Continuous Non-Invasive Blood Pressure Measurement Using 60 GHz-Radar—A Feasibility Study - Vysotskaya, Nastassia; Will, Christoph; Servadei, Lorenzo; Maul, Noah; Mandl, Christian; Nau, Merlin; Harnisch, Jens; Maier, Andreas (Sensors, 2023)
Blood pressure monitoring is of paramount importance in the assessment of a human’s cardiovascular health. The state-of-the-art method remains the usage of an upper-arm cuff sphygmomanometer. However, this device suffers from severe limitations—it only provides a static blood pressure value pair, is incapable of capturing blood pressure variations over time, is inaccurate, and causes discomfort upon use. This work presents a radar-based approach that utilizes the movement of the skin due to artery pulsation to extract pressure waves. From those waves, a set of 21 features was collected and used—together with the calibration parameters of age, gender, height, and weight—as input for a neural network-based regression model. After collecting data from 55 subjects from radar and a blood pressure reference device, we trained 126 networks to analyze the developed approach’s predictive power. As a result, a very shallow network with just two hidden layers produced a systolic error of 9.2±8.3 mmHg (mean error ± standard deviation) and a diastolic error of 7.7±5.7 mmHg. While the trained model did not reach the requirements of the AAMI and BHS blood pressure measuring standards, optimizing network performance was not the goal of the proposed work. Still, the approach has displayed great potential in capturing blood pressure variation with the proposed features. The presented approach therefore shows great potential to be incorporated into wearable devices for continuous blood pressure monitoring for home use or screening applications, after improving this approach even furthe

Differentiable Score-Based Likelihoods: Learning CT Motion Compensation from Clean Images - Thies, Mareike; Maul, Noah; Mei, Siyuan; Pfaff, Laura; Vysotskaya, Nastassia; Gu, Mingxuan; Utz, Jonas; Possart, Dennis; Folle, Lukas; Wagner, Fabian; Maier, Andreas (Medical Image Computing and Computer Assisted Intervention – MICCAI 2024, 2024)
Motion artifacts can compromise the diagnostic value of computed tomography (CT) images. Motion correction approaches require a per-scan estimation of patient-specific motion patterns. In this work, we train a score-based model to act as a probability density estimator for clean head CT images. Given the trained model, we quantify the deviation of a given motion-affected CT image from the ideal distribution through likelihood computation. We demonstrate that the likelihood can be utilized as a surrogate metric for motion artifact severity in the CT image facilitating the application of an iterative, gradient-based motion compensation algorithm. By optimizing the underlying motion parameters to maximize likelihood, our method effectively reduces motion artifacts, bringing the image closer to the distribution of motion-free scans. Our approach achieves comparable performance to state-of-the-art methods while eliminating the need for a representative data set of motion-affected samples. This is particularly advantageous in real-world applications, where patient motion patterns may exhibit unforeseen variability, ensuring robustness without implicit assumptions about recoverable motion type
Keywords: Diffusion models; Neural ordinary differential equations; Exact likelihood computation; Computed tomography; Motion compensation

Enhancing diffusion-weighted prostate MRI through self-supervised denoising and evaluation - Pfaff, Laura; Darwish, Omar; Wagner, Fabian; Thies, Mareike; Vysotskaya, Nastassia; Hoßbach, Julian; Weiland, Elisabeth; Benkert, Thomas; Eichner, Cornelius; Nickel, Dominik; Wuerfl, Tobias; Maier, Andreas (Scientific Reports, 2024)
Diffusion-weighted imaging (DWI) is a magnetic resonance imaging (MRI) technique that provides information about the Brownian motion of water molecules within biological tissues. DWI plays a crucial role in stroke imaging and oncology, but its diagnostic value can be compromised by the inherently low signal-to-noise ratio (SNR). Conventional supervised deep learning-based denoising techniques encounter challenges in this domain as they necessitate noise-free target images for training. This work presents a novel approach for denoising and evaluating DWI scans in a self-supervised manner, eliminating the need for ground-truth data. By leveraging an adapted version of Stein’s unbiased risk estimator (SURE) and exploiting a phase-corrected combination of repeated acquisitions, we outperform both state-of-the-art self-supervised denoising methods and conventional non-learning-based approaches. Additionally, we demonstrate the applicability of our proposed approach in accelerating DWI scans by acquiring fewer image repetitions. To evaluate denoising performance, we introduce a self-supervised methodology that relies on analyzing the characteristics of the residual signal removed by the denoising approaches.
CRIS-Team Scopus Importer:2024-10-25

