High-pitch CT pulmonary angiography (CTPA) with ultra-low contrast medium volume for the detection of pulmonary embolism: a comparison with standard CTPA.

OBJECTIVE: To investigate the feasibility and image quality of high-pitch CT pulmonary angiography (CTPA) with reduced iodine volume in normal weight patients. METHODS: In total, 81 normal weight patients undergoing CTPA for suspected pulmonary arterial embolism were retrospectively included: 41 in high-pitch mode with 20 mL of contrast medium (CM); and 40 with normal pitch and 50 mL of CM. Subjective image quality was assessed and rated on a 3-point scale. For objective image quality, attenuation and noise values were measured in all pulmonary arteries from the trunk to segmental level. Contrast-to-noise ratio (CNR) was calculated. Radiation dose estimations were recorded. RESULTS: There were no statistically significant differences in patient and scan demographics between high-pitch and standard CTPA. Subjective image quality was rated good to excellent in over 90% of all exams with no significant group differences (p = 0.32). Median contrast opacification was lower in high-pitch CTPA (283.18 [216.06-368.67] HU, 386.81 [320.57-526.12] HU; p = 0.0001). CNR reached a minimum of eight in all segmented arteries, but was lower in high-pitch CTPA (8.79 [5.82-12.42], 11.01 [9.19-17.90]; p = 0.005). Median effective dose of high-pitch CTPA was lower (1.04 [0.72-1.27] mSv/mGy·cm; 1.49 [1.07-2.05] mSv/mGy·cm; p < 0.0001). CONCLUSION: High-pitch CTPA using ultra-low contrast volume (20 mL) rendered diagnostic images for the detection of pulmonary arterial embolism in most instances. Compared to standard CTPA, the high-pitch CTPA exams with drastically reduced contrast medium volume had also concomitantly reduced radiation exposure. However, objective image quality of high-pitch CTPA was worse, though likely still within acceptable limits for confident diagnosis. CLINICAL RELEVANCE: This study provides valuable insights on the performance of a high-pitch dual-source CTPA protocol, offering potential benefits in reducing contrast medium and radiation dose while maintaining sufficient image quality for accurate diagnosis in patients suspected of pulmonary embolism. KEY POINTS: • High-pitch CT pulmonary angiography (CTPA) with ultra-low volume of contrast medium and reduced radiation dose renders diagnostic examinations with comparable subjective image quality to standard CTPA in most patients. • Objective image quality of high-pitch CTPA is reduced compared to standard CTPA, but contrast opacification and contrast-to-noise ratio remain above diagnostic thresholds. • Challenges of high-pitch CTPA may potentially be encountered in patients with severe heart failure or when performing a Valsalva maneuver during the examination.

Comparative evaluation of a surface-based respiratory monitoring system against a pressure sensor for 4DCT image reconstruction in phantoms.

Four-dimensional computed tomography (4DCT), which relies on breathing-induced motion, requires realistic surrogate information of breathing variations to reconstruct the tumor trajectory and motion variability of normal tissues accurately. Therefore, the SimRT surface-guided respiratory monitoring system has been installed on a Siemens CT scanner. This work evaluated the temporal and spatial accuracy of SimRT versus our commonly used pressure sensor, AZ-733 V. A dynamic thorax phantom was used to reproduce regular and irregular breathing patterns acquired by SimRT and Anzai. Various parameters of the recorded breathing patterns, including mean absolute deviations (MAD), Pearson correlations (PC), and tagging precision, were investigated and compared to ground-truth. Furthermore, 4DCT reconstructions were analyzed to assess the volume discrepancy, shape deformation and tumor trajectory. Compared to the ground-truth, SimRT more precisely reproduced the breathing patterns with a MAD range of 0.37 ± 0.27 and 0.92 ± 1.02 mm versus Anzai with 1.75 ± 1.54 and 5.85 ± 3.61 mm for regular and irregular breathing patterns, respectively. Additionally, SimRT provided a more robust PC of 0.994 ± 0.009 and 0.936 ± 0.062 for all investigated breathing patterns. Further, the peak and valley recognition were found to be more accurate and stable using SimRT. The comparison of tumor trajectories revealed discrepancies up to 7.2 and 2.3 mm for Anzai and SimRT, respectively. Moreover, volume discrepancies up to 1.71 ± 1.62% and 1.24 ± 2.02% were found for both Anzai and SimRT, respectively. SimRT was validated across various breathing patterns and showed a more precise and stable breathing tracking, (i) independent of the amplitude and period, (ii) and without placing any physical devices on the patient's body. These findings resulted in a more accurate temporal and spatial accuracy, thus leading to a more realistic 4DCT reconstruction and breathing-adapted treatment planning.

