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Ashtiani SZ, Sarabian M, Laksari K, Babaee H. Reconstructing blood flow in data-poor regimes: a vasculature network kernel for Gaussian process regression. J R Soc Interface 2024; 21:20240194. [PMID: 39173147 PMCID: PMC11341099 DOI: 10.1098/rsif.2024.0194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 05/12/2024] [Accepted: 06/12/2024] [Indexed: 08/24/2024] Open
Abstract
Blood flow reconstruction in the vasculature is important for many clinical applications. However, in clinical settings, the available data are often quite limited. For instance, transcranial Doppler ultrasound is a non-invasive clinical tool that is commonly used in clinical settings to measure blood velocity waveforms at several locations. This amount of data is grossly insufficient for training machine learning surrogate models, such as deep neural networks or Gaussian process regression. In this work, we propose a Gaussian process regression approach based on empirical kernels constructed by data generated from physics-based simulations-enabling near-real-time reconstruction of blood flow in data-poor regimes. We introduce a novel methodology to reconstruct the kernel within the vascular network. The proposed kernel encodes both spatiotemporal and vessel-to-vessel correlations, thus enabling blood flow reconstruction in vessels that lack direct measurements. We demonstrate that any prediction made with the proposed kernel satisfies the conservation of mass principle. The kernel is constructed by running stochastic one-dimensional blood flow simulations, where the stochasticity captures the epistemic uncertainties, such as lack of knowledge about boundary conditions and uncertainties in vasculature geometries. We demonstrate the performance of the model on three test cases, namely, a simple Y-shaped bifurcation, abdominal aorta and the circle of Willis in the brain.
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Affiliation(s)
- Shaghayegh Z. Ashtiani
- Department of Mechanical Engineering and Material Science, University of Pittsburgh, Pittsburgh, PA, USA
| | - Mohammad Sarabian
- Department of Biomedical Engineering, University of Arizona, Tucson, AZ, USA
| | - Kaveh Laksari
- Department of Mechanical Engineering, University of California Riverside, Riverside, CA, USA
| | - Hessam Babaee
- Department of Mechanical Engineering and Material Science, University of Pittsburgh, Pittsburgh, PA, USA
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Anjum M, Shahab S, Ahmad S, Dhahbi S, Whangbo T. Aggregated Pattern Classification Method for improving neural disorder stage detection. Brain Behav 2024; 14:e3519. [PMID: 39169422 PMCID: PMC11338743 DOI: 10.1002/brb3.3519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 03/08/2024] [Accepted: 03/17/2024] [Indexed: 08/23/2024] Open
Abstract
BACKGROUND Neurological disorders pose a significant health challenge, and their early detection is critical for effective treatment planning and prognosis. Traditional classification of neural disorders based on causes, symptoms, developmental stage, severity, and nervous system effects has limitations. Leveraging artificial intelligence (AI) and machine learning (ML) for pattern recognition provides a potent solution to address these challenges. Therefore, this study focuses on proposing an innovative approach-the Aggregated Pattern Classification Method (APCM)-for precise identification of neural disorder stages. METHOD The APCM was introduced to address prevalent issues in neural disorder detection, such as overfitting, robustness, and interoperability. This method utilizes aggregative patterns and classification learning functions to mitigate these challenges and enhance overall recognition accuracy, even in imbalanced data. The analysis involves neural images using observations from healthy individuals as a reference. Action response patterns from diverse inputs are mapped to identify similar features, establishing the disorder ratio. The stages are correlated based on available responses and associated neural data, with a preference for classification learning. This classification necessitates image and labeled data to prevent additional flaws in pattern recognition. Recognition and classification occur through multiple iterations, incorporating similar and diverse neural features. The learning process is finely tuned for minute classifications using labeled and unlabeled input data. RESULTS The proposed APCM demonstrates notable achievements, with high pattern recognition (15.03%) and controlled classification errors (CEs) (10.61% less). The method effectively addresses overfitting, robustness, and interoperability issues, showcasing its potential as a powerful tool for detecting neural disorders at different stages. The ability to handle imbalanced data contributes to the overall success of the algorithm. CONCLUSION The APCM emerges as a promising and effective approach for identifying precise neural disorder stages. By leveraging AI and ML, the method successfully resolves key challenges in pattern recognition. The high pattern recognition and reduced CEs underscore the method's potential for clinical applications. However, it is essential to acknowledge the reliance on high-quality neural image data, which may limit the generalizability of the approach. The proposed method allows future research to refine further and enhance its interpretability, providing valuable insights into neural disorder progression and underlying biological mechanisms.
