title: Hierarchical Bayesian estimation improves depth accuracy and spatial resolution of diffuse optical tomography.
abstract: High-density diffuse optical tomography (HD-DOT) is an emerging technique for visualizing the internal state of biological tissues. The large number of overlapping measurement channels due to the use of high-density probe arrays permits the reconstruction of the internal optical properties, even with a reflectance-only measurement. However, accurate three-dimensional reconstruction is still a challenging problem. First, the exponentially decaying sensitivity causes a systematic depth-localization error. Second, the nature of diffusive light makes the image blurred. In this paper, we propose a three-dimensional reconstruction method that overcomes these two problems by introducing sensitivity-normalized regularization and sparsity into the hierarchical Bayesian method. Phantom experiments were performed to validate the proposed method under three conditions of probe interval: 26 mm, 18.4 mm, and 13 mm. We found that two absorbers with distances shorter than the probe interval could be discriminated under the high-density conditions of 18.4-mm and 13-mm intervals. This discrimination ability was possible even if the depths of the two absorbers were different from each other. These results show the high spatial resolution of the proposed method in both depth and horizontal directions.
authors.affiliation: ATR Neural Information Analysis Laboratories, Kyoto 619-0288, Japan. shimokawa@atr.jp
authors.forename: Takeaki
authors.initials: T
authors.lastname: Shimokawa
authors.forename: Takashi
authors.initials: T
authors.lastname: Kosaka
authors.forename: Okito
authors.initials: O
authors.lastname: Yamashita
authors.forename: Nobuo
authors.initials: N
authors.lastname: Hiroe
authors.forename: Takashi
authors.initials: T
authors.lastname: Amita
authors.forename: Yoshihiro
authors.initials: Y
authors.lastname: Inoue
authors.forename: Masa-aki
authors.initials: MA
authors.lastname: Sato
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D009391:Nephelometry and Turbidimetry / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D041622:Tomography, Optical / Q000379:methods*