Advanced Necklace for Real-Time PPG Monitoring in Drivers.

Monitoring heart rate (HR) through photoplethysmography (PPG) signals is a challenging task due to the complexities involved, even during routine daily activities. These signals can indeed be heavily contaminated by significant motion artifacts resulting from the subjects' movements, which can lead to inaccurate heart rate estimations. In this paper, our objective is to present an innovative necklace sensor that employs low-computational-cost algorithms for heart rate estimation in individuals performing non-abrupt movements, specifically drivers. Our solution facilitates the acquisition of signals with limited motion artifacts and provides acceptable heart rate estimations at a low computational cost. More specifically, we propose a wearable sensor necklace for assessing a driver's well-being by providing information about the driver's physiological condition and potential stress indicators through HR data. This innovative necklace enables real-time HR monitoring within a sleek and ergonomic design, facilitating seamless and continuous data gathering while driving. Prioritizing user comfort, the necklace's design ensures ease of wear, allowing for extended use without disrupting driving activities. The collected physiological data can be transmitted wirelessly to a mobile application for instant analysis and visualization. To evaluate the sensor's performance, two algorithms for estimating the HR from PPG signals are implemented in a microcontroller: a modified version of the mountaineer's algorithm and a sliding discrete Fourier transform. The goal of these algorithms is to detect meaningful peaks corresponding to each heartbeat by using signal processing techniques to remove noise and motion artifacts. The developed design is validated through experiments conducted in a simulated driving environment in our lab, during which drivers wore the sensor necklace. These experiments demonstrate the reliability of the wearable sensor necklace in capturing dynamic changes in HR levels associated with driving-induced stress. The algorithms integrated into the sensor are optimized for low computational cost and effectively remove motion artifacts that occur when users move their heads.

Test Platform for Developing New Optical Position Tracking Technology towards Improved Head Motion Correction in Magnetic Resonance Imaging.

Optical tracking of head pose via fiducial markers has been proven to enable effective correction of motion artifacts in the brain during magnetic resonance imaging but remains difficult to implement in the clinic due to lengthy calibration and set up times. Advances in deep learning for markerless head pose estimation have yet to be applied to this problem because of the sub-millimetre spatial resolution required for motion correction. In the present work, two optical tracking systems are described for the development and training of a neural network: one marker-based system (a testing platform for measuring ground truth head pose) with high tracking fidelity to act as the training labels, and one markerless deep-learning-based system using images of the markerless head as input to the network. The markerless system has the potential to overcome issues of marker occlusion, insufficient rigid attachment of the marker, lengthy calibration times, and unequal performance across degrees of freedom (DOF), all of which hamper the adoption of marker-based solutions in the clinic. Detail is provided on the development of a custom moiré-enhanced fiducial marker for use as ground truth and on the calibration procedure for both optical tracking systems. Additionally, the development of a synthetic head pose dataset is described for the proof of concept and initial pre-training of a simple convolutional neural network. Results indicate that the ground truth system has been sufficiently calibrated and can track head pose with an error of <1 mm and <1°. Tracking data of a healthy, adult participant are shown. Pre-training results show that the average root-mean-squared error across the 6 DOF is 0.13 and 0.36 (mm or degrees) on a head model included and excluded from the training dataset, respectively. Overall, this work indicates excellent feasibility of the deep-learning-based approach and will enable future work in training and testing on a real dataset in the MRI environment.

Optical mapping of contracting hearts.

Optical mapping is a widely used tool to record and visualize the electrophysiological properties in a variety of myocardial preparations such as Langendorff-perfused isolated hearts, coronary-perfused wedge preparations, and cell culture monolayers. Motion artifact originating from the mechanical contraction of the myocardium creates a significant challenge to performing optical mapping of contracting hearts. Hence, to minimize the motion artifact, cardiac optical mapping studies are mostly performed on non-contracting hearts, where the mechanical contraction is removed using pharmacological excitation-contraction uncouplers. However, such experimental preparations eliminate the possibility of electromechanical interaction, and effects such as mechano-electric feedback cannot be studied. Recent developments in computer vision algorithms and ratiometric techniques have opened the possibility of performing optical mapping studies on isolated contracting hearts. In this review, we discuss the existing techniques and challenges of optical mapping of contracting hearts.

