Motion Artifacts Research Articles

Open-chest cardiac ultrasound-mediated imaging with a vacuum coupler.

A fundamental obstacle for the preclinical development of ultrasound-(US) mediated cardiac imaging remains cardiac motion, which limits interframe correlation during extended acquisition periods. To address this need, we present the design and implementation of a 3D-printed vacuum coupler that stabilizes a US transducer on the epicardial surface of the heart for feasibility assessment and development of advanced, cardiac, US-mediated imaging approaches. The vacuum coupler was 3D printed with biocompatible resins and secured with a standard intraoperative suction aspirator. US-mediated imaging (i.e., B-mode and photoacoustic [PA] imaging) was performed in an open-chest porcine model with and without the vacuum coupler. Based on inter-frame displacement tracking and cross-correlation (CC) coefficients, changes in frame motion and stability were compared for each imaging mode/configuration through a prolonged (∼1min) acquisition, while the impact on PA-based SO2 accuracy was assessed. When compared to conventional handheld imaging, stand-off imaging, and coupler without suction, epicardial imaging with the vacuum coupler and suction applied led to a significantly reduced mean axial displacement of 0.15 mm versus 0.89, 0.49, & 0.49 mm, respectively (p-values≤8.65e-7). Comparing the coupler without suction to that with suction applied, physiologically unrealistic SO2 estimates reduced from 1.72 to 0.81%, respectively, and lateral interframe displacement reduced from 4.58 to 2.01mm, respectively (p-value=5.07e-23). Overall,reduced cardiac tissue motion and increased interframe CC coefficient (baseline=0.43vs. coupler with suction=0.80) allow for more accurate PA unmixing. Epicardial US-mediated imaging with a vacuum coupler reduces cardiac motion artifact, providing a consistent sampling of an intended region of interest (ROI) over multiple cardiac cycles. This could help facilitate the development of advanced US-mediated imaging, which is often hindered by cardiac motion. Stable implementation of these imaging techniques could allow for intra-operative assessments of local cardiac perfusion as well as tissue characterization.

A non-invasive heart rate prediction method using a convolutional approach.

The research focuses on leveraging convolutional neural networks (CNNs) to enhance the analysis of physiological signals, specifically photoplethysmogram (PPG) data which is a valuable tool for non-invasive heart rate prediction. Recognizing the global challenge of heart failure, the study seeks to provide a rapid, accurate, and non-invasive alternative to traditional, uncomfortable blood pressure cuffs. To achieve more accurate and efficient heart rate estimates, a k-fold CNN model with an optimal number of convolutional layers is employed. While the findings show promising results, the study addresses potential sources of error in cuffless PPG-based heart rate measurement, including motion artifacts and skin color variations, emphasizing the need for validation against gold standard methods. The research optimizes a CNN model with optimal layers, operating on 1D arrays of 8-s data slices and employing k-fold cross-validation to mitigate probabilistic uncertainties. Finally, the model yields a remarkable minimum absolute error (MAE) rate of 6.98 beats per minute (bpm), marking a significant 10% improvement over recent studies. The study also advances medical diagnostics and data analysis, then lays a strong foundation for developing cost-effective, reliable devices that offer a more comfortable and efficient way of predicting heart rate.

Accuracy of Wrist-Worn Heart Rate Monitors: A Comprehensive Review of Smartwatches in Exercise Monitoring

Wrist-worn heart rate monitors have become integral in personal health monitoring, particularly during exercise and daily activities. This review evaluates the precision of various commercially available smartwatches in assessing heart rate, focusing on their accuracy across different physical activities, such as walking, running, cycling, and elliptical training. A systematic analysis of 29 peer-reviewed studies, encompassing over 900 participants, was conducted to compare devices like the Apple Watch, Fitbit, Garmin, and others. The results demonstrate significant variation in accuracy depending on the device, type of activity, and individual factors such as body mass index and skin tone. The Apple Watch consistently outperformed other devices, showing the lowest mean absolute percentage error (MAPE), particularly during moderate-intensity activities. In contrast, devices such as the Fitbit Blaze exhibited greater error rates, especially during high-intensity exercises or activities involving significant arm movement. Although wrist-worn heart rate monitors provide users with convenient real-time data, limitations such as motion artifacts, device placement, and external factors may affect their reliability.

