Abstract

Cerebral subdural hematomas due to trauma can easily worsen suddenly due to the rupture of blood vessels in the brain after the condition is stabilized. Therefore, continuous monitoring of the size of cerebral subdural hematomas has important clinical significance. To achieve fast, real-time, noninvasive, and accurate monitoring of subdural hematomas, a cerebral subdural hematoma monitoring method combining brain magnetic resonance imaging (MRI) image guidance, diffusion optical tomography technology, and deep learning is proposed in this manuscript. First, an MRI brain image is segmented to obtain a three-dimensional multi-layer brain model with structures and parameters matching a real brain. Then, a near-infrared light source and detectors (source-detector separations ranging from 0.5 to 6.5 cm) were placed on the model to achieve fast, real-time and noninvasive acquisition of intracranial hematoma information. Finally, a deep learning method is used to obtain accurate reconstructed images of cerebral subdural hematomas. The experimental results show that the reconstruction effect of stacked auto-encoder with the mean volume error of 0.1 ml is better than the result reconstructed by algebraic reconstruction techniques with the mean volume error of 0.9 ml. Under different signal-to-noise ratios, the curve fitting R2 between the actual blood volume of a simulated hematoma and a reconstructed hematoma is more than 0.95. We conclude that the proposed monitoring method can realize fast, noninvasive, real-time, and accurate monitoring of subdural hematomas, and can provide a technical basis for continuous wearable subdural hematoma monitoring equipment.

© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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    [Crossref]
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    [Crossref]

2019 (3)

J. Wang, J. Lin, Y. Chen, C. G. Welle, and T. J. Pfefer, “Phantom-based evaluation of near-infrared intracranial hematoma detector performance,” J. Biomed. Opt. 24(4), 1 (2019).
[Crossref]

L. Guo, F. Liu, C. Cai, J. Liu, and G. Zhang, “3D deep encoder–decoder network for fluorescence molecular tomography,” Opt. Lett. 44(8), 1892–1895 (2019).
[Crossref]

H. Wang, N. Wu, Y. Cai, L. Ren, Z. Zhao, G. Han, and J. Wang, “Optimization of Reconstruction Accuracy of Anomaly Position Based on Stacked Auto-Encoder Neural Networks,” IEEE Access 7, 116578–116584 (2019).
[Crossref]

2018 (9)

Y. Gao, K. Wang, Y. An, S. Jiang, H. Meng, and J. Tian, “Nonmodel-based bioluminescence tomography using a machine-learning reconstruction strategy,” Optica 5(11), 1451–1454 (2018).
[Crossref]

H. Wang, X. Feng, B. Shi, W. Liang, Y. Chen, J. Wang, and X. Li, “Signal-to-noise ratio analysis and improvement for fluorescence tomography imaging,” Rev. Sci. Instrum. 89(9), 093114 (2018).
[Crossref]

Y. Sun, Z. Xia, and U. S. Kamilov, “Efficient and accurate inversion of multiple scattering with deep learning,” Opt. Express 26(11), 14678–14688 (2018).
[Crossref]

C. Cai, K. Deng, C. Ma, and J. Luo, “End-to-end deep neural network for optical inversion in quantitative photoacoustic imaging,” Opt. Lett. 43(12), 2752–2755 (2018).
[Crossref]

D. Ancora, L. Qiu, G. Zacharakis, and L. Spinelli, “Noninvasive optical estimation of CSF thickness for brain-atrophy monitoring,” Biomed. Opt. Express 9(9), 4094 (2018).
[Crossref]

H. Wang, L. Ren, Z. Zhao, and J. Wang, “Fast localization method of an anomaly in tissue based on differential optical density,” Biomed. Opt. Express 9(5), 2018–2026 (2018).
[Crossref]

P. Li, Z. Chen, L. T. Yang, and J. Gao, “An improved stacked auto-encoder for network traffic flow classification,” IEEE Network 32(6), 22–27 (2018).
[Crossref]