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Affiliation(s)
- Mohd Anjum
- Department of Computer EngineeringAligarh Muslim UniversityAligarhIndia
| | - Sana Shahab
- Department of Business AdministrationCollege of Business AdministrationPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia
| | - Shabir Ahmad
- Department of Computer EngineeringCollege of IT ConvergenceGachon UniversitySeongnamRepublic of Korea
| | - Sami Dhahbi
- Department of Computer science, College of Science and Art at MahayilKing Khalid UniversityMuhayil AseerSaudi Arabia
| | - Taegkeun Whangbo
- Department of Computer EngineeringCollege of IT ConvergenceGachon UniversitySeongnamRepublic of Korea
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Veeturi SS, Hall S, Fujimura S, Mossa-Basha M, Sagues E, Samaniego EA, Tutino VM. Imaging of Intracranial Aneurysms: A Review of Standard and Advanced Imaging Techniques. Transl Stroke Res 2024:10.1007/s12975-024-01261-w. [PMID: 38856829 DOI: 10.1007/s12975-024-01261-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Revised: 04/16/2024] [Accepted: 05/13/2024] [Indexed: 06/11/2024]
Abstract
The treatment of intracranial aneurysms is dictated by its risk of rupture in the future. Several clinical and radiological risk factors for aneurysm rupture have been described and incorporated into prediction models. Despite the recent technological advancements in aneurysm imaging, linear length and visible irregularity with a bleb are the only radiological measure used in clinical prediction models. The purpose of this article is to summarize both the standard imaging techniques, including their limitations, and the advanced techniques being used experimentally to image aneurysms. It is expected that as our understanding of advanced techniques improves, and their ability to predict clinical events is demonstrated, they become an increasingly routine part of aneurysm assessment. It is important that neurovascular specialists understand the spectrum of imaging techniques available.
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Affiliation(s)
- Sricharan S Veeturi
- Canon Stroke and Vascular Research Center, Clinical and Translational Research Center, University at Buffalo, 875 Ellicott Street, Buffalo, NY, 14214, USA
- Department of Neurosurgery, University at Buffalo, Buffalo, NY, USA
| | - Samuel Hall
- Department of Neurosurgery, University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Soichiro Fujimura
- Department of Mechanical Engineering, Tokyo University of Science, Tokyo, Japan
- Division of Innovation for Medical Information Technology, The Jikei University School of Medicine, Tokyo, Japan
| | | | - Elena Sagues
- Department of Neurology, University of Iowa, Iowa City, IA, USA
| | | | - Vincent M Tutino
- Canon Stroke and Vascular Research Center, Clinical and Translational Research Center, University at Buffalo, 875 Ellicott Street, Buffalo, NY, 14214, USA.
- Department of Pathology and Anatomical Sciences, University at Buffalo, Buffalo, NY, USA.
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Martin T, El Hage G, Chaalala C, Peeters JB, Bojanowski MW. Hemodynamic factors of spontaneous vertebral artery dissecting aneurysms assessed with numerical and deep learning algorithms: Role of blood pressure and asymmetry. Neurochirurgie 2024; 70:101519. [PMID: 38280371 DOI: 10.1016/j.neuchi.2023.101519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 11/21/2023] [Indexed: 01/29/2024]
Abstract
BACKGROUND AND OBJECTIVES The pathophysiology of spontaneous vertebral artery dissecting aneurysms (SVADA) is poorly understood. Our goal is to investigate the hemodynamic factors contributing to their formation using computational fluid dynamics (CFD) and deep learning algorithms. METHODS We have developed software that can use patient imagery as input to recreate the vertebrobasilar arterial system, both with and without SVADA, which we used in a series of three patients. To obtain the kinematic blood flow data before and after the aneurysm forms, we utilized numerical methods to solve the complex Navier-Stokes partial differential equations. This was accomplished through the application of a finite volume solver (OpenFoam/Helyx OS). Additionally, we trained a neural ordinary differential equation (NODE) to learn and replicate the dynamical streamlines obtained from the computational fluid dynamics (CFD) simulations. RESULTS In all three cases, we observed that the equilibrium of blood pressure distributions across the VAs, at a specific vertical level, accurately predicted the future SVADA location. In the two cases where there was a dominant VA, the dissection occurred on the dominant artery where blood pressure was lower compared to the contralateral side. The SVADA sac was characterized by reduced wall shear stress (WSS) and decreased velocity magnitude related to increased turbulence. The presence of a high WSS gradient at the boundary of the SVADA may explain its extension. Streamlines generated by CFD were learned with a neural ordinary differential equation (NODE) capable of capturing the system's dynamics to output meaningful predictions of the flow vector field upon aneurysm formation. CONCLUSION In our series, asymmetry in the vertebrobasilar blood pressure distributions at and proximal to the site of the future SVADA accurately predicted its location in all patients. Deep learning algorithms can be trained to model blood flow patterns within biological systems, offering an alternative to the computationally intensive CFD. This technology has the potential to find practical applications in clinical settings.