Comparison of different acceleration factors of artificial intelligence-compressed sensing for brachial plexus MRI imaging: scanning time and image quality.

3D brachial plexus MRI scanning is prone to examination failure due to the lengthy scan times, which can lead to patient discomfort and motion artifacts. Our purpose is to investigate the efficacy of artificial intelligence-assisted compressed sensing (ACS) in improving the acceleration efficiency and maintaining or enhancing the image quality of brachial plexus MR imaging. A total of 30 volunteers underwent 3D sampling perfection with application-optimized contrast using different flip angle evolution short time inversion recovery using a 3.0T MR scanner. The imaging protocol included parallel imaging (PI) and ACS employing acceleration factors of 4.37, 6.22, and 9.03. Radiologists evaluated the neural detail display, fat suppression effectiveness, presence of image artifacts, and overall image quality. Signal intensity and standard deviation of specific anatomical sites within the brachial plexus and background tissues were measured, with signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) subsequently calculated. Cohen's weighted kappa (κ), One-way ANOVA, Kruskal-Wallis and pairwise comparisons with Bonferroni-adjusted significance level. P < 0.05 was considered statistically significant. ACS significantly reduced scanning times compared to PI. Evaluations revealed differences in subjective scores and SNR across the sequences (P < 0.05), with no marked differences in CNR (P > 0.05). For subjective scores, ACS 9.03 were lower than the other three sequences in neural details display, image artifacts and overall image quality. There was no significant difference in fat suppression. For objective quantitative evaluation, SNR of right C6 root in ACS 6.22 and ACS 9.03 was higher than that in PI; SNR of left C6 root in ACS 4.37, ACS 6.22 and ACS 9.03 was higher than that in PI; SNR of medial cord in ACS 6.22, ACS 9.03 was higher than that in PI. Compared with PI, ACS can shorten scanning time while ensuring good image quality.

4DCT image artifact detection using deep learning.

Four-dimensional computed tomography (4DCT) is an es sential tool in radiation therapy. However, the 4D acquisition process may cause motion artifacts which can obscure anatomy and distort functional measurements from CTscans. We describe a deep learning algorithm to identify the location of artifacts within 4DCT images. Our method is flexible enough to handle different types of artifacts, including duplication, misalignment, truncation, andinterpolation. We trained and validated a U-net convolutional neural network artifact detection model on more than 23000 coronal slices extracted from 98 4DCT scans. The receiver operating characteristic (ROC) curve and precision-recall curve were used to evaluate the model's performance at identifying artifacts compared to a manually identified ground truth. The model was adjusted so that the sensitivity in identifying artifacts was equivalent to that of a human observer, as measured by computing the average ratio of artifact volume to lung volume in a givenscan. The model achieved a sensitivity, specificity, and precision of 0.78, 0.99, and 0.58, respectively. The ROC area-under-the-curve (AUC) was 0.99 and the precision-recall AUC was 0.73. Our model sensitivity is 8% higher than previously reported state-of-the-art artifact detectionmethods. The model developed in this study is versatile, designed to handle duplication, misalignment, truncation, and interpolation artifacts within a single image, unlike earlier models that were designed for a single artifact type.

Neural speech tracking and auditory attention decoding in everyday life

IntroductionIn our complex world, the auditory system plays a crucial role in perceiving and processing our environment. Humans are able to segment and stream concurrent auditory objects, allowing them to focus on specific sounds, such as speech, and suppress irrelevant auditory objects. The attentional enhancement or suppression of sound processing is evident in neural data through a phenomenon called neural speech tracking. Previous studies have identified correlates of neural speech tracking in electroencephalography (EEG) data, but EEG measures are susceptible to motion artefacts, and the association between neural data and auditory objects is vulnerable to distraction.MethodsThe current study investigated EEG-based auditory attention decoding in realistic everyday scenarios. N=20 participants were exposed to the sound of a busy cafeteria or walked along busy and quiet streets while listening to one or two simultaneous speech streams. We also investigated the robustness of neural speech tracking estimates within subjects. Linear decoding models were used to determine the magnitude of neural speech tracking.ResultsThe results confirmed that neural speech tracking was strongest in single speaker scenarios. In dual speaker conditions, there was significantly stronger neural speech tracking for the attended speaker compared to the ignored speaker, even in complex environments such as a busy cafeteria or outdoor settings.DiscussionIn conclusion, EEG-based attention decoding is feasible in highly complex and realistic everyday conditions while humans behave naturally.