M. Alayed, M. A. Naser, I. Aden-Ali, and M. Jamal Deen, “Time-resolved diffuse optical tomography system using an accelerated inverse problem solver,” Opt. Express 26(2), 963 (2018).
[Crossref]

W. Lu, S. Daniel Lighter, and I. B. Styles, “L1-norm Based Nonlinear Reconstruction Improves Quantitative Accuracy of Spectral Diffuse Optical Tomography,” Biomed. Opt. Express 9(4), 1423 (2018).
[Crossref]

2017 (5)

H. Zhao and R. J. Cooper, “Review of recent progress toward a fiberless, whole-scalp diffuse optical tomography system,” Neurophotonics 5(1), 011012 (2017).
[Crossref]

L. Xu, X. Tao, W. Liu, and Y. Li, “Portable near-infrared rapid detection of intracranial hemorrhage in Chinese population,” J. Clin. Neurosci. 40, 136–146 (2017).
[Crossref]

J. Adler and O. Öktem, “Solving ill-posed inverse problems using iterative deep neural networks,” Inverse Problems 33(12), 124007 (2017).
[Crossref]

L. Jiang, Z. Ge, and Z. Song, “Semi-supervised fault classification based on dynamic Sparse Stacked auto-encoders model,” Chemom. Intell. Lab. Syst. 168, 72–83 (2017).
[Crossref]

E. Kang, J. Min, and J. C. Ye, “A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction,” Med. Phys. 44(10), e360–e375 (2017).
[Crossref]

2014 (1)

F. Tian and H. Liu, “Depth-compensated diffuse optical tomography enhanced by general linear model analysis and an anatomical atlas of human head,” NeuroImage 85, 166–180 (2014).
[Crossref]

2013 (1)

M. Jermyn, H. Ghadyani, M. A. Mastanduno, W. Turner, S. C. Davis, H. Dehghani, and B. W. Pogue, “Fast segmentation and high-quality three-dimensional volume mesh creation from medical images for diffuse optical tomography,” J. Biomed. Opt. 18(8), 086007 (2013).
[Crossref]

2011 (2)

H. Ayaz, B. B. Dor, D. Solt, and B. Onaral, “Infrascanner: Cost Effective, Mobile Medical Imaging System for Detecting Hematomas,” J. Med. Devices 5(2), 027540 (2011).
[Crossref]

S. Proskurin, “Using late arriving photons for diffuse optical tomography of biological objects,” Quantum Electron. 41(5), 402–406 (2011).
[Crossref]

2010 (1)

C. S. Robertson, E. L. Zager, R. K. Narayan, and N. Handly, “Clinical Evaluation of a Portable Near-Infrared Device for Detection of Traumatic Intracranial Hematomas,” J Neurotraum 27(9), 1597–1604 (2010).
[Crossref]

2008 (1)

H. Ghalenoui, H. Saidi, M. Azar, and S. T. Yahyavi, “Near-Infrared Laser Spectroscopy as a Screening Tool for Detecting Hematoma in Patients with Head Trauma,” Prehosp. Disaster med. 23(6), 558–561 (2008).
[Crossref]

1995 (1)

C. S. Robertson, S. P. Gopinath, and B. Chance, “A New Application for Near-Infrared Spectroscopy: Detection of Delayed Intracranial Hematomas after Head Injury,” J Neurotraum 12(4), 591–600 (1995).
[Crossref]

Aden-Ali, I.

Adler, J.

J. Adler and O. Öktem, “Solving ill-posed inverse problems using iterative deep neural networks,” Inverse Problems 33(12), 124007 (2017).
[Crossref]

Alayed, M.

An, Y.

Ancora, D.

Ayaz, H.

