This research is primarily focused on laser-based non-invasive photoacoustic tomography of small animal brains. Photoacoustic tomography, a novel imaging modality, was applied to visualize the distribution of optical absorptions in small-animal brains through the skin and skull. This technique combines the high-contrast advantage of optical imaging with the high-resolution advantage of ultrasonic imaging. Based on the intrinsic optical contrast, this imaging system successfully visualized three-dimensional tissue structures in intact brains, including lesions and tumors in brain cerebral cortex. Physiological changes and functional activities in brains, including cerebral blood volume and blood oxygenation in addition to anatomical information, were also satisfactorily monitored. This technique successfully imaged the dynamic distributions of exogenous contrast agents in small-animal brains. Photoacoustic angiography in small-animal brains yielding high contrast and high spatial resolution was implemented noninvasively using intravenously injected absorbing dyes. In the appendix, the theory of Monte Carlo simulation of polarized light propagation in scattering media was briefly summarized.

This research is primarily focused on laser-based non-invasive photoacoustic tomography of small animal brains. Photoacoustic tomography, a novel imaging modality, was applied to visualize the distribution of optical absorptions in small-animal brains through the skin and skull. This technique combines the high-contrast advantage of optical imaging with the high-resolution advantage of ultrasonic imaging. Based on the intrinsic optical contrast, this imaging system successfully visualized three-dimensional tissue structures in intact brains, including lesions and tumors in brain cerebral cortex. Physiological changes and functional activities in brains, including cerebral blood volume and blood oxygenation in addition to anatomical information, were also satisfactorily monitored. This technique successfully imaged the dynamic distributions of exogenous contrast agents in small-animal brains. Photoacoustic angiography in small-animal brains yielding high contrast and high spatial resolution was implemented noninvasively using intravenously injected absorbing dyes. In the appendix, the theory of Monte Carlo simulation of polarized light propagation in scattering media was briefly summarized.

We consider the inverse source problem arising in thermo-and photoacoustic tomography. It consists in reconstructing the initial pressure from the boundary measurements of the acoustic wave. Our goal is to extend versatile time reversal techniques to the case when the boundary of the domain is perfectly reflecting, effectively turning the domain into a reverberant cavity. Standard time reversal works only if the solution of the direct problem decays in time, which does not happen in the setup we consider. We thus propose a novel time reversal technique with a nonstandard boundary condition. The error induced by this time reversal technique satisfies the wave equation with a dissipative boundary condition and, therefore, decays in time. For larger measurement times, this method yields a close approximation; for smaller times, the first approximation can be iteratively refined, resulting in a convergent Neumann series for the approximation.

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第22328号 / 医博第4569号 / 新制||医||1041(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 大森 孝一, 教授 椛島 健治, 教授 松田 秀一 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

To confirm the presence of prostate cancer which is the most incident visceral cancer in men, prostate biopsies are acquired using the magnetic resonance imaging fusion-guided prostate biopsy protocol. For this approach annotated magnetic resonance imaging is overlaid onto real-time ultrasound imaging to guide sampling of suspicious regions marked by uroradiologists. Additional biopsy samples are acquired via the previous clinical gold standard, i.e. the templated 12-core transrectal ultrasound-guided prostate biopsy protocol. While this approach improves the sensitivity of the prostate biopsy, a real-time, multiparametric imaging method of identifying biopsy targets could help overcome some of the inherent pitfalls of the magnetic resonance imaging fusion-guided prostate biopsy. Since ultrasound is used during the prostate biopsy, photoacoustic tomography, e.g. a hybrid imaging modality in which clinical ultrasound probes can be used to detect centimeters deep chemical alterations, has the potential to provide real-time targeting during biopsy. The translation of photoacoustic tomography to the clinic for prostate biopsy has been prevented by engineering challenges, which include identification of a biomarker for detecting suspicious regions of tissue and light delivery to the prostate for photoacoustic signal generation. Here, we present a vascular texture analysis method that identified 100% of primary and 67% of secondary tumors in the testing data set of ex vivo human prostate specimens. This method can be applied to future in vivo photoacoustic and ultrasound tomography of human prostates after further optimization of light delivery for photoacoustic tomography. To progress towards achieving this aim, we developed a transurethral light delivery device with angular light coupling method. By controlling the launch angle of the light into the fiber, the conversion of forward to side propagating energy can be improved from 27% to 98%, and the longitudinal emission profile can be controlled in order to illuminate the whole prostate simultaneously.<br>