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

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

Abstract 4140123: Real-time imaging of microvasculature obstruction and the vasculoprotection of nitric-oxide-donor nanoparticles during acute myocardial ischemia/reperfusion injury

Background/Introduction: Microvascular obstruction (MVO), due to damage to the coronary microvasculature, is a key determinant of infarct size, heart failure and poor outcomes following acute myocardial infarction, and there is currently no treatment for preventing MVO. Real-time in vivo imaging of MVO in the beating rodent heart is challenging due to the limited spatial and temporal resolution from movement artifacts. Here, we apply, for the first time, fiber-optic confocal laser endomicroscopy (CLM) for real-time imaging of the microvasculature in a beating murine heart with acute ischemia/reperfusion injury (IRI), and then monitoring the development of MVO. Methods: An in vivo murine acute myocardial IRI model (45 min ligation of left coronary artery (LCA) and 30 min reperfusion) was applied. At 10 min prior to ischaemia, 150 µl Dextran-FITC (150 kDa, 10 mg/ml) was injected retro-orbitally, and then CLM imaging with a flexible miniprobe (ProFlex S-1500 with CellVizio system) was applied to the epicardial surface at multiple sites at 5 min post-injection (baseline), 30 min post-ischemia and 30 min post-reperfusion. A nitric oxide donor(NO) nanoparticle (NONP) was synthesized and IV bolus injected into IRI mice 5min prior to reperfusion to prevent MVO. Results: We confirmed visualization of the macro- and microvasculature at various sites on the epicardial surface of the beating heart. Next, we observed reduced microvasculature blood flow below LCA ligature as evidenced by reduced or even totally absence of FITC within the vessels at 30min post-ischemia. The microvasculature at the non-ischemic myocardium was unaffected. Furthermore, at 30 min post-reperfusion, we visualised patchy areas of reduced FITC signal suggesting MVO, and damaged microvasculature as evidenced by leakage of FITC outside the vessel. Interestingly, NONP treatment preserved the microvascular network and prevented MVO at 30 min post-reperfusion with even greater FITC, suggesting increased microvascular blood flow and penetration into cardiac tissue because of the vasodilatory effect of NO in the ischemic area. Conclusion: With CellVizio CLM system, we have demonstrated the MVO development during IRI, and damage to the microvasculature with leakage of dye from vessels into cardiac interstitium, thereby providing a pre-clinical platform to test novel therapeutic agents for preventing MVO. Importantly, we have shown an effective MVO prevention with NO-donor nanoparticle following IRI in mice.

Motion and magnetic field inhomogeneity correction techniques for chemical exchange saturation transfer (CEST) MRI: A contemporary review.

Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) has emerged as a powerful imaging technique sensitive to tissue molecular composition, pH, and metabolic processes in situ. CEST MRI uniquely probes the physical exchange of protons between water and specific molecules within tissues, providing a window into physiological phenomena that remain invisible to standard MRI. However, given the very low concentration (millimolar range) of CEST compounds, the effects measured are generally only on the order of a few percent of the water signal. Consequently, a few critical challenges, including correction of motion artifacts and magnetic field (B0 and B1 +) inhomogeneities, have to be addressed in order to unlock the full potential of CEST MRI. Motion, whether from patient movement or inherent physiological pulsations, can distort the CEST signal, hindering accurate quantification. B0 and B1 + inhomogeneities, arising from scanner hardware imperfections, further complicate data interpretation by introducing spurious variations in the signal intensity. Without proper correction of these confounding factors, reliable analysis and clinical translation of CEST MRI remain challenging. Motion correction methods aim to compensate for patient movement during (prospective) or after (retrospective) image acquisition, reducing artifacts and preserving data quality. Similarly, B0 and B1 + inhomogeneity correction techniques enhance the spatial and spectral accuracy of CEST MRI. This paper aims to provide a comprehensive review of the current landscape of motion and magnetic field inhomogeneity correction methods in CEST MRI. The methods discussed apply to saturation transfer (ST) MRI in general, including semisolid magnetization transfer contrast (MTC) and relayed nuclear Overhauser enhancement (rNOE) studies.

