Numerical Modeling and High-Speed Parallel Computing: New Perspectives on Tomographic Microwave Imaging for Brain Stroke Detection and Monitoring

Tournier, Pierre-Henri; Bonazzoli, Marcella; Dolean, Victorita; Rapetti, Francesca; Hecht, Frédéric; Nataf, Frédéric; Aliferis, Iannis; Kanfoud, Ibtissam El; Migliaccio, Claire; Buhan, Maya de; Darbas, Marion; Semenov, Serguei; Pichot, Christian

doi:10.1109/map.2017.2731199

Cited by 115 publications

(67 citation statements)

References 22 publications

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“…Brain stroke is worldwide a major cause of permanent disability and death . Strokes can be classified into 2 categories: (1) ischemic (almost 85% of occurrences) when they are caused by the abrupt interruption of the blood flow within a region of the brain and (2) hemorrhagic when due to some blood leakage/bleeding phenomena.…”

Section: Introductionmentioning

confidence: 99%

“…Strokes can be classified into 2 categories: (1) ischemic (almost 85% of occurrences) when they are caused by the abrupt interruption of the blood flow within a region of the brain and (2) hemorrhagic when due to some blood leakage/bleeding phenomena. The latter ones are the most lethal pathology . Recognizing the type of stroke at hand and then inferring its position play a key role towards a suitable and effective clinical treatment just within few hours from the first symptoms .…”

Section: Introductionmentioning

confidence: 99%

“…Towards this end, several leading‐edge research efforts consider the iterative solution of a forward scattering problem of a complex trial brain model to minimize the mismatch between measurement data and simulated ones. For instance, recent attempts employ massive parallel computing to reduce the arising computational burden and to match clinical time constraints, while graphics processing units ( GPU s) have been adopted in Ref. to speed up the reconstruction process.…”

Section: Introductionmentioning

confidence: 99%

“…Brain stroke is worldwide a major cause of permanent disability and death. [1][2][3][4][5] Strokes can be classified into 2 categories:…”

Section: Introductionmentioning

confidence: 99%

“…The latter ones are the most lethal pathology. 1 Recognizing the type of stroke at hand and then inferring its position play a key role towards a suitable and effective clinical treatment just within few hours from the first symptoms. 2 Nowadays, such information mainly arise from 2D images generated through computed tomography (CT) and magnetic resonance imaging (MRI) that, despite their high reliability and robustness, need bulky and expensive instruments, and/or ionizing radiations, which are not suitable for a diffused (ie, many users) and continuous/repeated monitoring.…”

Section: Introductionmentioning

confidence: 99%

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Instantaneous brain stroke classification and localization from real scattering data

Salucci

Gelmini

Vrba

et al. 2018

Micro & Optical Tech Letters

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This work presents a 2‐step Learning‐by‐Examples approach for the real‐time classification of hemorrhagic/ischemic brain strokes and their successive localization from microwave scattering data collected around the human head. An experimental assessment against laboratory‐controlled data is performed to assess the potentialities of the proposed approach towards a reliable monitoring and instantaneous diagnosis clinic protocol.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Brain stroke is worldwide a major cause of permanent disability and death. [1][2][3][4][5] Strokes can be classified into 2 categories:…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Instantaneous brain stroke classification and localization from real scattering data

Salucci

Gelmini

Vrba

et al. 2018

Micro & Optical Tech Letters

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On the utilization of the adjoint method in microwave tomography

Soydan,

Top,

Gençer

2024

Numer Methods Biomed Eng

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In microwave imaging, the adjoint method is widely used for the efficient calculation of the update direction, which is then used to update the unknown model parameter. However, the utilization and the formulation of the adjoint method differ significantly depending on the imaging scenario and the applied optimization algorithm. Because of the problem‐specific nature of the adjoint formulations, the dissimilarities between the adjoint calculations may be overlooked. Here, we have classified the adjoint method formulations into two groups: the direct and indirect methods. The direct method involves calculating the derivative of the cost function, whereas, in the indirect method, the derivative of the predicted data is calculated. In this review, the direct and indirect adjoint methods are presented, compared, and discussed. The formulations are explicitly derived using the two‐dimensional wave equation in frequency and time domains. Finite‐difference time‐domain simulations are conducted to show the different uses of the adjoint methods for both single source‐multiple receiver, and multiple transceiver scenarios. This study demonstrated that an appropriate adjoint method selection is significant to achieve improved computational efficiency for the applied optimization algorithm.

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Accelerating magnetic induction tomography‐based imaging through heterogeneous parallel computing

Walker

Kramer

Biebl

et al. 2019

Concurrency and Computation

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Magnetic Induction Tomography (MIT) is a non-invasive imaging technique, which has applications in both industrial and clinical settings. In essence, it is capable of reconstructing the electromagnetic parameters of an object from measurements made on its surface. With the exploitation of parallelism, it is possible to achieve high quality inexpensive MIT images for biomedical applications on clinically relevant time scales. In this paper we investigate the performance of different parallel implementations of the forward eddy current problem, which is the main computational component of the inverse problem through which measured voltages are converted into images. We show that a heterogeneous parallel method that exploits multiple CPUs and GPUs can provide a high level of parallel scaling, leading to considerably improved runtimes. We also show how multiple GPUs can be used in conjunction with deal.II, awidely-used open source finite element library. KEYWORDScomputational electromagnetics, magnetic induction tomography, parallel applications INTRODUCTIONMagnetic Induction Tomography (MIT) is a non-invasive imaging technique, which has applications in both industrial and clinical settings. In essence, it is capable of reconstructing the electromagnetic parameters of an object from measurements made on its surface. These parameters are the permittivity, , the permeability, , and the conductivity, . An MIT device consists of two sets of coils placed around the boundary of the object to be imaged. The first set of coils is used for the purpose of excitation, and by passing a current through each coil in turn, a primary magnetic field is created. The second set of coils is then used for measurement. This procedure causes an eddy current when each of the primary magnetic fields interacts with a conducting body inducing secondary magnetic fields, and hence voltages, that are measured in the second set of coils. In order to estimate the electromagnetic properties of the material, ( , , ), from the induced currents and measured voltage, an inverse problem must be solved. In many practical applications, the distribution of one or more of these material parameters is assumed to be constant throughout the medium of interest.Conventional imaging techniques for imaging cerebral stroke, such as Magnetic Resonance Imaging (MRI) and Computer Tomography (CT), are expensive. Although MRI may be used for real-time image reconstruction, 1 it has been proposed that MIT can offer a low cost alternative in the first stages of diagnosis. 2 However, the low conductivity contrast between biomedical tissues presents significant challenges to MIT, and there are considerable difficulties in employing current computational techniques to solve the associated inverse problem. 3,4 Enabling MIT to take the step from being an experimental technique, which has already received some clinical interest, to become a viable imaging technique for the detection and monitoring of conditions, such as cerebral stroke, requires a step change in the quality of ...

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Numerical Modeling and High-Speed Parallel Computing: New Perspectives on Tomographic Microwave Imaging for Brain Stroke Detection and Monitoring

Cited by 115 publications

References 22 publications

Instantaneous brain stroke classification and localization from real scattering data

Instantaneous brain stroke classification and localization from real scattering data

On the utilization of the adjoint method in microwave tomography

Accelerating magnetic induction tomography‐based imaging through heterogeneous parallel computing

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