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Affiliation(s)
- Tristan Martin
- Division of Neurosurgery, Department of Surgery, University of Montreal Hospital Center 1000, rue St-Denis Montréal, QC H2X 0C, Canada
| | - Gilles El Hage
- Division of Neurosurgery, Department of Surgery, University of Montreal Hospital Center 1000, rue St-Denis Montréal, QC H2X 0C, Canada
| | - Chiraz Chaalala
- Division of Neurosurgery, Department of Surgery, University of Montreal Hospital Center 1000, rue St-Denis Montréal, QC H2X 0C, Canada
| | - Jean-Baptiste Peeters
- Division of Neurosurgery, Department of Surgery, University of Montreal Hospital Center 1000, rue St-Denis Montréal, QC H2X 0C, Canada
| | - Michel W Bojanowski
- Division of Neurosurgery, Department of Surgery, University of Montreal Hospital Center 1000, rue St-Denis Montréal, QC H2X 0C, Canada.
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5
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Aghaee A, Khan MO. Performance of Fourier-based activation function in physics-informed neural networks for patient-specific cardiovascular flows. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 247:108081. [PMID: 38428251 DOI: 10.1016/j.cmpb.2024.108081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 02/14/2024] [Accepted: 02/14/2024] [Indexed: 03/03/2024]
Abstract
BACKGROUND AND OBJECTIVES Physics-informed neural networks (PINNs) can be used to inversely model complex physical systems by encoding the governing partial differential equations and training data into the neural network. However, neural networks are known to be biased towards learning less complex functions, called spectral bias. This has important implications in modeling cardiovascular flows, where spatial frequencies can vary substantially across anatomies and pathologies (e.g., aneurysms or stenoses). Recent evidence suggests that Fourier-based activation functions have desirable properties, and can potentially reduce spectral bias; however, the performance and adequacy of such Fourier activation functions have not yet been evaluated in patient-specific cardiovascular flow applications. METHODS The performance of sine activation function was evaluated against tanh and swish activation functions in a 1D advection-diffusion problem, an eccentric 2D stenosis model (Re=5000), and a patient-specific 3D aortic model (Re=823) under pulsatile flow conditions. CFD simulations were performed at high spatio-temporal resolution and data points were extracted for training the neural network. The number of training data points were normalized by L/D. The performance of the PINNs framework was evaluated with increasing number of training data points and across all three activation functions. RESULTS Our results demonstrate that sine activation function presents desirable characteristics, such as monotonic reduction in errors, relatively faster convergence, and accurate eigen spectra at higher modes, compared to tanh and swish activation functions. Interestingly, for all activation functions, the domain-averaged errors tended to asymptote at ≈15-20% despite substantial increase in training point density. For 2D eccentric stenosis, errors asymptoted at a sensor point density of 40L/D. For 3D patient-specific aorta, this asymptote was achieved at 180L/D for all three activation functions with an error of ≈15% although sine activation function demonstrated relatively faster convergence. CONCLUSIONS We have demonstrated that Fourier-based activation functions have higher performance in terms of accuracy and convergence properties for cardiovascular flow applications; however, inherent challenges of neural networks (e.g., spectral bias) can limit the accuracy to ≈15% under physiological, 3D patient-specific blood flow conditions.
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Affiliation(s)
- Arman Aghaee
- Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, 350 Victoria Street, Toronto, Ontario, M5B 2K3, Canada
| | - M Owais Khan
- Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, 350 Victoria Street, Toronto, Ontario, M5B 2K3, Canada.