Spectrally selective and interleaved water imaging and fat imaging (siWIFI).

To develop a novel imaging sequence that independently acquires water and fat images while being inherently insensitive to motion. The new sequence, termed spectrally selective and interleaved water imaging and fat imaging (siWIFI), uses a narrow bandwidth RF pulse for selective excitation of water and fat separately. The interleaved acquisition method ensures that the obtained water and fat images are inherently coregistered. A radial sampling strategy further reduces motion-induced artifacts. Phantoms with lipid concentrations ranging from 0% to 50% were scanned to measure fat fraction. Moreover, healthy volunteers were scanned to assess the in vivo feasibility of fat fraction measurement at the hip, knee, and liver. In vivo fat fraction measurements were compared with those from vendor-provided iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL) scans. Furthermore, a magnetization transfer (MT) preparation module was incorporated to demonstrate the feasibility of simultaneous measurement of fat fraction and MT ratio utilizing the siWIFI framework. The phantom fat fractions measured by siWIFI showed excellent correlation with lipid concentrations (R2 = 0.9995, p < 0.0001). In vivo studies demonstrated that the fat fractions obtained from siWIFI were comparable to those from IDEAL. Additionally, siWIFI demonstrates reduced motion artifacts from pulsatile flow in knee imaging compared to IDEAL scans and exhibits less sensitivity to respiratory motion in liver imaging compared to IDEAL scans without breath-hold. The knee imaging study demonstrated that MT-prepared siWIFI is capable of generating fat fraction and MT ratio maps simultaneously. The proposed siWIFI sequence allows selective water-fat imaging and quantification with reduced motion artifacts.

Impact of pre-examination video education in Gd-EOB-DTPA-enhanced liver MRI: A comparative study.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality, and early diagnosis via gadolinium ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) significantly impacts patient outcomes. However, patient anxiety during MRI can affect image quality. This study investigates the impact of pre-examination video education on anxiety, satisfaction and image quality in Gd-EOB-DTPA-enhanced liver MRI. We prospectively enrolled 480 patients who underwent Gd-EOB-DTPA-enhanced liver MRI from January 2022 to May 2023 at our hospital. Patients were divided into study and control groups in order of odd and even days, with 240 cases in each group. Before the examination, the radiology staff provided routine verbal guidance and breathing training to the patients in the control group, while the study group was given additional video education. The state anxiety scores, satisfaction scores of the provided information and motion artefact scores of the images before and after the examination were compared between the two groups. The state anxiety scores of both groups of patients were lower than before the examination (all P < 0.05), but the change value of the study group was significantly greater than that of the control group (P = 0.004). The satisfaction rate of the information provided before the scan in the study group was significantly higher (P < 0.001). The image quality scores of the arterial phase were similar between the two groups (P = 0.403), but the image quality of the study group in the pre-contrast, portal phase, transitional phase and hepatobiliary phase was significantly better than that of the control group (all P < 0.05). Supplementing routine pre-scan care with video guidance for Gd-EOB-DTPA-enhanced liver MRI offers several benefits, including reduced patient anxiety, increased satisfaction and improved image quality. These results suggest the potential for widespread application of video-based interventions to enhance the MRI experience for patients.

Machine Learning Applied to Reference Signal-Less Detection of Motion Artifacts in Photoplethysmographic Signals: A Review

Machine learning algorithms have brought remarkable advancements in detecting motion artifacts (MAs) from the photoplethysmogram (PPG) with no measured or synthetic reference data. However, no study has provided a synthesis of these methods, let alone an in-depth discussion to aid in deciding which one is more suitable for a specific purpose. This narrative review examines the application of machine learning techniques for the reference signal-less detection of MAs in PPG signals. We did not consider articles introducing signal filtering or decomposition algorithms without previous identification of corrupted segments. Studies on MA-detecting approaches utilizing multiple channels and additional sensors such as accelerometers were also excluded. Despite its promising results, the literature on this topic shows several limitations and inconsistencies, particularly those regarding the model development and testing process and the measures used by authors to support the method’s suitability for real-time applications. Moreover, there is a need for broader exploration and validation across different body parts and a standardized set of experiments specifically designed to test and validate MA detection approaches. It is essential to provide enough elements to enable researchers and developers to objectively assess the reliability and applicability of these methods and, therefore, obtain the most out of them.

