), and Low-Intensity Region Score (LIRS). CT images were acquired of a dynamic cardiac phantom that simulated cardiac motion and contained six iodine-filled vessels of varying diameter and with regions of soft plaque and calcifications. Scans were repeated with different gantry start angles. Images were reconstructed at five phases of the motion cycle. Clinical images were acquired from 14 CCTA exams with patient heart rates ranging from 52 to 82 bpm. The vessel and shading artifacts were manually segmented by three readers and combined to create ground-truth artifact regions. Motion artifact levels were also assessed by readers using a pairwise comparison method to establish a ground-truth reader score. The Kendall's Tau coefficients were calculated to evaluate the statistical agreement in ranking between the motion artifacts metrics and reader scores. Linear regression between the reader scores and the metrics was also performed.

RESULTS

On phantom images, the Kendall's Tau coefficients of the five motion artifact metrics were 0.50 (normalized circularity), 0.35 (entropy), 0.82 (positivity), 0.77 (FOR), 0.77(LIRS), where higher Kendall's Tau signifies higher agreement. The FOR, LIRS, and transformed positivity (the fourth root of the positivity) were further evaluated in the study of clinical images. The Kendall's Tau coefficients of the selected metrics were 0.59 (FOR), 0.53 (LIRS), and 0.21 (Transformed positivity). In the study of clinical data, a Motion Artifact Score, defined as the product of FOR and LIRS metrics, further improved agreement with reader scores, with a Kendall's Tau coefficient of 0.65.

CONCLUSION

The metrics of FOR, LIRS, and the product of the two metrics provided the highest agreement in motion artifact ranking when compared to the readers, and the highest linear correlation to the reader scores. The validated motion artifact metrics may be useful for developing and evaluating methods to reduce motion in Coronary Computed Tomography Angiography (CCTA) images.

Motion Artifact Quantification and Sensor Fusion for Unobtrusive Health Monitoring

Sensors integrated into objects of everyday life potentially allow unobtrusive health monitoring at home. However, since the coupling of sensors and subject is not as well-defined as compared to a clinical setting, the signal quality is much more variable and can be disturbed significantly by motion artifacts. One way of tackling this challenge is the combined evaluation of multiple channels via sensor fusion. For robust and accurate sensor fusion, analyzing the influence of motion on different modalities is crucial. In this work, a multimodal sensor setup integrated into an armchair is presented that combines capacitively coupled electrocardiography, reflective photoplethysmography, two high-frequency impedance sensors and two types of ballistocardiography sensors. To quantify motion artifacts, a motion protocol performed by healthy volunteers is recorded with a motion capture system, and reference sensors perform cardiorespiratory monitoring. The shape-based signal-to-noise ratio SNRS is introduced and used to quantify the effect on motion on different sensing modalities. Based on this analysis, an optimal combination of sensors and fusion methodology is developed and evaluated. Using the proposed approach, beat-to-beat heart-rate is estimated with a coverage of 99.5% and a mean absolute error of 7.9 ms on 425 min of data from seven volunteers in a proof-of-concept measurement scenario.