H. Ayaz, B. B. Dor, D. Solt, and B. Onaral, “Infrascanner: Cost Effective, Mobile Medical Imaging System for Detecting Hematomas,” J. Med. Devices 5(2), 027540 (2011).
[Crossref]

Azar, M.

H. Ghalenoui, H. Saidi, M. Azar, and S. T. Yahyavi, “Near-Infrared Laser Spectroscopy as a Screening Tool for Detecting Hematoma in Patients with Head Trauma,” Prehosp. Disaster med. 23(6), 558–561 (2008).
[Crossref]

Cai, C.

Cai, Y.

H. Wang, N. Wu, Y. Cai, L. Ren, Z. Zhao, G. Han, and J. Wang, “Optimization of Reconstruction Accuracy of Anomaly Position Based on Stacked Auto-Encoder Neural Networks,” IEEE Access 7, 116578–116584 (2019).
[Crossref]

Chance, B.

C. S. Robertson, S. P. Gopinath, and B. Chance, “A New Application for Near-Infrared Spectroscopy: Detection of Delayed Intracranial Hematomas after Head Injury,” J Neurotraum 12(4), 591–600 (1995).
[Crossref]

Chen, Y.

J. Wang, J. Lin, Y. Chen, C. G. Welle, and T. J. Pfefer, “Phantom-based evaluation of near-infrared intracranial hematoma detector performance,” J. Biomed. Opt. 24(4), 1 (2019).
[Crossref]

H. Wang, X. Feng, B. Shi, W. Liang, Y. Chen, J. Wang, and X. Li, “Signal-to-noise ratio analysis and improvement for fluorescence tomography imaging,” Rev. Sci. Instrum. 89(9), 093114 (2018).
[Crossref]

Chen, Z.

P. Li, Z. Chen, L. T. Yang, and J. Gao, “An improved stacked auto-encoder for network traffic flow classification,” IEEE Network 32(6), 22–27 (2018).
[Crossref]

Cooper, R. J.

H. Zhao and R. J. Cooper, “Review of recent progress toward a fiberless, whole-scalp diffuse optical tomography system,” Neurophotonics 5(1), 011012 (2017).
[Crossref]

Daniel Lighter, S.

Davis, S. C.

M. Jermyn, H. Ghadyani, M. A. Mastanduno, W. Turner, S. C. Davis, H. Dehghani, and B. W. Pogue, “Fast segmentation and high-quality three-dimensional volume mesh creation from medical images for diffuse optical tomography,” J. Biomed. Opt. 18(8), 086007 (2013).
[Crossref]

Dehghani, H.

M. Jermyn, H. Ghadyani, M. A. Mastanduno, W. Turner, S. C. Davis, H. Dehghani, and B. W. Pogue, “Fast segmentation and high-quality three-dimensional volume mesh creation from medical images for diffuse optical tomography,” J. Biomed. Opt. 18(8), 086007 (2013).
[Crossref]

Deng, K.

Dor, B. B.

H. Ayaz, B. B. Dor, D. Solt, and B. Onaral, “Infrascanner: Cost Effective, Mobile Medical Imaging System for Detecting Hematomas,” J. Med. Devices 5(2), 027540 (2011).
[Crossref]

Feng, X.

H. Wang, X. Feng, B. Shi, W. Liang, Y. Chen, J. Wang, and X. Li, “Signal-to-noise ratio analysis and improvement for fluorescence tomography imaging,” Rev. Sci. Instrum. 89(9), 093114 (2018).
[Crossref]

Gao, J.

P. Li, Z. Chen, L. T. Yang, and J. Gao, “An improved stacked auto-encoder for network traffic flow classification,” IEEE Network 32(6), 22–27 (2018).
[Crossref]

Gao, Y.

Ge, Z.

L. Jiang, Z. Ge, and Z. Song, “Semi-supervised fault classification based on dynamic Sparse Stacked auto-encoders model,” Chemom. Intell. Lab. Syst. 168, 72–83 (2017).
[Crossref]

Ghadyani, H.