Simultaneous multislice EPI prospective motion correction by real-time receiver phase correction and coil sensitivity map interpolation.

PURPOSE: To improve the image reconstruction for prospective motion correction (PMC) of simultaneous multislice (SMS) EPI of the brain, an update of receiver phase and resampling of coil sensitivities are proposed and evaluated. METHODS: A camera-based system was used to track head motion (3 translations and 3 rotations) and dynamically update the scan position and orientation. We derived the change in receiver phase associated with a shifted field of view (FOV) and applied it in real-time to each k-space line of the EPI readout trains. Second, for the SMS reconstruction, we adapted resampled coil sensitivity profiles reflecting the movement of slices. Single-shot gradient-echo SMS-EPI scans were performed in phantoms and human subjects for validation. RESULTS: Brain SMS-EPI scans in the presence of motion with PMC and no phase correction for scan plane shift showed noticeable artifacts. These artifacts were visually and quantitatively attenuated when corrections were enabled. Correcting misaligned coil sensitivity maps improved the temporal SNR (tSNR) of time series by 24% (p = 0.0007) for scans with large movements (up to ˜35 mm and 30°). Correcting the receiver phase improved the tSNR of a scan with minimal head movement by 50% from 50 to 75 for a United Kingdom biobank protocol. CONCLUSION: Reconstruction-induced motion artifacts in single-shot SMS-EPI scans acquired with PMC can be removed by dynamically adjusting the receiver phase of each line across EPI readout trains and updating coil sensitivity profiles during reconstruction. The method may be a valuable tool for SMS-EPI scans in the presence of subject motion.

Comparison of Motion Grading in 1,000 Patients by First- and Second-Generation HR-pQCT: A Propensity Score Matched Cohort Study.

In-vivo bone microstructure measured by high-resolution peripheral quantitative computed tomography (HR-pQCT) is gaining importance in research and clinical practice. Second-generation HR-pQCT (XCT2) shows improved image quality and shorter measurement duration compared to the first generation (XCT1). Predicting and understanding the occurrence of motion artifacts is crucial for clinical practice. We retrospectively analyzed data from HR-pQCT measurements at the distal radius and tibia of 1,000 patients (aged 20 to 89) evenly distributed between both generations of HR-pQCT. Motion artifacts were graded between 1 (no motion) and 5 (severe motion), with grades greater 3 considered unusable. Additionally, baseline characteristics and patients' muscle performance and balance were measured. Various group comparisons between the two generations of HR-pQCT and regression analyses between patient characteristics and motion grading were performed. The study groups of XCT1 and XCT2 did not differ by age (XCT1: 64.9 vs. XCT2: 63.8 years, p = 0.136), sex (both 74.5% females, p > 0.999), or BMI (both 24.2 kg/m2, p = 0.911) after propensity score matching. XCT2 scans exhibited significantly lower motion grading in both extremities compared to XCT1 (Radius: p < 0.001; Tibia: p = 0.002). In XCT2 motion-corrupted scans were more than halved at the radius (XCT1: 35.3% vs. XCT2: 15.5%, p < 0.001), and at the tibia the frequency of best image quality scans was increased (XCT1: 50.2% vs. XCT2: 63.7%, p < 0.001). The strongest independent predictor for motion-corrupted images is the occurrence of high motion grading at the other scanning site during the same consultation. The association between high motion grading in one scan and a corresponding high motion grading in another scan within the same session suggests a non-resting patient. Additionally, aged, female, and patients with smaller stature tend towards higher motion grading, requiring special attention to a correct extremity fixation.

Motion artifact removal in coronary CT angiography based on generative adversarial networks.

