Diffuse optical tomography for breast cancer imaging guided by computed tomography: A feasibility study

Abstract

Abstract. Diffuse optical tomography (DOT) has attracted attentions in the last two decades due to its intrinsic sensitivity in imaging chromophores of tissues such as hemoglobin, water, and lipid. However, DOT has not been clinically accepted yet due to its low spatial resolution caused by strong optical scattering in tissues. Structural guidance provided by an anatomical imaging modality enhances the DOT imaging substantially. Here, we propose a computed tomography (CT) guided multispectral DOT imaging system for breast cancer imaging. To validate its feasibility, we have built a prototype DOT imaging system which consists of a laser at the wavelength of 650 nm and an electron multiplying charge coupled device (EMCCD) camera. We have validated the CT guided DOT reconstruction algorithms with numerical simulations and phantom experiments, in which different imaging setup parameters, such as projection number of measurements and width of measurement patch, have been investigated. Our results indicate that an air-cooling EMCCD camera is good enough for the transmission mode DOT imaging. We have also found that measurements at six angular projections are sufficient for DOT to reconstruct the optical targets with 2 and 4 times absorption contrast when the CT guidance is applied. Finally, we have described our future research plan on integration of a multispectral DOT imaging system into a breast CT scanner.

Kernel-based anatomically-aided diffuse optical tomography reconstruction

“…The numerical phantom was discretized into a 3D tetrahedral finite element mesh with 9,877 nodes, 54,913 elements, and 2,921 surface nodes. Numerical DOT measurement data at six angular projections were generated by the DOT forward model in continuous wave mode [57]. In each angular projection, six source positions separated 5 mm apart were placed on one side of the phantom in the vertical line as indicated by the black dots in Fig.…”

“…Details of the prototype DOT imaging system were described in Ref. 57. During the experiments, the EMCCD camera stayed stationary while the rotary stage rotated the phantom with an angular step of 60 degrees.…”

Back-propagation neural network-based reconstruction algorithm for diffuse optical tomography

“…Except for Eq. (16), one of the typical equations which can be used to determine the number of neurons in the hidden layer is log 2 ðnÞ, 48 where n is the number of input neurons. In our experiments, n is 240.…”

“…Although both linear and nonlinear reconstruction algorithms for DOT are available, 14 considerable efforts have been made to develop various reconstruction algorithms to improve quantitative accuracy and image quality. [14][15][16][17][18][19][20][21][22] To date, the illposedness of the inverse problem in DOT can be alleviated by employing a regularization technique, which utilizes a data fitting term together with a regularizer (L 2 or L 1 norm, etc.) to suppress the effect of measurement noise and modeling errors.…”

Weighting function effects in a direct regularization method for image-guided near-infrared spectral tomography of breast cancer

“…However, NIRST alone suffers from low spatial resolution due to the strongly scattering nature of NIR light and leading to diffuse propagation in tissue [6]. To achieve high spatial resolution, the prior structural information provided by anatomical images such as X-ray/CT [7,8], ultrasound [9,10] or MRI [11][12][13][14], have been incorporated into NIRST reconstruction algorithms. The most common methods to combine anatomical images into NIRST reconstruction, are hard [15] or soft [16] priors based algorithms.…”