The spec of the JSVD processor

The circuit is implemented to hardware description language in Verilog.

Although the main goal of the processor is not the speed, the speed of the processor can reach 200MHz. The total cell area is 248180 with the UMC 90nm manufacture library. The combinational circuit area is 102804 cell and the non-combinational area is 145376 cell. The result is synthesized by the ncverilog of Synopsys. The fix-point JSVD can decompose the 16-by-16 matrix and offer 14bits precision CORDIC engine. It also offers the restriction of iteration times. To deal with an iteration of 4x16 matrix only take 160μ . s

Conclusion

In this paper, a portable CW-DOT system of prototype is proposed. The system was used to verify the reconstruction algorithm of forward model and inverse solution.

In order to reduce the complexity of computation and enhance the quality of the CW-DOT images, we apply Truncated and Jacobi SVD algorithm to do the inverse solution in CW-DOT systems.

The different reconstruction modes are also provided: sub-frame mode and frame mode. Sub-frame mode has been proven can reduce the computational overhead in reconstruction processing. We simulate inhomogeneous media with different shapes and locations and study the impact of different reconstruction modes on the quality of image. This simulation demonstrates that low computational cost is possible without harming severely the image quality. In short, Truncated and Jacobi SVD is a highly efficient technique for reconstruction of good quality images and is suitable for CW-DOT system.

Moreover, the high precision 16-bits image reconstruction processor is also proposed. The design of the processor is aim to low-area, low-power consumption and reasonable precision. In conclusion, the study reduces the volume of CW-DOT systems by implementing the low computational overhead algorithm on VLSI. The algorithm is also tested by simulation and emulation with the prototype.

In the future, the image reconstruction processor can be combined with other biomedical signal processors to be a SoC, and operate with the wireless handheld devises. In this way, he devises can benefit more doctors, more patients, and more researchers.

Reference

[1] W.-C. Kuo, "非侵入式生醫斷層影像簡介," 物理雙月刊, vol. 28, pp.

698-703, 2006.

[2] Aaron G. Filler, "The History, Development and Impact of Computed Imaging in Neurological Diagnosis and Neurosurgery: CT, MRI, and DTI," 2009.

[3] J. Ollinger and J. Fessler, "Positron-emission tomography," IEEE Signal Processing Magazine, vol. 14, pp. 43-55, 1997.

[4] A. Fercher, W. Drexler, C. Hitzenberger, and T. Lasser, "Optical coherence tomography-principles and applications," Reports on progress in physics, vol.

66, pp. 239-303, 2003.

[5] S. Arridge and M. Schweiger, "Image reconstruction in optical tomography,"

Philosophical Transactions: Biological Sciences, vol. 352, pp. 717-726, 1997.

[6] M. Schweiger, "Computational aspects of diffuse optical tomography," in Computing in Science & Engineering. vol. 5, A. Gibson, Ed., 2003, pp. 33-41.

[7] V. Kondepati, H. Heise, and J. Backhaus, "Recent applications of near-infrared spectroscopy in cancer diagnosis and therapy," Analytical and Bioanalytical Chemistry, vol. 390, pp. 125-139, 2008.

[8] T. M. Benson, "Modified simultaneous iterative reconstruction technique for faster parallel computation," in Nuclear Science Symposium Conference Record, 2005 IEEE. vol. 5, J. Gregor, Ed., 2005, pp. 2715-2718.

[9] M. Jiang and G. Wang, "Convergence of the simultaneous algebraic reconstruction technique (SART)," IEEE Transactions on Image Processing, vol. 12, pp. 957-961, 2003.

[10] R. J. Gaudette, D. H. Brooks, C. A. DiMarzio, M. E. Kilmer, E. L. Miller, T.

Gaudette, and D. A. Boas, "A comparison study of linear reconstruction techniques for diffuse optical tomographic imaging of absorption coefficient,"

Physics in Medicine and Biology, vol. 45, pp. 1051-1051, 2000.

[11] C. Sau-Gee and C. Chin-Chi, "A new efficient algorithm for singular value decomposition," in Circuits and Systems, 1999. ISCAS '99. Proceedings of the 1999 IEEE International Symposium on, 1999, pp. 523-526 vol.5.

[12] G. Strangman, D. A. Boas, and J. P. Sutton, "Non-invasive neuroimaging using near-infrared light," Biological Psychiatry, vol. 52, pp. 679-693, 10/1 2002.

[13] A. Bozkurt, A. Rosen, H. Rosen, and B. Onaral, "A portable near infrared spectroscopy system for bedside monitoring of newborn brain," BioMedical Engineering OnLine, vol. 4, p. 29, 2005.

[14] S. C. Bunce, M. Izzetoglu, K. Izzetoglu, B. Onaral, and K. Pourrezaei,

"Functional near-infrared spectroscopy," Engineering in Medicine and Biology Magazine, IEEE, vol. 25, pp. 54-62, 2006.

[15] R. X. Xu and S. P. Povoski, "Diffuse optical imaging and spectroscopy for cancer," Expert Review of Medical Devices, vol. 4, pp. 83-95, 2007.

[16] W. F. Cheong, "A review of the optical properties of biological tissues," in Quantum Electronics, IEEE Journal of. vol. 26, S. A. Prahl, Ed., 1990, pp.

2166-2185.

[17] C. Schmitz, D. Klemer, R. Hardin, M. Katz, Y. Pei, H. Graber, M. Levin, R.

Levina, N. Franco, and W. Solomon, "Design and implementation of dynamic near-infrared optical tomographic imaging instrumentation for simultaneous dual-breast measurements," Applied Optics, vol. 44, pp. 2140-2153, 2005.

[18] D. A. Boas, D. H. Brooks, E. L. Miller, C. A. DiMarzio, M. Kilmer, R. J.

Gaudette, and Z. Quan, "Imaging the body with diffuse optical tomography,"

Signal Processing Magazine, IEEE, vol. 18, pp. 57-75, 2001.

[19] D. Boas, T. Gaudette, and S. Arridge, "Simultaneous imaging and optode calibration with diffuse optical tomography," Optics Express, vol. 8, pp.

263-270, 2001.

[20] A. Ribes and F. Schmitt, "Linear inverse problems in imaging," in Signal Processing Magazine, IEEE. vol. 25, 2008, pp. 84-99.

[21] U. Schwiegelshohn and L. Thiele, "A systolic algorithm for cyclic-by-rows SVD," in Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP '87., 1987, pp. 768-770.

[22] R. Andraka, "A survey of CORDIC algorithms for FPGA based computers,"

1998, pp. 191-200.

[23] M. Rahmati, M. S. Sadri, and M. A. Naeini, "FPGA based singular value decomposition for image processing applications," in Application-Specific Systems, Architectures and Processors, 2008. ASAP 2008. International Conference on, 2008, pp. 185-190.

[24] W. Ma, M. E. Kaye, D. M. Luke, and R. Doraiswami, "An FPGA-Based Singular Value Decomposition Processor," in Electrical and Computer Engineering, Canadian Conference on, 2006, pp. 1047-1050.

[25] J. Cavallaro and F. Luk, "CORDIC Arithmetic for an SVD Processor," Journal of parallel and distributed computing, vol. 5, pp. 271-290, 1988.