OBJECTIVES: Coronary motion artifacts affect the diagnostic accuracy of coronary CT angiography (CCTA), especially in the mid right coronary artery (mRCA). The purpose is to correct CCTA motion artifacts of the mRCA using a GAN (generative adversarial network). METHODS: We included 313 patients with CCTA scans, who had paired motion-affected and motion-free reference images at different R-R interval phases in the same cardiac cycle and included another 53 CCTA cases with invasive coronary angiography (ICA) comparison. Pix2pix, an image-to-image conversion GAN, was trained by the motion-affected and motion-free reference pairs to generate motion-free images from the motion-affected images. Peak signal-to-noise ratio (PSNR), structural similarity (SSIM), Dice similarity coefficient (DSC), and Hausdorff distance (HD) were calculated to evaluate the image quality of GAN-generated images. RESULTS: At the image level, the median of PSNR, SSIM, DSC, and HD of GAN-generated images were 26.1 (interquartile: 24.4-27.5), 0.860 (0.830-0.882), 0.783 (0.714-0.825), and 4.47 (3.00-4.47), respectively, significantly better than the motion-affected images (p < 0.001). At the patient level, the image quality results were similar. GAN-generated images improved the motion artifact alleviation score (4 vs. 1, p < 0.001) and overall image quality score (4 vs. 1, p < 0.001) than those of the motion-affected images. In patients with ICA comparison, GAN-generated images achieved accuracy of 81%, 85%, and 70% in identifying no, < 50%, and ≥ 50% stenosis, respectively, higher than 66%, 72%, and 68% for the motion-affected images. CONCLUSION: Generative adversarial network-generated CCTA images greatly improved the image quality and diagnostic accuracy compared to motion-affected images. KEY POINTS: • A generative adversarial network greatly reduced motion artifacts in coronary CT angiography and improved image quality. • GAN-generated images improved diagnosis accuracy of identifying no, < 50%, and ≥ 50% stenosis.

Dynamic 3D Measurement without Motion Artifacts Based on Feature Compensation.

Phase-shift profilometry (PSP) holds great promise for high-precision 3D shape measurements. However, in the case of measuring moving objects, as PSP requires multiple images to calculate the phase, the movement of the object causes artifacts in the measurement, which in turn has a significant impact on the accuracy of the 3D surface measurement. Therefore, we propose a method to reduce motion artifacts using feature information in the image and simulate it using the six-step term shift method as a case study. The simulation results show that the phase of the object is greatly affected when the object is in motion and that the phase shift due to motion can be effectively reduced using this method. Finally, artifact optimization was carried out by way of specific copper tube vibration experiments at a measurement frequency of 320 Hz. The experimental results prove that the method is well implemented.

iCanClean Removes Motion, Muscle, Eye, and Line-Noise Artifacts from Phantom EEG.

The goal of this study was to test a novel approach (iCanClean) to remove non-brain sources from scalp EEG data recorded in mobile conditions. We created an electrically conductive phantom head with 10 brain sources, 10 contaminating sources, scalp, and hair. We tested the ability of iCanClean to remove artifacts while preserving brain activity under six conditions: Brain, Brain + Eyes, Brain + Neck Muscles, Brain + Facial Muscles, Brain + Walking Motion, and Brain + All Artifacts. We compared iCanClean to three other methods: Artifact Subspace Reconstruction (ASR), Auto-CCA, and Adaptive Filtering. Before and after cleaning, we calculated a Data Quality Score (0-100%), based on the average correlation between brain sources and EEG channels. iCanClean consistently outperformed the other three methods, regardless of the type or number of artifacts present. The most striking result was for the condition with all artifacts simultaneously present. Starting from a Data Quality Score of 15.7% (before cleaning), the Brain + All Artifacts condition improved to 55.9% after iCanClean. Meanwhile, it only improved to 27.6%, 27.2%, and 32.9% after ASR, Auto-CCA, and Adaptive Filtering. For context, the Brain condition scored 57.2% without cleaning (reasonable target). We conclude that iCanClean offers the ability to clear multiple artifact sources in real time and could facilitate human mobile brain-imaging studies with EEG.

A Novel Signal Restoration Method of Noisy Photoplethysmograms for Uninterrupted Health Monitoring.