On the influence of smoothness constraints in computed tomography motion compensation - Thies, Mareike; Wagner, Fabian; Maul, Noah; Mei, Siyuan; Gu, Mingxuan; Pfaff, Laura; Vysotskaya, Nastassia; Yu, Haijun; Maier, Andreas (8th International Conference on Image Formation in X-Ray Computed Tomography (CT Meeting), 2024)
Computed tomography (CT) relies on precise patient immobilization during image acquisition. Nevertheless, motion artifacts in the reconstructed images can persist. Motion compensation methods aim to correct such artifacts post-acquisition, often incorporating temporal smoothness constraints on the estimated motion patterns. This study analyzes the influence of a spline-based motion model within an existing rigid motion compensation algorithm for cone-beam CT on the recoverable motion frequencies. Results demonstrate that the choice of motion model crucially influences recoverable frequencies. The optimization-based motion compensation algorithm is able to accurately fit the spline nodes for frequencies almost up to the node-dependent theoretical limit according to the Nyquist-Shannon theorem. Notably, a higher node count does not compromise reconstruction performance for slow motion patterns, but can extend the range of recoverable high frequencies for the investigated algorithm. Eventually, the optimal motion model is dependent on the imaged anatomy, clinical use case, and scanning protocol and should be tailored carefully to the expected motion frequency spectrum to ensure accurate motion compensation.<br
Keywords: cone-beam computed tomography; patient motion; motion frequency; splines

RadarSleepNet: Sleep Pose Classification via PointNet++ and 5D Radar Point Clouds - Fusco, Alessandra; Akkus, Mervenur; Vysotskaya, Nastassia; Hazra, Souvik; Servadei, Lorenzo; Maier, Andreas; Wille, Robert (2023 IEEE Microwaves, Antennas, and Propagation Conference (MAPCON), 2023)
Understanding sleep patterns and postures is critical for assessing overall well-being. However, traditional sleep analysis methods are often limited in their practicality due to invasive devices or complex configurations. In this study, we introduce RadarSleepNet, a non-intrusive 60 GHz Frequency-modulated Continuous Wave (FMCW) radar-based system for sleep posture monitoring that accurately infers sleep postures without compromising privacy or comfort, even in low-light conditions. Our system combines a SincNet classifier and a PointNet++ sleep pose estimation model, achieving remarkable class accuracy for each sleep posture: 98.43% for supine, 98.01% for side (chest facing radar), 97.22% for prone, and 95.72% for side (back facing radar). This demonstrates its effectiveness in accurately classifying sleep postures. This innovation offers significant potential in healthcare, providing insights into disease management and improving individual health understanding.<br
Keywords: Technological innovation; Privacy; Pose estimation; Radar detection; Medical services; Feature extraction; Sleep apnea; FMCW radar; sleep monitoring; contactless; deep learning; SincNet; PointNet+

Transforming Cardiovascular Health: a Transformer-Based Approach to Continuous, Non-Invasive Blood Pressure Estimation via Radar Sensing - Vysotskaya, Nastassia; Maul, Noah; Fusco, Alessandra; Hazra, Souvik; Harnisch, Jens; Arias Vergara, Tomás; Maier, Andreas (ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2024)
Hypertension is considered to be one of the most critical risk factors for cardiovascular diseases. As such, continuous, accurate and non-invasive monitoring of blood pressure (BP) is of utmost importance and research on such approaches is gaining momentum. In this study, we propose a novel transformer-based model architecture that leverages historic pressure wave information for accurate blood pressure regression. We achieve remarkable results that satisfy both the British Hypertension Society (BHS) and the Association for the Advancement of Medical Instrumentation (AAMI) blood pressure monitoring standards, with 97% of errors less than 5mmHg and 1.02 ± 1.77mmHg (mean absolute error ± standard deviation) accuracy for systolic (SBP), and 93% and 1.57 ± 2.36mmHg for diastolic BP (DBP). To the best of our knowledge, this is the first approach that utilizes transformers for BP regression and the first radar approach to satisfy BP standards, demonstrating the predictive power of the proposed model and the suitability of radar for the task.<br
Keywords: Hypertension; Radar; Transformers; Radar signal processing; Sensors; Biomedical monitoring; Task analysis; Continuous Non-invasive Blood Pressure Monitoring; FMCW radar; Neural Networks; Vital Sensing; Wearables

Virtual DSA for Learning Contrast Agent Dynamics in Projection Space - Maul, Noah; Birkhold, Annette; Thies, Mareike; Vysotskaya, Nastassia; Wagner, Fabian; Pfaff, Laura; Kowarschik, Markus; Maier, Andreas (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2025)
Digital Subtraction Angiography (DSA) is a well-established imaging modality supporting treatment and diagnosis of vascular pathologies. Various clinical DSA acquisition protocols exist that provide qualitative blood flow information for vascular diseases. Velocity quantification algorithms primarily rely on tracking a contrast agent (CA) bolus through the vasculature. However, the true CA velocity is fast compared to the frame rate of angiographic images. Therefore, high blood velocities pose a challenge for these methods, as the bolus may flow through long vessel segments between two subsequent DSA frames. This problem can be mitigated by increasing the temporal resolution, or equivalently, the projection image frame rate. We propose a simulation-informed neural network approach to synthetically double the projection frame rate without additional patient dose. We evaluate the quality of the synthesized projection images and show the impact on bolus tracking algorithms. Synthesized projection images can be predicted with a mean absolute percentage error of 2.5±0.8 % in the inflow phase. Further, the synthesized projections qualitatively capture bolus dynamics more accurately compared to linear interpolation. Conceptually, our method allows extension to predicting multiple intermediate projection frames, which can be a valuable tool toward accurate quantitative vascular flow estimation.
CRIS-Team Scopus Importer:2025-03-14
Keywords: Cerebrovascular Blood Flow; Computational Fluid Dynamics; Deep Learning; Digital Subtraction Angiography