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Xie J, Li H, Su S, Cheng J, Cai Q, Tan H, Zu L, Qu X, Han H. Quantitative analysis of molecular transport in the extracellular space using physics-informed neural network. Comput Biol Med 2024; 171:108133. [PMID: 38364661 DOI: 10.1016/j.compbiomed.2024.108133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 02/06/2024] [Accepted: 02/12/2024] [Indexed: 02/18/2024]
Abstract
The brain extracellular space (ECS), an irregular, extremely tortuous nanoscale space located between cells or between cells and blood vessels, is crucial for nerve cell survival. It plays a pivotal role in high-level brain functions such as memory, emotion, and sensation. However, the specific form of molecular transport within the ECS remain elusive. To address this challenge, this paper proposes a novel approach to quantitatively analyze the molecular transport within the ECS by solving an inverse problem derived from the advection-diffusion equation (ADE) using a physics-informed neural network (PINN). PINN provides a streamlined solution to the ADE without the need for intricate mathematical formulations or grid settings. Additionally, the optimization of PINN facilitates the automatic computation of the diffusion coefficient governing long-term molecule transport and the velocity of molecules driven by advection. Consequently, the proposed method allows for the quantitative analysis and identification of the specific pattern of molecular transport within the ECS through the calculation of the Péclet number. Experimental validation on two datasets of magnetic resonance images (MRIs) captured at different time points showcases the effectiveness of the proposed method. Notably, our simulations reveal identical molecular transport patterns between datasets representing rats with tracer injected into the same brain region. These findings highlight the potential of PINN as a promising tool for comprehensively exploring molecular transport within the ECS.
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Affiliation(s)
- Jiayi Xie
- Department of Automation, Tsinghua University, Beijing 100084, China; Institute of Medical Technology, Peking University Health Science Center, Beijing 100191, China
| | - Hongfeng Li
- Institute of Medical Technology, Peking University Health Science Center, Beijing 100191, China
| | - Shaoyi Su
- Institute of Medical Technology, Peking University Health Science Center, Beijing 100191, China
| | - Jin Cheng
- School of Mathematical Sciences, Fudan University, Shanghai 200433, China
| | - Qingrui Cai
- National Integrated Circuit Industry Education Integration Innovation Platform, School of Electronic Science and Engineering (National Model Microelectronics College), Xiamen University, Xiamen 361102, China; Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361102, China
| | - Hanbo Tan
- Institute of Medical Technology, Peking University Health Science Center, Beijing 100191, China
| | - Lingyun Zu
- Department of Endocrinology and Metabolism, Department of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital, Beijing 100191, China
| | - Xiaobo Qu
- National Integrated Circuit Industry Education Integration Innovation Platform, School of Electronic Science and Engineering (National Model Microelectronics College), Xiamen University, Xiamen 361102, China; Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361102, China
| | - Hongbin Han
- Institute of Medical Technology, Peking University Health Science Center, Beijing 100191, China; Department of Radiology, Peking University Third Hospital, Beijing 100191, China; Peking University Third Hospital, Beijing Key Laboratory of Magnetic Resonance Imaging Devices and Technology, Beijing 100191, China; NMPA key Laboratory of Evaluation of Medical Imaging Equipment and Technique, Beijing 100191, China.
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MacRaild M, Sarrami-Foroushani A, Lassila T, Frangi AF. Accelerated simulation methodologies for computational vascular flow modelling. J R Soc Interface 2024; 21:20230565. [PMID: 38350616 PMCID: PMC10864099 DOI: 10.1098/rsif.2023.0565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 01/12/2024] [Indexed: 02/15/2024] Open
Abstract
Vascular flow modelling can improve our understanding of vascular pathologies and aid in developing safe and effective medical devices. Vascular flow models typically involve solving the nonlinear Navier-Stokes equations in complex anatomies and using physiological boundary conditions, often presenting a multi-physics and multi-scale computational problem to be solved. This leads to highly complex and expensive models that require excessive computational time. This review explores accelerated simulation methodologies, specifically focusing on computational vascular flow modelling. We review reduced order modelling (ROM) techniques like zero-/one-dimensional and modal decomposition-based ROMs and machine learning (ML) methods including ML-augmented ROMs, ML-based ROMs and physics-informed ML models. We discuss the applicability of each method to vascular flow acceleration and the effectiveness of the method in addressing domain-specific challenges. When available, we provide statistics on accuracy and speed-up factors for various applications related to vascular flow simulation acceleration. Our findings indicate that each type of model has strengths and limitations depending on the context. To accelerate real-world vascular flow problems, we propose future research on developing multi-scale acceleration methods capable of handling the significant geometric variability inherent to such problems.