Continuous heart rate monitoring in atrial fibrillation with multispectral photoplethysmography

Heart attacks and strokes account for more than four out of every five Cardiovascular Disease (CVD) deaths, with one-third occurring before age 70 years. Atrial fibrillation (AF), a common cause of ischemic strokes, can significantly reduce mortality rates through continuous monitoring, timely diagnosis, accurate confirmation, and early prognosis. This can be achieved by employing Multi-Spectral Photoplethysmography (MSPPG) technology as an alternative diagnostic tool to Electrocardiography (ECG). This investigation is implemented by determining the Heart Rate (HR) of AF-affected patients using a developed MSPPG-based wrist-worn device, keeping ECG as a standard gold reference. The Multispectral-based wrist-worn wearable Photoplethysmography device employs an optical bio-sensing, integrated BIOFY sensor to acquire the MSPPG signals using two green (526 nm), one red (660 nm) and one infrared (950 nm) LED and two photodetectors (Broadband detector, IR-cut detector). The MSPPG signals acquired using a microcontroller unit are stored in an On-device storage system simultaneously in the IoT Platform. Statistical analysis techniques were employed to compare the HR detected using the MSPPG technique with the HR measured from the gold-standard ECG. The accuracy paves the way for implementing the proposed MSPPG technology in detecting AF conditions in terms of HR, further laying the foundation for earlier detection of AF. Wearable PPG-based devices face a significant issue of motion artifacts that can be overcome using the MSPPG technique. The research advancements can potentially increase the accessibility of HR monitoring in AF patients, conveniently integrating into daily routines without restricting day-to-day activities.

An Unobtrusive and Lightweight Ear-worn System for Continuous Epileptic Seizure Detection

Epilepsy is one of the most common neurological diseases globally (around 50M people globally). Fortunately, up to 70% of people with epilepsy could live seizure-free if properly diagnosed and treated, and a reliable technique to monitor the onset of seizures could improve the quality of life of patients who are constantly facing the fear of random seizure attacks. The current gold standard, video-EEG (v-EEG), involves attaching over 20 electrodes to the scalp, is costly, requires hospitalization, trained professionals, and is uncomfortable for patients. To address this gap, we developed EarSD , a lightweight and unobtrusive ear-worn system to detect seizure onsets by measuring physiological signals behind the ears. This system can be integrated into earphones, headphones, or hearing aids, providing a convenient solution for continuous monitoring. EarSD is an integrated custom-built sensing - computing - communication ear-worn platform to capture seizure signals, remove the noises caused by motion artifacts and environmental impacts, and stream the collected data wirelessly to the computer/mobile phone nearby. EarSD 's ML algorithm, running on a server, identifies seizure-associated signatures and detects onset events. We evaluated the proposed system in both in-lab and in-hospital experiments at the University of Texas Southwestern Medical Center with epileptic seizure patients, confirming its usability and practicality.

Deep Autoencoder for Real-Time Single-Channel EEG Cleaning and Its Smartphone Implementation Using TensorFlow Lite With Hardware/Software Acceleration.

To remove signal contamination in electroencephalogram (EEG) traces coming from ocular, motion, and muscular artifacts which degrade signal quality. To do this in real-time, with low computational overhead, on a mobile platform in a channel count independent manner to enable portable Brain-Computer Interface (BCI) applications. We propose a Deep AutoEncoder (DAE) neural network for single-channel EEG artifact removal, and implement it on a smartphone via TensorFlow Lite. Delegate based acceleration is employed to allow real-time, low computational resource operation. Artifact removal performance is quantified by comparing corrupted and ground-truth clean EEG data from public datasets for a range of artifact types. The on-phone computational resources required are also measured when processing pre-saved data. DAE cleaned EEG shows high correlations with ground-truth clean EEG, with average Correlation Coefficients of 0.96, 0.85, 0.70 and 0.79 for clean EEG reconstruction, and EOG, motion, and EMG artifact removal respectively. On-smartphone tests show the model processes a 4 s EEG window within 5 ms, substantially outperforming a comparison FastICA artifact removal algorithm. The proposed DAE model shows effectiveness in single-channel EEG artifact removal. This is the first demonstration of a low-computational resource deep learning model for mobile EEG in smartphones with hardware/software acceleration. This work enables portable BCIs which require low latency real-time artifact removal, and potentially operation with a small number of EEG channels for wearability. It makes use of the artificial intelligence accelerators found in modern smartphones to improve computational performance compared to previous artifact removal approaches.