Health-tracking from photoplethysmography (PPG) signals is significantly hindered by motion artifacts (MAs). Although many algorithms exist to detect MAs, the corrupted signal often remains unexploited. This work introduces a novel method able to reconstruct noisy PPGs and facilitate uninterrupted health monitoring. The algorithm starts with spectral-based MA detection, followed by signal reconstruction by using the morphological and heart-rate variability information from the clean segments adjacent to noise. The algorithm was tested on (a) 30 noisy PPGs of a maximum 20 s noise duration and (b) 28 originally clean PPGs, after noise addition (2-120 s) (1) with and (2) without cancellation of the corresponding clean segment. Sampling frequency was 250 Hz after resampling. Noise detection was evaluated by means of accuracy, sensitivity, and specificity. For the evaluation of signal reconstruction, the heart-rate (HR) was compared via Pearson correlation (PC) and absolute error (a) between ECGs and reconstructed PPGs and (b) between original and reconstructed PPGs. Bland-Altman (BA) analysis for the differences in HR estimation on original and reconstructed segments of (b) was also performed. Noise detection accuracy was 90.91% for (a) and 99.38-100% for (b). For the PPG reconstruction, HR showed 99.31% correlation in (a) and >90% for all noise lengths in (b). Mean absolute error was 1.59 bpm for (a) and 1.26-1.82 bpm for (b). BA analysis indicated that, in most cases, 90% or more of the recordings fall within the confidence interval, regardless of the noise length. Optimal performance is achieved even for signals of noise up to 2 min, allowing for the utilization and further analysis of recordings that would otherwise be discarded. Thereby, the algorithm can be implemented in monitoring devices, assisting in uninterrupted health-tracking.

Widespread effects of dMRI data quality on diffusion measures in children.

Diffusion magnetic resonance imaging (dMRI) datasets are susceptible to several confounding factors related to data quality, which is especially true in studies involving young children. With the recent trend of large-scale multicenter studies, it is more critical to be aware of the varied impacts of data quality on measures of interest. Here, we investigated data quality and its effect on different diffusion measures using a multicenter dataset. dMRI data were obtained from 691 participants (5-17 years of age) from six different centers. Six data quality metrics-contrast to noise ratio, outlier slices, and motion (absolute, relative, translation, and rotational)-and four diffusion measures-fractional anisotropy, mean diffusivity, tract density, and length-were computed for each of 36 major fiber tracts for all participants. The results indicated that four out of six data quality metrics (all except absolute and translation motion) differed significantly between centers. Associations between these data quality metrics and the diffusion measures differed significantly across the tracts and centers. Moreover, these effects remained significant after applying recently proposed harmonization algorithms that purport to remove unwanted between-site variation in diffusion data. These results demonstrate the widespread impact of dMRI data quality on diffusion measures. These tracts and measures have been routinely associated with individual differences as well as group-wide differences between neurotypical populations and individuals with neurological or developmental disorders. Accordingly, for analyses of individual differences or group effects (particularly in multisite dataset), we encourage the inclusion of data quality metrics in dMRI analysis.

Real-time X-ray imaging of mouse cerebral microvessels in vivo using a pixel temporal averaging method.

Rodents are used extensively as animal models for the preclinical investigation of microvascular-related diseases. However, motion artifacts in currently available imaging methods preclude real-time observation of microvessels in vivo. In this paper, a pixel temporal averaging (PTA) method that enables real-time imaging of microvessels in the mouse brain in vivo is described. Experiments using live mice demonstrated that PTA efficiently eliminated motion artifacts and random noise, resulting in significant improvements in contrast-to-noise ratio. The time needed for image reconstruction using PTA with a normal computer was 250 ms, highlighting the capability of the PTA method for real-time angiography. In addition, experiments with less than one-quarter of photon flux in conventional angiography verified that motion artifacts and random noise were suppressed and microvessels were successfully identified using PTA, whereas conventional temporal subtraction and averaging methods were ineffective. Experiments performed with an X-ray tube verified that the PTA method could also be successfully applied to microvessel imaging of the mouse brain using a laboratory X-ray source. In conclusion, the proposed PTA method may facilitate the real-time investigation of cerebral microvascular-related diseases using small animal models.

Non-Contact Monitoring of ECG in the Home Environment-Selecting Optimal Electrode Configuration.