M. Jermyn, H. Ghadyani, M. A. Mastanduno, W. Turner, S. C. Davis, H. Dehghani, and B. W. Pogue, “Fast segmentation and high-quality three-dimensional volume mesh creation from medical images for diffuse optical tomography,” J. Biomed. Opt. 18(8), 086007 (2013).
[Crossref]

Ghalenoui, H.

H. Ghalenoui, H. Saidi, M. Azar, and S. T. Yahyavi, “Near-Infrared Laser Spectroscopy as a Screening Tool for Detecting Hematoma in Patients with Head Trauma,” Prehosp. Disaster med. 23(6), 558–561 (2008).
[Crossref]

Gopinath, S. P.

C. S. Robertson, S. P. Gopinath, and B. Chance, “A New Application for Near-Infrared Spectroscopy: Detection of Delayed Intracranial Hematomas after Head Injury,” J Neurotraum 12(4), 591–600 (1995).
[Crossref]

Guo, L.

Han, G.

H. Wang, N. Wu, Y. Cai, L. Ren, Z. Zhao, G. Han, and J. Wang, “Optimization of Reconstruction Accuracy of Anomaly Position Based on Stacked Auto-Encoder Neural Networks,” IEEE Access 7, 116578–116584 (2019).
[Crossref]

Handly, N.

C. S. Robertson, E. L. Zager, R. K. Narayan, and N. Handly, “Clinical Evaluation of a Portable Near-Infrared Device for Detection of Traumatic Intracranial Hematomas,” J Neurotraum 27(9), 1597–1604 (2010).
[Crossref]

Jamal Deen, M.

Jermyn, M.

M. Jermyn, H. Ghadyani, M. A. Mastanduno, W. Turner, S. C. Davis, H. Dehghani, and B. W. Pogue, “Fast segmentation and high-quality three-dimensional volume mesh creation from medical images for diffuse optical tomography,” J. Biomed. Opt. 18(8), 086007 (2013).
[Crossref]

Jiang, L.

L. Jiang, Z. Ge, and Z. Song, “Semi-supervised fault classification based on dynamic Sparse Stacked auto-encoders model,” Chemom. Intell. Lab. Syst. 168, 72–83 (2017).
[Crossref]

Jiang, S.

Kamilov, U. S.

Kang, E.

E. Kang, J. Min, and J. C. Ye, “A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction,” Med. Phys. 44(10), e360–e375 (2017).
[Crossref]

Li, P.

P. Li, Z. Chen, L. T. Yang, and J. Gao, “An improved stacked auto-encoder for network traffic flow classification,” IEEE Network 32(6), 22–27 (2018).
[Crossref]

Li, X.

H. Wang, X. Feng, B. Shi, W. Liang, Y. Chen, J. Wang, and X. Li, “Signal-to-noise ratio analysis and improvement for fluorescence tomography imaging,” Rev. Sci. Instrum. 89(9), 093114 (2018).
[Crossref]

Li, Y.

L. Xu, X. Tao, W. Liu, and Y. Li, “Portable near-infrared rapid detection of intracranial hemorrhage in Chinese population,” J. Clin. Neurosci. 40, 136–146 (2017).
[Crossref]

Liang, W.

H. Wang, X. Feng, B. Shi, W. Liang, Y. Chen, J. Wang, and X. Li, “Signal-to-noise ratio analysis and improvement for fluorescence tomography imaging,” Rev. Sci. Instrum. 89(9), 093114 (2018).
[Crossref]

Lin, J.

J. Wang, J. Lin, Y. Chen, C. G. Welle, and T. J. Pfefer, “Phantom-based evaluation of near-infrared intracranial hematoma detector performance,” J. Biomed. Opt. 24(4), 1 (2019).
[Crossref]

Liu, F.

Liu, H.