Thermoacoustic tomography (TAT) is a newly emerging modality in biomedical imaging. It combines the good contrast of electromagnetic and good resolution of ultrasound imaging. The mathematical model of TAT is the observability problem for the wave equation: one observes the data on a hyper-surface and reconstructs the initial perturbation. In this dissertation, we consider several mathematical problems of TAT. The first problem is the inversion formulas. We provide a family of closed form inversion formulas to reconstruct the initial perturbation from the observed data. The second problem is the range description. We present the range description of the spherical mean Radon transform, which is an important transform in TAT. The next problem is the stability analysis for TAT. We prove that the reconstruction of the initial perturbation from observed data is not H¨older stable if some observability condition is violated. The last problem is the speed determination. The question is whether the observed data uniquely determines the ultrasound speed and initial perturbation. We provide some initial results on this issue. They include the unique determination of the unknown constant speed, a weak local uniqueness, a characterization of the non-uniqueness, and a characterization of the kernel of the linearized operator.

Photoacoustic tomography (PAT), as well as thermoacoustic tomography (TAT), utilize electromagnetic radiation in its visible, near infrared, microwave, and radiofrequency forms, respectively, to induce acoustic waves in biological tissues for imaging purposes. Combining the advantages of both the high image contrast that results from electromagnetic absorption and the high resolution of ultrasound imaging, these new imaging modalities could be the next successful imaging techniques in biomedical applications. Basic research on PAT and TAT, and the relevant physics, is presented in Chapter I. In Chapter II, we investigate the imaging mechanisms of TAT in terms of signal generation, propagation and detection. We present a theoretical analysis as well as simulations of such imaging characteristics as contrast and resolution, accompanied by experimental results from phantom and tissue samples. In Chapter III, we discuss the further application of TAT to the imaging of biological tissues. The microwave absorption difference in normal and cancerous breast tissues, as well as its influence on thermoacoustic wave generation and the resulting transducer response, is investigated over a wide range of electromagnetic frequencies and depths of tumor locations. In Chapter IV, we describe the mechanism of PAT and the algorithm used for image reconstruction. Because of the broad bandwidth of the laser-induced ultrasonic waves and the limited bandwidth of the single transducer, multiple ultrasonic transducers, each with a different central frequency, are employed for simultaneous detection. Chapter V further demonstrates PAT’s ability to image vascular structures in biological tissue based on blood’s strong light absorption capability. The photoacoustic images of rat brain tumors in this study clearly reveal the angiogenesis that is associated with tumors. In Chapter VI, we report on further developing PAT to image deeply embedded optical heterogeneity in biological tissues. The improved imaging ability is attributed to better penetration by NIR light, the use of the optical contrast agent ICG (indocyanine green) and a new detection scheme of a circular scanning configuration. Deep penetrating PAT, which is based on a tissue’s intrinsic contrast using laser light of 532 nm green light and 1.06 µm near infrared light, is also presented.