Capacitive electrocardiography (cECG) is most often used in wearable or embedded measurement systems. The latter is considered in the paper. An optimal electrocardiographic lead, as an individual feature, was determined based on model studies. It was defined as the possibly highest value of the R-wave amplitude measured on the back of the examined person. The lead configuration was also analyzed in terms of minimizing its susceptibility to creating motion artifacts. It was found that the direction of the optimal lead coincides with the electrical axis of the heart. Moreover, the electrodes should be placed in the areas preserving the greatest voltage and at the same time characterized by the lowest gradient of the potential. Experimental studies were conducted using the developed measurement system on a group of 14 people. The ratio of the R-wave amplitude (as measured on the back and chest, using optimal leads) was less than 1 while the SNR reached at least 20 dB. These parameters allowed for high-quality QRS complex detection with a PPV of 97%. For the "worst" configurations of the leads, the signals measured were practically uninterpretable.

Graph-Based Motion Artifacts Detection Method from Head Computed Tomography Images.

Computed tomography (CT) images play an important role due to effectiveness and accessibility, however, motion artifacts may obscure or simulate pathology and dramatically degrade the diagnosis accuracy. In recent years, convolutional neural networks (CNNs) have achieved state-of-the-art performance in medical imaging due to the powerful learning ability with the help of the advanced hardware technology. Unfortunately, CNNs have significant overhead on memory usage and computational resources and are labeled 'black-box' by scholars for their complex underlying structures. To this end, an interpretable graph-based method has been proposed for motion artifacts detection from head CT images in this paper. From a topological perspective, the artifacts detection problem has been reformulated as a complex network classification problem based on the network topological characteristics of the corresponding complex networks. A motion artifacts detection method based on complex networks (MADM-CN) has been proposed. Firstly, the graph of each CT image is constructed based on the theory of complex networks. Secondly, slice-to-slice relationship has been explored by multiple graph construction. In addition, network topological characteristics are investigated locally and globally, consistent topological characteristics including average degree, average clustering coefficient have been utilized for classification. The experimental results have demonstrated that the proposed MADM-CN has achieved better performance over conventional machine learning and deep learning methods on a real CT dataset, reaching up to 98% of the accuracy and 97% of the sensitivity.

Robust PPG Peak Detection Using Dilated Convolutional Neural Networks.

Accurate peak determination from noise-corrupted photoplethysmogram (PPG) signal is the basis for further analysis of physiological quantities such as heart rate. Conventional methods are designed for noise-free PPG signals and are insufficient for PPG signals with low signal-to-noise ratio (SNR). This paper focuses on enhancing PPG noise-resiliency and proposes a robust peak detection algorithm for PPG signals distorted due to noise and motion artifact. Our algorithm is based on convolutional neural networks (CNNs) with dilated convolutions. We train and evaluate the proposed method using a dataset collected via smartwatches under free-living conditions in a home-based health monitoring application. A data generator is also developed to produce noisy PPG data used for model training and evaluation. The method performance is compared against other state-of-the-art methods and is tested with SNRs ranging from 0 to 45 dB. Our method outperforms the existing adaptive threshold, transform-based, and machine learning methods. The proposed method shows overall precision, recall, and F1-score of 82%, 80%, and 81% in all the SNR ranges. In contrast, the best results obtained by the existing methods are 78%, 80%, and 79%. The proposed method proves to be accurate for detecting PPG peaks even in the presence of noise.

Dual Wavelength Photoplethysmography Framework for Heart Rate Calculation.

The quality of heart rate (HR) measurements extracted from human photoplethysmography (PPG) signals are known to deteriorate under appreciable human motion. Auxiliary signals, such as accelerometer readings, are usually employed to detect and suppress motion artifacts. A 2019 study by Yifan Zhang and his coinvestigatorsused the noise components extracted from an infrared PPG signal to denoise a green PPG signal from which HR was extracted. Until now, this approach was only tested on "micro-motion" such as finger tapping. In this study, we extend this technique to allow accurate calculation of HR under high-intensity full-body repetitive "macro-motion". Our Dual Wavelength (DWL) framework was tested on PPG data collected from 14 human participants while running on a treadmill. The DWL method showed the following attributes: (1) it performed well under high-intensity full-body repetitive "macro-motion", exhibiting high accuracy in the presence of motion artifacts (as compared to the leading accelerometer-dependent HR calculation techniques TROIKA and JOSS); (2) it used only PPG signals; auxiliary signals such as accelerometer signals were not needed; and (3) it was computationally efficient, hence implementable in wearable devices. DWL yielded a Mean Absolute Error (MAE) of 1.22|0.57 BPM, Mean Absolute Error Percentage (MAEP) of 0.95|0.38%, and performance index (PI) (which is the frequency, in percent, of obtaining an HR estimate that is within ±5 BPM of the HR ground truth) of 95.88|4.9%. Moreover, DWL yielded a short computation period of 3.0|0.3 s to process a 360-second-long run.