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Affiliation(s)
- Michael MacRaild
- Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), University of Leeds, Leeds, UK
- EPSRC Centre for Doctoral Training in Fluid Dynamics, University of Leeds, Leeds, UK
| | - Ali Sarrami-Foroushani
- Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), University of Leeds, Leeds, UK
- School of Health Science, University of Manchester, Manchester, UK
| | - Toni Lassila
- Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), University of Leeds, Leeds, UK
- School of Computing, University of Leeds, Leeds, UK
| | - Alejandro F. Frangi
- Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), University of Leeds, Leeds, UK
- School of Computer Science, University of Manchester, Manchester, UK
- School of Health Science, University of Manchester, Manchester, UK
- Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium
- Department of Electrical Engineering (ESAT), KU Leuven, Leuven, Belgium
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Luu HM, Park SH. SIMPLEX: Multiple phase-cycled bSSFP quantitative magnetization transfer imaging with physic-guided simulation learning of neural network. Neuroimage 2023; 284:120449. [PMID: 37951485 DOI: 10.1016/j.neuroimage.2023.120449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 09/21/2023] [Accepted: 11/07/2023] [Indexed: 11/14/2023] Open
Abstract
Most quantitative magnetization transfer (qMT) imaging methods require acquiring additional quantitative maps (such as T1) for data fitting. A method based on multiple phase-cycled bSSFP was recently proposed to enable high-resolution 3D qMT imaging based on least square fitting without any extra acquisition, and thus has high potential for simplifying the qMT procedure. However, the quantification of qMT parameters with this method was suboptimal, limiting its potential for clinical application despite its simpler protocol and higher spatial resolution. To improve the fitting of qMT data obtained with multiple phase-cycled bSSFP, we propose SIMulation-based Physics-guided Learning of neural network for qMT parameters EXtraction, or SIMPLEX. In contrast to previous deep learning supervised approaches for quantitative MR that require the acquisition of input data and corresponding ground truth for training, we leveraged the MR signal model to generate training samples without expensive data curation. The network was trained exclusively with simulation data by predicting the simulation parameters. The same network was applied directly to in-vivo data without additional training. The approach was verified with both simulation and in-vivo data. SIMPLEX showed a decrease in fitting mean squared error for all simulation data compared to the existing least-square fitting method. The in-vivo experiment revealed that the network performed well with the real in vivo data unseen during training. For all experiments, we observed that SIMPLEX consistently improved the quantification quality of the qMT parameters whilst being more robust to noise compared to the prior technique. The proposed SIMPLEX will expedite the routine clinical application of qMT by providing qMT parameters (exchange rate, pool fraction) as well as T1, T2, and ΔB0 maps simultaneously with high spatial resolution, better reliability, and reduced processing time.
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Affiliation(s)
- Huan Minh Luu
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Rm 1002, CMS (E16) Building, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, South Korea
| | - Sung-Hong Park
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Rm 1002, CMS (E16) Building, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, South Korea.
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9
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de Vries L, van Herten RLM, Hoving JW, Išgum I, Emmer BJ, Majoie CBLM, Marquering HA, Gavves E. Spatio-temporal physics-informed learning: A novel approach to CT perfusion analysis in acute ischemic stroke. Med Image Anal 2023; 90:102971. [PMID: 37778103 DOI: 10.1016/j.media.2023.102971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 07/20/2023] [Accepted: 09/11/2023] [Indexed: 10/03/2023]
Abstract
CT perfusion imaging is important in the imaging workup of acute ischemic stroke for evaluating affected cerebral tissue. CT perfusion analysis software produces cerebral perfusion maps from commonly noisy spatio-temporal CT perfusion data. High levels of noise can influence the results of CT perfusion analysis, necessitating software tuning. This work proposes a novel approach for CT perfusion analysis that uses physics-informed learning, an optimization framework that is robust to noise. In particular, we propose SPPINN: Spatio-temporal Perfusion Physics-Informed Neural Network and research spatio-temporal physics-informed learning. SPPINN learns implicit neural representations of contrast attenuation in CT perfusion scans using the spatio-temporal coordinates of the data and employs these representations to estimate a continuous representation of the cerebral perfusion parameters. We validate the approach on simulated data to quantify perfusion parameter estimation performance. Furthermore, we apply the method to in-house patient data and the public Ischemic Stroke Lesion Segmentation 2018 benchmark data to assess the correspondence between the perfusion maps and reference standard infarct core segmentations. Our method achieves accurate perfusion parameter estimates even with high noise levels and differentiates healthy tissue from infarcted tissue. Moreover, SPPINN perfusion maps accurately correspond with reference standard infarct core segmentations. Hence, we show that using spatio-temporal physics-informed learning for cerebral perfusion estimation is accurate, even in noisy CT perfusion data. The code for this work is available at https://github.com/lucasdevries/SPPINN.