Meta-analysis study on anesthetic sedation recovery and onset times in pediatric and elderly patients undergoing CT and MRI.

Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are crucial diagnostic modalities that require patients to remain immobile for extended periods, with anesthesia sometimes used for comfort and image quality enhancement. The study compares dexmedetomidine and propofol in reducing recovery time and sedation onset in pediatric and elderly patients undergoing CT and MRI procedures. A meta-analysis of fifteen studies assessing recovery time, sedation onset, and failed sedation between dexmedetomidine and propofol in pediatric and elderly patients during CT and MRI was conducted. The study indicated that the administration of anaesthesia markedly improved patient compliance and reduced motion artefacts in both CT and MRI (P<0.00001, I2=94%). The meta-analysis indicated that the mean difference (MD) in the onset of sedation was significantly faster in the control group (P<0.00001, I2=96%). The study reveals that dexmedetomidine and propofol anesthesia can improve patient image quality during CT and MRI procedures by reducing motion artefacts. Dexmedetomidine sedated people more quickly than propofol, but no significant differences in sedation duration were observed.

CMOS Pulse Oximeter

Abstract: This paper presents the design and implementation of a non-invasive pulse oximeter based on Complementary MetalOxide-Semiconductor (CMOS) technology, aimed at enhancing the accuracy and accessibility of blood oxygen saturation measurements. Pulse oximetry is a critical tool in clinical and personal health monitoring, providing vital information about respiratory function. The integration of CMOS technology allows for miniaturization and energy efficiency, facilitating the development of compact, wearable devices capable of continuous monitoring. The proposed system utilizes advanced signal processing techniques, including lock-in detection and dual-wavelength illumination, to improve the reliability of photoplethysmographic (PPG) signal acquisition under various conditions. Experimental validation demonstrates the device's capability to deliver accurate SpO2 readings even in the presence of motion artifacts and ambient light interference. Furthermore, the calibration methods employed ensure that measurements are consistent and precise across different users. By leveraging the advantages of CMOS technology, this study not only highlights the potential for improved patient comfort and compliance but also opens new avenues for the application of pulse oximetry in diverse environments. The findings underscore the effectiveness of CMOS-based pulse oximeters in advancing non-invasive health monitoring solutions, ultimately contributing to enhanced patient care and outcomes.

Sudden Cardiac Death Risk Prediction Based on Noise Interfered Single-Lead ECG Signals

Sudden cardiac death (SCD) represents a critical acute cardiovascular event characterized by rapid onset of cardiac and respiratory arrest, posing a significant threat to patients due to its high fatality rate. Monitoring indices related to SCD using wearable devices holds profound implications for preemptive measures aimed at reducing the incidence of such life-threatening events. Hence, this study proposed a predictive algorithm for SCD leveraging single-lead electrocardiogram (ECG) signals featuring low signal-to-noise ratios. Initially, simulated electrode motion artifact noise was introduced to ideal ECG signals to emulate the signal conditions with low signal-to-noise ratios encountered in everyday scenarios. To meet the criteria of simplicity and cost-effectiveness required for wearable devices, the analysis focused exclusively on single-lead signals. The proposed algorithm in this study employed a lightweight machine learning approach to extract 12-dimensional features encompassing ventricular late potentials, T-wave electrical alternation, and corrected QT intervals from the signal. The algorithm achieved an average prediction accuracy of 93.22% within 30 min prior to SCD onset, and 95.43% when utilizing a normal sinus rhythm database as a control, demonstrating robust performance. Additionally, a comprehensive Sudden Cardiac Death Index (SCDI) was devised to quantify the risk of SCD, formulated by integrating pivotal two-dimensional features contributing significantly to the algorithm. This index effectively distinguishes high-risk signals indicative of SCD from normal signals, thereby offering valuable supplementary insights in clinical settings.