F. Tian and H. Liu, “Depth-compensated diffuse optical tomography enhanced by general linear model analysis and an anatomical atlas of human head,” NeuroImage 85, 166–180 (2014).
[Crossref]

Liu, J.

Liu, W.

L. Xu, X. Tao, W. Liu, and Y. Li, “Portable near-infrared rapid detection of intracranial hemorrhage in Chinese population,” J. Clin. Neurosci. 40, 136–146 (2017).
[Crossref]

Lu, W.

Luo, J.

Ma, C.

Mastanduno, M. A.

M. Jermyn, H. Ghadyani, M. A. Mastanduno, W. Turner, S. C. Davis, H. Dehghani, and B. W. Pogue, “Fast segmentation and high-quality three-dimensional volume mesh creation from medical images for diffuse optical tomography,” J. Biomed. Opt. 18(8), 086007 (2013).
[Crossref]

Meng, H.

Min, J.

E. Kang, J. Min, and J. C. Ye, “A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction,” Med. Phys. 44(10), e360–e375 (2017).
[Crossref]

Narayan, R. K.

C. S. Robertson, E. L. Zager, R. K. Narayan, and N. Handly, “Clinical Evaluation of a Portable Near-Infrared Device for Detection of Traumatic Intracranial Hematomas,” J Neurotraum 27(9), 1597–1604 (2010).
[Crossref]

Naser, M. A.

Öktem, O.

J. Adler and O. Öktem, “Solving ill-posed inverse problems using iterative deep neural networks,” Inverse Problems 33(12), 124007 (2017).
[Crossref]

Onaral, B.

H. Ayaz, B. B. Dor, D. Solt, and B. Onaral, “Infrascanner: Cost Effective, Mobile Medical Imaging System for Detecting Hematomas,” J. Med. Devices 5(2), 027540 (2011).
[Crossref]

Pfefer, T. J.

J. Wang, J. Lin, Y. Chen, C. G. Welle, and T. J. Pfefer, “Phantom-based evaluation of near-infrared intracranial hematoma detector performance,” J. Biomed. Opt. 24(4), 1 (2019).
[Crossref]

Pogue, B. W.

M. Jermyn, H. Ghadyani, M. A. Mastanduno, W. Turner, S. C. Davis, H. Dehghani, and B. W. Pogue, “Fast segmentation and high-quality three-dimensional volume mesh creation from medical images for diffuse optical tomography,” J. Biomed. Opt. 18(8), 086007 (2013).
[Crossref]

Proskurin, S.

S. Proskurin, “Using late arriving photons for diffuse optical tomography of biological objects,” Quantum Electron. 41(5), 402–406 (2011).
[Crossref]

Qiu, L.

Ren, L.

H. Wang, N. Wu, Y. Cai, L. Ren, Z. Zhao, G. Han, and J. Wang, “Optimization of Reconstruction Accuracy of Anomaly Position Based on Stacked Auto-Encoder Neural Networks,” IEEE Access 7, 116578–116584 (2019).
[Crossref]

H. Wang, L. Ren, Z. Zhao, and J. Wang, “Fast localization method of an anomaly in tissue based on differential optical density,” Biomed. Opt. Express 9(5), 2018–2026 (2018).
[Crossref]

Robertson, C. S.

C. S. Robertson, E. L. Zager, R. K. Narayan, and N. Handly, “Clinical Evaluation of a Portable Near-Infrared Device for Detection of Traumatic Intracranial Hematomas,” J Neurotraum 27(9), 1597–1604 (2010).
[Crossref]

C. S. Robertson, S. P. Gopinath, and B. Chance, “A New Application for Near-Infrared Spectroscopy: Detection of Delayed Intracranial Hematomas after Head Injury,” J Neurotraum 12(4), 591–600 (1995).
[Crossref]

Saidi, H.