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Affiliation(s)
- Lucas de Vries
- Amsterdam UMC location University of Amsterdam, Biomedical Engineering and Physics, Meibergdreef 9, Amsterdam, 1105 AZ, The Netherlands; Amsterdam UMC location University of Amsterdam, Radiology and Nuclear Medicine, Meibergdreef 9, Amsterdam, 1105 AZ, The Netherlands; Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands; Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands; Amsterdam Neuroscience, Amsterdam, The Netherlands.
| | - Rudolf L M van Herten
- Amsterdam UMC location University of Amsterdam, Biomedical Engineering and Physics, Meibergdreef 9, Amsterdam, 1105 AZ, The Netherlands; Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands; Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands
| | - Jan W Hoving
- Amsterdam UMC location University of Amsterdam, Radiology and Nuclear Medicine, Meibergdreef 9, Amsterdam, 1105 AZ, The Netherlands; Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Ivana Išgum
- Amsterdam UMC location University of Amsterdam, Biomedical Engineering and Physics, Meibergdreef 9, Amsterdam, 1105 AZ, The Netherlands; Amsterdam UMC location University of Amsterdam, Radiology and Nuclear Medicine, Meibergdreef 9, Amsterdam, 1105 AZ, The Netherlands; Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands; Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands; Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Bart J Emmer
- Amsterdam UMC location University of Amsterdam, Radiology and Nuclear Medicine, Meibergdreef 9, Amsterdam, 1105 AZ, The Netherlands; Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Charles B L M Majoie
- Amsterdam UMC location University of Amsterdam, Radiology and Nuclear Medicine, Meibergdreef 9, Amsterdam, 1105 AZ, The Netherlands; Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Henk A Marquering
- Amsterdam UMC location University of Amsterdam, Biomedical Engineering and Physics, Meibergdreef 9, Amsterdam, 1105 AZ, The Netherlands; Amsterdam UMC location University of Amsterdam, Radiology and Nuclear Medicine, Meibergdreef 9, Amsterdam, 1105 AZ, The Netherlands; Amsterdam Cardiovascular Sciences, Amsterdam, The Netherlands; Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Efstratios Gavves
- Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands
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10
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Grijalva C, Hale D, Wu L, Toosizadeh N, Laksari K. Hyper-acute effects of sub-concussive soccer headers on brain function and hemodynamics. Front Hum Neurosci 2023; 17:1191284. [PMID: 37780960 PMCID: PMC10538631 DOI: 10.3389/fnhum.2023.1191284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 08/29/2023] [Indexed: 10/03/2023] Open
Abstract
Introduction Sub-concussive head impacts in soccer are drawing increasing research attention regarding their acute and long-term effects as players may experience thousands of headers in a single season. During these impacts, the head experiences rapid acceleration similar to what occurs during a concussion, but without the clinical implications. The physical mechanism and response to repetitive impacts are not completely understood. The objective of this work was to examine the immediate functional outcomes of sub-concussive level impacts from soccer heading in a natural, non-laboratory environment. Methods Twenty university level soccer athletes were instrumented with sensor-mounted bite bars to record impacts from 10 consecutive soccer headers. Pre- and post-header measurements were collected to determine hyper-acute changes, i.e., within minutes after exposure. This included measuring blood flow velocity using transcranial Doppler (TCD) ultrasound, oxyhemoglobin concentration using functional near infrared spectroscopy imaging (fNIRS), and upper extremity dual-task (UEF) neurocognitive testing. Results On average, the athletes experienced 30.7 ± 8.9 g peak linear acceleration and 7.2 ± 3.1 rad/s peak angular velocity, respectively. Results from fNIRS measurements showed an increase in the brain oxygenation for the left prefrontal cortex (PC) (p = 0.002), and the left motor cortex (MC) (p = 0.007) following the soccer headers. Additional analysis of the fNIRS time series demonstrates increased sample entropy of the signal after the headers in the right PC (p = 0.02), right MC (p = 0.004), and left MC (p = 0.04). Discussion These combined results reveal some variations in brain oxygenation immediately detected after repetitive headers. Significant changes in balance and neurocognitive function were not observed in this study, indicating a mild level of head impacts. This is the first study to observe hemodynamic changes immediately after sub-concussive impacts using non-invasive portable imaging technology. In combination with head kinematic measurements, this information can give new insights and a framework for immediate monitoring of sub-concussive impacts on the head.
