A three-step reconstruction method for fluorescence molecular tomography based on compressive sensing
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Standard
A three-step reconstruction method for fluorescence molecular tomography based on compressive sensing. / Zhu, Yansong; Jha, Abhinav K.; Dreyer, Jakob K.; Le, Hanh N.D.; Kang, Jin U.; Roland, Per E.; Wong, Dean F.; Rahmim, Arman.
Optical Tomography and Spectroscopy of Tissue XII. Vol. 10059 SPIE - International Society for Optical Engineering, 2017. 1005911.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - GEN
T1 - A three-step reconstruction method for fluorescence molecular tomography based on compressive sensing
AU - Zhu, Yansong
AU - Jha, Abhinav K.
AU - Dreyer, Jakob K.
AU - Le, Hanh N.D.
AU - Kang, Jin U.
AU - Roland, Per E.
AU - Wong, Dean F.
AU - Rahmim, Arman
PY - 2017
Y1 - 2017
N2 - Fluorescence molecular tomography (FMT) is a promising tool for real time in vivo quantification of neurotransmission (NT) as we pursue in our BRAIN initiative effort. However, the acquired image data are noisy and the reconstruction problem is ill-posed. Further, while spatial sparsity of the NT effects could be exploited, traditional compressive-sensing methods cannot be directly applied as the system matrix in FMT is highly coherent. To overcome these issues, we propose and assess a three-step reconstruction method. First, truncated singular value decomposition is applied on the data to reduce matrix coherence. The resultant image data are input to a homotopy-based reconstruction strategy that exploits sparsity via ℓ1 regularization. The reconstructed image is then input to a maximum-likelihood expectation maximization (MLEM) algorithm that retains the sparseness of the input estimate and improves upon the quantitation by accurate Poisson noise modeling. The proposed reconstruction method was evaluated in a three-dimensional simulated setup with fluorescent sources in a cuboidal scattering medium with optical properties simulating human brain cortex (reduced scattering coefficient: 9.2 cm-1, absorption coefficient: 0.1 cm-1 and tomographic measurements made using pixelated detectors. In different experiments, fluorescent sources of varying size and intensity were simulated. The proposed reconstruction method provided accurate estimates of the fluorescent source intensity, with a 20% lower root mean square error on average compared to the pure-homotopy method for all considered source intensities and sizes. Further, compared with conventional ℓ2 regularized algorithm, overall, the proposed method reconstructed substantially more accurate fluorescence distribution. The proposed method shows considerable promise and will be tested using more realistic simulations and experimental setups.
AB - Fluorescence molecular tomography (FMT) is a promising tool for real time in vivo quantification of neurotransmission (NT) as we pursue in our BRAIN initiative effort. However, the acquired image data are noisy and the reconstruction problem is ill-posed. Further, while spatial sparsity of the NT effects could be exploited, traditional compressive-sensing methods cannot be directly applied as the system matrix in FMT is highly coherent. To overcome these issues, we propose and assess a three-step reconstruction method. First, truncated singular value decomposition is applied on the data to reduce matrix coherence. The resultant image data are input to a homotopy-based reconstruction strategy that exploits sparsity via ℓ1 regularization. The reconstructed image is then input to a maximum-likelihood expectation maximization (MLEM) algorithm that retains the sparseness of the input estimate and improves upon the quantitation by accurate Poisson noise modeling. The proposed reconstruction method was evaluated in a three-dimensional simulated setup with fluorescent sources in a cuboidal scattering medium with optical properties simulating human brain cortex (reduced scattering coefficient: 9.2 cm-1, absorption coefficient: 0.1 cm-1 and tomographic measurements made using pixelated detectors. In different experiments, fluorescent sources of varying size and intensity were simulated. The proposed reconstruction method provided accurate estimates of the fluorescent source intensity, with a 20% lower root mean square error on average compared to the pure-homotopy method for all considered source intensities and sizes. Further, compared with conventional ℓ2 regularized algorithm, overall, the proposed method reconstructed substantially more accurate fluorescence distribution. The proposed method shows considerable promise and will be tested using more realistic simulations and experimental setups.
KW - Compressive sensing
KW - FMT
KW - Noise modeling
KW - Reconstruction
U2 - 10.1117/12.2252664
DO - 10.1117/12.2252664
M3 - Article in proceedings
C2 - 28596634
AN - SCOPUS:85019184733
VL - 10059
BT - Optical Tomography and Spectroscopy of Tissue XII
PB - SPIE - International Society for Optical Engineering
T2 - Optical Tomography and Spectroscopy of Tissue XII
Y2 - 30 January 2017 through 1 February 2017
ER -
ID: 188452314