Laboratory evaluation of performance of pulse oximeters from six different manufacturers during motion artifacts produced by Fluke 2XL SpO2 simulator.

We performed laboratory evaluation of six pulse oximeters from different manufacturers using the Fluke 2XL SpO2 simulator. The pulse oximeter probes were labeled 1 through 6 and tested using the two pre-programed preset functions of the Fluke 2XL SpO2 simulator, level 01 and level 02, for their performance in the presence of motion artifacts. The pulse oximeters were also tested at low perfusion index (PI) settings. The accuracy of the individual probes was ranked as good, mediocre, or poor based on the degree of deviation of the measured SpO2 and pulse rate (PR) by the probes from that generated by the simulator in both the presence and absence of motion artifacts and at lower PI. In preset level 01, probes numbered 1, 2, and 6 performed flawlessly, probes numbered 3, 4, and 5 performed poorly. In preset level 02, all probes performed well in the absence of motion artifacts. When probes were attached in the absence of motion, but readings were recorded after the emergence of motion artifacts, probes 1, 2, and 6 continued to perform well. However, the performance of probes 3, 4, and 5 deteriorated. When probes were attached directly in the presence of motion artifacts, probes 2 and 6 performed well, whereas all other probes performed poorly. Successively lowering the PI degraded performance of probes 3, 4, and 5 at extremely low PI. It is observed that during motion and/or low PI conditions, multiple probes see deterioration in performance.

Cardiac CT motion artifact grading via semi-automatic labeling and vessel tracking using synthetic image-augmented training data.

Cardiac CT provides critical information for the evaluation of cardiovascular diseases. However, involuntary patient motion and physiological movement of the organs during CT scanning cause motion blur in the reconstructed CT images, degrading both cardiac CT image quality and its diagnostic value. In this paper, we propose and demonstrate an effective and efficient method for CT coronary angiography image quality grading via semi-automatic labeling and vessel tracking. These algorithms produce scores that accord with those of expert readers to within 0.85 points on a 5-point scale. We also train a neural network model to perform fully-automatic motion artifact grading. We demonstrate, using XCAT simulation tools to generate realistic phantom CT data, that supplementing clinical data with synthetic data improves the scoring performance of this network. With respect to ground truth scores assigned by expert operators, the mean square error of grading motion of the right coronary artery is reduced by 36% by synthetic data supplementation. This demonstrates that augmentation of clinical training data with realistically synthesized images can potentially reduce the number of clinical studies needed to train the network.

Prospective motion detection and re-acquisition in diffusion MRI using a phase image-based method-Application to brain and tongue imaging.

PURPOSE: To develop an image-based motion-robust diffusion MRI (dMRI) acquisition framework that is able to minimize motion artifacts caused by rigid and nonrigid motion, applicable to both brain and tongue dMRI. METHODS: We developed a novel prospective motion-correction technique in dMRI using a phase image-based real-time motion-detection method (PITA-MDD) with re-acquisition of motion-corrupted images. The prospective PITA-MDD acquisition technique was tested in the brains and tongues of volunteers. The subjects were instructed to move their heads or swallow, to induce motion. Motion-detection efficacy was validated against visual inspection as the gold standard. The effect of the PITA-MDD technique on diffusion-parameter estimates was evaluated by comparing reconstructed fiber tracts using tractography with and without re-acquisition. RESULTS: The prospective PITA-MDD technique was able to effectively and accurately detect motion-corrupted data as compared with visual inspection. Tractography results demonstrated that PITA-MDD motion detection followed by re-acquisition helps in recovering lost and misshaped fiber tracts in the brain and tongue that would otherwise be corrupted by motion and yield erroneous estimates of the diffusion tensor. CONCLUSION: A prospective PITA-MDD technique was developed for dMRI acquisition, providing improved dMRI image quality and motion-robust diffusion estimation of the brain and tongue.