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Affiliation(s)
- Carissa Grijalva
- Department of Biomedical Engineering, University of Arizona, Tucson, AZ, United States
| | - Dallin Hale
- Department of Physiology, University of Arizona, Tucson, AZ, United States
| | - Lyndia Wu
- Department of Mechanical Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Nima Toosizadeh
- Department of Biomedical Engineering, University of Arizona, Tucson, AZ, United States
- Arizona Center for Aging, Department of Medicine, University of Arizona, Tucson, AZ, United States
| | - Kaveh Laksari
- Department of Biomedical Engineering, University of Arizona, Tucson, AZ, United States
- Department of Aerospace and Mechanical Engineering, University of Arizona, Tucson, AZ, United States
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11
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Zhang X, Mao B, Che Y, Kang J, Luo M, Qiao A, Liu Y, Anzai H, Ohta M, Guo Y, Li G. Physics-informed neural networks (PINNs) for 4D hemodynamics prediction: An investigation of optimal framework based on vascular morphology. Comput Biol Med 2023; 164:107287. [PMID: 37536096 DOI: 10.1016/j.compbiomed.2023.107287] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 07/06/2023] [Accepted: 07/28/2023] [Indexed: 08/05/2023]
Abstract
Hemodynamic parameters are of great significance in the clinical diagnosis and treatment of cardiovascular diseases. However, noninvasive, real-time and accurate acquisition of hemodynamics remains a challenge for current invasive detection and simulation algorithms. Here, we integrate computational fluid dynamics with our customized analysis framework based on a multi-attribute point cloud dataset and physics-informed neural networks (PINNs)-aided deep learning modules. This combination is implemented by our workflow that generates flow field datasets within two types of patient personalized models - aorta with fine coronary branches and abdominal aorta. Deep learning modules with or without an antecedent hierarchical structure model the flow field development and complete the mapping from spatial and temporal dimensions to 4D hemodynamics. 88,000 cases on 4 randomized partitions in 16 controlled trials reveal the hemodynamic landscape of spatio-temporal anisotropy within two types of personalized models, which demonstrates the effectiveness of PINN in predicting the space-time behavior of flow fields and gives the optimal deep learning framework for different blood vessels in terms of balancing the training cost and accuracy dimensions. The proposed framework shows intentional performance in computational cost, accuracy and visualization compared to currently prevalent methods, and has the potential for generalization to model flow fields and corresponding clinical metrics within vessels at different locations. We expect our framework to push the 4D hemodynamic predictions to the real-time level, and in statistically significant fashion, applicable to morphologically variable vessels.
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Affiliation(s)
- Xuelan Zhang
- School of Mathematics and Physics, University of Science and Technology Beijing, Beijing, 100083, China
| | - Baoyan Mao
- Beijing University of Chinese Medicine, Beijing 100029, China
| | - Yue Che
- School of Mathematics and Physics, University of Science and Technology Beijing, Beijing, 100083, China
| | - Jiaheng Kang
- School of Mathematics and Physics, University of Science and Technology Beijing, Beijing, 100083, China
| | - Mingyao Luo
- Department of Vascular Surgery, Fuwai Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, 100037, China; Department of Vascular Surgery, Fuwai Yunnan Cardiovascular Hospital, Affiliated Cardiovascular Hospital of Kunming Medical University, Kunming, 650102, China
| | - Aike Qiao
- Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Youjun Liu
- Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Hitomi Anzai
- Institute of Fluid Science, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai, 980-8577, Japan
| | - Makoto Ohta
- Institute of Fluid Science, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai, 980-8577, Japan
| | - Yuting Guo
- Department of Mechanical Engineering and Science, Kyoto University, Kyoto, 615-8540, Japan
| | - Gaoyang Li
- Institute of Fluid Science, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai, 980-8577, Japan.
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12
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Duman AN, Tatar AE. Topological data analysis for revealing dynamic brain reconfiguration in MEG data. PeerJ 2023; 11:e15721. [PMID: 37489123 PMCID: PMC10363343 DOI: 10.7717/peerj.15721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 06/16/2023] [Indexed: 07/26/2023] Open
Abstract
In recent years, the focus of the functional connectivity community has shifted from stationary approaches to the ones that include temporal dynamics. Especially, non-invasive electrophysiological data (magnetoencephalography/electroencephalography (MEG/EEG)) with high temporal resolution and good spatial coverage have made it possible to measure the fast alterations in the neural activity in the brain during ongoing cognition. In this article, we analyze dynamic brain reconfiguration using MEG images collected from subjects during the rest and the cognitive tasks. Our proposed topological data analysis method, called Mapper, produces biomarkers that differentiate cognitive tasks without prior spatial and temporal collapse of the data. The suggested method provides an interactive visualization of the rapid fluctuations in electrophysiological data during motor and cognitive tasks; hence, it has the potential to extract clinically relevant information at an individual level without temporal and spatial collapse.