Amide proton transfer-weighted CEST MRI for radiotherapy target delineation of glioblastoma: a prospective pilot study

BackgroundExtensive glioblastoma infiltration justifies a 15-mm margin around the gross tumor volume (GTV) to define the radiotherapy clinical target volume (CTV). Amide proton transfer (APT)-weighted imaging could enable visualization of tumor infiltration, allowing more accurate GTV delineation. We quantified the impact of integrating APT-weighted imaging into GTV delineation of glioblastoma and compared two APT-weighted quantification methods—magnetization transfer ratio asymmetry (MTRasym) and Lorentzian difference (LD) analysis—for target delineation.MethodsNine glioblastoma patients underwent an extended imaging protocol prior to radiotherapy, yielding APT-weighted MTRasym and LD maps. From both maps, biological tumor volumes were generated (BTVMTRasym and BTVLD) and added to the conventional GTV to generate biological GTVs (GTVbio,MTRasym and GTVbio,LD). Wilcoxon signed-rank tests were performed for comparisons.ResultsThe GTVbio,MTRasym and GTVbio,LD were significantly larger than the conventional GTV (p ≤ 0.022), with a median volume increase of 9.3% and 2.1%, respectively. The GTVbio,MTRasym and GTVbio,LD were significantly smaller than the CTV (p = 0.004), with a median volume reduction of 72.1% and 70.9%, respectively. There was no significant volume difference between the BTVMTRasym and BTVLD (p = 0.074). In three patients, BTVMTRasym delineation was affected by elevated signals at the brain periphery due to residual motion artifacts; this elevation was absent on the APT-weighted LD maps.ConclusionLarger biological GTVs compared to the conventional GTV highlight the potential of APT-weighted imaging for radiotherapy target delineation of glioblastoma. APT-weighted LD mapping may be advantageous for target delineation as it may be more robust against motion artifacts.Relevance statementThe introduction of APT-weighted imaging may, ultimately, enhance visualization of tumor infiltration and eliminate the need for the substantial 15-mm safety margin for target delineation of glioblastoma. This could reduce the risk of radiation toxicity while still effectively irradiating the tumor.Trial registrationNCT05970757 (ClinicalTrials.gov).Key PointsIntegration of APT-weighted imaging into target delineation for radiotherapy is feasible.The integration of APT-weighted imaging yields larger GTVs in glioblastoma.APT-weighted LD mapping may be more robust against motion artifacts than APT-weighted MTRasym.Graphical

Fast inter-frame motion correction in contrast-free ultrasound quantitative microvasculature imaging using deep learning

Contrast-free ultrasound quantitative microvasculature imaging shows promise in several applications, including the assessment of benign and malignant lesions. However, motion represents one of the major challenges in imaging tumor microvessels in organs that are prone to physiological motions. This study aims at addressing potential microvessel image degradation in in vivo human thyroid due to its proximity to carotid artery. The pulsation of the carotid artery induces inter-frame motion that significantly degrades microvasculature images, resulting in diagnostic errors. The main objective of this study is to reduce inter-frame motion artifacts in high-frame-rate ultrasound imaging to achieve a more accurate visualization of tumor microvessel features. We propose a low-complex deep learning network comprising depth-wise separable convolutional layers and hybrid adaptive and squeeze-and-excite attention mechanisms to correct inter-frame motion in high-frame-rate images. Rigorous validation using phantom and in-vivo data with simulated inter-frame motion indicates average improvements of 35% in Pearson correlation coefficients (PCCs) between motion corrected and reference data with respect to that of motion corrupted data. Further, reconstruction of microvasculature images using motion-corrected frames demonstrates PCC improvement from 31 to 35%. Another thorough validation using in-vivo thyroid data with physiological inter-frame motion demonstrates average improvement of 20% in PCC and 40% in mean inter-frame correlation. Finally, comparison with the conventional image registration method indicates the suitability of proposed network for real-time inter-frame motion correction with 5000 times reduction in motion corrected frame prediction latency.