H. Ghalenoui, H. Saidi, M. Azar, and S. T. Yahyavi, “Near-Infrared Laser Spectroscopy as a Screening Tool for Detecting Hematoma in Patients with Head Trauma,” Prehosp. Disaster med. 23(6), 558–561 (2008).
[Crossref]

Shi, B.

H. Wang, X. Feng, B. Shi, W. Liang, Y. Chen, J. Wang, and X. Li, “Signal-to-noise ratio analysis and improvement for fluorescence tomography imaging,” Rev. Sci. Instrum. 89(9), 093114 (2018).
[Crossref]

Solt, D.

H. Ayaz, B. B. Dor, D. Solt, and B. Onaral, “Infrascanner: Cost Effective, Mobile Medical Imaging System for Detecting Hematomas,” J. Med. Devices 5(2), 027540 (2011).
[Crossref]

Song, Z.

L. Jiang, Z. Ge, and Z. Song, “Semi-supervised fault classification based on dynamic Sparse Stacked auto-encoders model,” Chemom. Intell. Lab. Syst. 168, 72–83 (2017).
[Crossref]

Spinelli, L.

Styles, I. B.

Sun, Y.

Tao, X.

L. Xu, X. Tao, W. Liu, and Y. Li, “Portable near-infrared rapid detection of intracranial hemorrhage in Chinese population,” J. Clin. Neurosci. 40, 136–146 (2017).
[Crossref]

Tian, F.

F. Tian and H. Liu, “Depth-compensated diffuse optical tomography enhanced by general linear model analysis and an anatomical atlas of human head,” NeuroImage 85, 166–180 (2014).
[Crossref]

Tian, J.

Turner, W.

M. Jermyn, H. Ghadyani, M. A. Mastanduno, W. Turner, S. C. Davis, H. Dehghani, and B. W. Pogue, “Fast segmentation and high-quality three-dimensional volume mesh creation from medical images for diffuse optical tomography,” J. Biomed. Opt. 18(8), 086007 (2013).
[Crossref]

Wang, H.

H. Wang, N. Wu, Y. Cai, L. Ren, Z. Zhao, G. Han, and J. Wang, “Optimization of Reconstruction Accuracy of Anomaly Position Based on Stacked Auto-Encoder Neural Networks,” IEEE Access 7, 116578–116584 (2019).
[Crossref]

H. Wang, L. Ren, Z. Zhao, and J. Wang, “Fast localization method of an anomaly in tissue based on differential optical density,” Biomed. Opt. Express 9(5), 2018–2026 (2018).
[Crossref]

H. Wang, X. Feng, B. Shi, W. Liang, Y. Chen, J. Wang, and X. Li, “Signal-to-noise ratio analysis and improvement for fluorescence tomography imaging,” Rev. Sci. Instrum. 89(9), 093114 (2018).
[Crossref]

Wang, J.

J. Wang, J. Lin, Y. Chen, C. G. Welle, and T. J. Pfefer, “Phantom-based evaluation of near-infrared intracranial hematoma detector performance,” J. Biomed. Opt. 24(4), 1 (2019).
[Crossref]

H. Wang, N. Wu, Y. Cai, L. Ren, Z. Zhao, G. Han, and J. Wang, “Optimization of Reconstruction Accuracy of Anomaly Position Based on Stacked Auto-Encoder Neural Networks,” IEEE Access 7, 116578–116584 (2019).
[Crossref]

H. Wang, L. Ren, Z. Zhao, and J. Wang, “Fast localization method of an anomaly in tissue based on differential optical density,” Biomed. Opt. Express 9(5), 2018–2026 (2018).
[Crossref]

H. Wang, X. Feng, B. Shi, W. Liang, Y. Chen, J. Wang, and X. Li, “Signal-to-noise ratio analysis and improvement for fluorescence tomography imaging,” Rev. Sci. Instrum. 89(9), 093114 (2018).
[Crossref]

Wang, K.

Welle, C. G.