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Affiliation(s)
- Ali Nabi Duman
- Department of Mathematics, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
| | - Ahmet E. Tatar
- Center for Information Technology, University of Groningen, Groningen, Netherlands
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13
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Kamali A, Sarabian M, Laksari K. Elasticity imaging using physics-informed neural networks: Spatial discovery of elastic modulus and Poisson's ratio. Acta Biomater 2023; 155:400-409. [PMID: 36402297 PMCID: PMC9805508 DOI: 10.1016/j.actbio.2022.11.024] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 11/07/2022] [Accepted: 11/09/2022] [Indexed: 11/18/2022]
Abstract
Elasticity imaging is a technique that discovers the spatial distribution of mechanical properties of tissue using deformation and force measurements under various loading conditions. Given the complexity of this discovery, most existing methods approximate only one material parameter while assuming homogeneous distributions for the others. We employ physics-informed neural networks (PINN) in linear elasticity problems to discover the space-dependent distribution of both elastic modulus (E) and Poisson's ratio (ν) simultaneously, using strain data, normal stress boundary conditions, and the governing physics. We validated our model on three examples. First, we experimentally loaded hydrogel samples with embedded stiff inclusions, representing tumorous tissue, and compared the approximations against ground truth determined through tensile tests. Next, using data from finite element simulation of a rectangular domain containing a stiff circular inclusion, the PINN model accurately localized the inclusion and estimated both E and ν. We observed that in a heterogeneous domain, assuming a homogeneous ν distribution increases estimation error for stiffness as well as the area of the stiff inclusion, which could have clinical importance when determining size and stiffness of tumorous tissue. Finally, our model accurately captured spatial distribution of mechanical properties and the tissue interfaces on data from another computational model, simulating uniaxial loading of a rectangular hydrogel sample containing a human brain slice with distinct gray matter and white matter regions and complex geometrical features. This elasticity imaging implementation has the potential to be used in clinical imaging scenarios to reliably discover the spatial distribution of mechanical parameters and identify material interfaces such as tumors. STATEMENT OF SIGNIFICANCE: Our work is the first implementation of physics-informed neural networks to reconstruct both material parameters - Young's modulus and Poisson's ratio - and stress distributions for isotropic linear elastic materials by having deformation and force measurements. We comprehensively validate our model using experimental measurements and synthetic data generated using finite element modeling. Our method can be implemented in clinical elasticity imaging scenarios to improve diagnosis of tumors and for mechanical characterization of biomaterials and biological tissues in a minimally invasive manner.
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Affiliation(s)
- Ali Kamali
- Department of Biomedical Engineering, University of Arizona College of Engineering, Tucson, AZ, United States
| | - Mohammad Sarabian
- Department of Biomedical Engineering, University of Arizona College of Engineering, Tucson, AZ, United States
| | - Kaveh Laksari
- Department of Biomedical Engineering, University of Arizona College of Engineering, Tucson, AZ, United States; Department of Aerospace and Mechanical Engineering, University of Arizona College of Engineering, Tucson, AZ, United States.
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14
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Investigating molecular transport in the human brain from MRI with physics-informed neural networks. Sci Rep 2022; 12:15475. [PMID: 36104360 PMCID: PMC9474534 DOI: 10.1038/s41598-022-19157-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 08/24/2022] [Indexed: 11/23/2022] Open
Abstract
In recent years, a plethora of methods combining neural networks and partial differential equations have been developed. A widely known example are physics-informed neural networks, which solve problems involving partial differential equations by training a neural network. We apply physics-informed neural networks and the finite element method to estimate the diffusion coefficient governing the long term spread of molecules in the human brain from magnetic resonance images. Synthetic testcases are created to demonstrate that the standard formulation of the physics-informed neural network faces challenges with noisy measurements in our application. Our numerical results demonstrate that the residual of the partial differential equation after training needs to be small for accurate parameter recovery. To achieve this, we tune the weights and the norms used in the loss function and use residual based adaptive refinement of training points. We find that the diffusion coefficient estimated from magnetic resonance images with physics-informed neural networks becomes consistent with results from a finite element based approach when the residuum after training becomes small. The observations presented here are an important first step towards solving inverse problems on cohorts of patients in a semi-automated fashion with physics-informed neural networks.
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