J. Wang, J. Lin, Y. Chen, C. G. Welle, and T. J. Pfefer, “Phantom-based evaluation of near-infrared intracranial hematoma detector performance,” J. Biomed. Opt. 24(4), 1 (2019).
[Crossref]

Wu, N.

H. Wang, N. Wu, Y. Cai, L. Ren, Z. Zhao, G. Han, and J. Wang, “Optimization of Reconstruction Accuracy of Anomaly Position Based on Stacked Auto-Encoder Neural Networks,” IEEE Access 7, 116578–116584 (2019).
[Crossref]

Xia, Z.

Xu, L.

L. Xu, X. Tao, W. Liu, and Y. Li, “Portable near-infrared rapid detection of intracranial hemorrhage in Chinese population,” J. Clin. Neurosci. 40, 136–146 (2017).
[Crossref]

Yahyavi, S. T.

H. Ghalenoui, H. Saidi, M. Azar, and S. T. Yahyavi, “Near-Infrared Laser Spectroscopy as a Screening Tool for Detecting Hematoma in Patients with Head Trauma,” Prehosp. Disaster med. 23(6), 558–561 (2008).
[Crossref]

Yang, L. T.

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Figures (8)

Fig. 1.
Fig. 1. Hierarchical structure of the head model.
Fig. 2.
Fig. 2. Schematic diagram of the brain model. (a) Brain ROI selection; (b) three-layer brain simulation model; (c) brain model Single-source Multi-Detector distribution.
Fig. 3.
Fig. 3. Structure of the auto-encoder.
Fig. 4.
Fig. 4. SAE network structure. (a) Pre-training network structure; (b) NN-training network structure.
Fig. 5.
Fig. 5. Comparison of reconstruction effect based on SAE and traditional ART. (a) 3D rendering visualized images of real subdural hematomas; (b) slice images of real subdural hematoma; (c) subdural hematoma slice images reconstructed by the SAE network; (d) subdural hematoma slice images reconstructed by the ART algorithm.
Fig. 6.
Fig. 6. VE analysis of real subdural hematoma and reconstructed subdural hematoma. (a) Comparison of real subdural hematoma volumes and reconstructed subdural hematoma volumes based on SAE with different radii; (b) Comparison of real subdural hematoma volumes and reconstructed subdural hematoma volumes based on ART with different radii.
Fig. 7.
Fig. 7. Reconstruction effect of SAE network under different SNRs. (a) 3D visualization images of a real cerebral subdural hematoma; (b) slice images of real cerebral subdural hematomas; (c) cerebral subdural hematoma slice images reconstructed with 50 dB SNR; (d) cerebral subdural hematoma slice images reconstructed with 40 dB SNR; (e) cerebral subdural hematoma slice images reconstructed with 30 dB SNR; (f) cerebral subdural hematoma slice images reconstructed with 20 dB SNR.
Fig. 8.
Fig. 8. Curve fitting figures of real volume and reconstructed volume of SAE. (a) SNR under 50 dB; (b) SNR under 40 dB; (c) SNR under 30 dB; (d) SNR under 20 dB.

Tables (5)

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Table 1. Optical parameters of human head

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Table 2. Optical parameters of a human head model

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Table 3. BCE analysis of the SAE algorithm and the ART algorithm for reconstruction of the subdural hematoma

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Table 4. VE analysis of real cerebral subdural hematoma and SAE-reconstructed cerebral subdural hematoma under different SNRs

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Table 5. BCE analysis of real cerebral subdural hematoma and SAE network reconstruction cerebral subdural hematoma under different SNRs

Equations (4)

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C x i = D x i V i V i , C y i = D y i V i V i , C z i = D z i V i V i .
C x j = D x j V j V j , C y j = D y j V j V j , C z j = D z j V j V j .
C t = ( C x i , C y i , C z i ) , C r  = ( C x j , C y j , C z j ) .
B C E = C t C r 2 .

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