Near Infrared (NIR) and Photoacoustic (PA) Imaging are promising imaging modalities that provides functional information of the soft biological tissues in-vivo, with applica-tions in breast and brain tissue imaging. These techniques use near infrared light in the wavelength range of (600 nm - 900 nm), giving an advantage of being non-ionizing imaging modality. This makes the prolong bed-side monitoring of tissue feasible, making them highly desirable medical imaging modalities in the clinic. The computation models that are deployed in these imaging scenarios are computationally demanding and often require a high performance computing systems to deploy them in real-time. This the-sis presents three computationally e cient models for near-infrared and photoacoustic imaging, without compromising the quality of measured functional properties, to make them more appealing in clinical scenarios. The attenuation of near-infrared (NIR) light intensity as it propagates in a turbid medium like biological tissue is described by modi ed the BeerLambert law (MBLL). The MBLL is generally used to quantify the changes in tissue chromophore concen-trations for NIR spectroscopic data analysis. Even though MBLL is e ective in terms of providing qualitative comparison, it su ers from its applicability across tissue types and tissue dimensions. A Lambert-W function-based modeling for light propagation in biological tissues is proposed and introduced, which is a generalized version of the Beer-Lambert model. The proposed modeling provides parametrization of tissue properties, which includes two attenuation coe cients o and . The model is validated against the Monte Carlo simulation, which is the gold standard for modeling NIR light propagation in biological tissue. Numerous human and animal tissues are included to validate the proposed empirical model, including an inhomogeneous adult human head model. The proposed model, which has a closed form (analytical), is rst of its kind in providing accurate modeling of NIR light propagation in biological tissues. Model based image reconstruction techniques yield better quantitative accuracy in photoacoustic (PA) image reconstruction, especially in limited data cases. An exponen-tial ltering of singular values is proposed for carrying out the image reconstruction in photoacoustic tomography. The results were compared with widely popular Tikhonov regularization, time reversal, and the state of the art least-squares QR based reconstruc-tion algorithms for three digital phantom cases with varying signal-to-noise ratios of data. The exponential ltering provided superior photoacoustic images of better quanti-tative accuracy. Moreover, the proposed ltering approach was observed to be less biased towards regularization parameter and did not come with any additional computational burden as it was implemented within the Tikhonov ltering framework. It was also shown that the standard Tikhonov ltering becomes an approximation to the proposed exponential ltering. The model based image reconstruction techniques for photoacoustic tomography re-quire an explicit regularization. An error estimate minimization based approach was proposed and developed for the determination of regularization parameter for PA imag-ing. The regularization was used within Lanczos bidiagonalization framework, which provides the advantage of dimensionality reduction for a large system of equations. The proposed method was computationally faster than the state of the art techniques and provided similar performance in terms of quantitative accuracy in reconstructed im-ages.The estimate can also be utilized in determining suitable regularization parameter for other popular techniques such as Tikhonov,exponential ltering and `1 norm based regularization methods.

Diffuse optical tomography uses near infrared (NIR) light as the probing media to re-cover the distributions of tissue optical properties with an ability to provide functional information of the tissue under investigation. As NIR light propagation in the tissue is dominated by scattering, the image reconstruction problem (inverse problem) is non-linear and ill-posed, requiring usage of advanced computational methods to compensate this. Diffuse optical image reconstruction problem is always rank-deficient, where finding the independent measurements among the available measurements becomes challenging problem. Knowing these independent measurements will help in designing better data acquisition set-ups and lowering the costs associated with it. An optimal measurement selection strategy based on incoherence among rows (corresponding to measurements) of the sensitivity (or weight) matrix for the near infrared diffuse optical tomography is proposed. As incoherence among the measurements can be seen as providing maximum independent information into the estimation of optical properties, this provides high level of optimization required for knowing the independency of a particular measurement on its counterparts. The utility of the proposed scheme is demonstrated using simulated and experimental gelatin phantom data set comparing it with the state-of-the-art methods. The traditional image reconstruction methods employ ℓ2-norm in the regularization functional, resulting in smooth solutions, where the sharp image features are absent. The sparse recovery methods utilize the ℓp-norm with p being between 0 and 1 (0 ≤ p1), along with an approximation to utilize the ℓ0-norm, have been deployed for the reconstruction of diffuse optical images. These methods are shown to have better utility in terms of being more quantitative in reconstructing realistic diffuse optical images compared to traditional methods. Utilization of ℓp-norm based regularization makes the objective (cost) function non-convex and the algorithms that implement ℓp-norm minimization utilizes approximations to the original ℓp-norm function. Three methods for implementing the ℓp-norm were con-sidered, namely Iteratively Reweigthed ℓ1-minimization (IRL1), Iteratively Reweigthed Least-Squares (IRLS), and Iteratively Thresholding Method (ITM). These results in-dicated that IRL1 implementation of ℓp-minimization provides optimal performance in terms of shape recovery and quantitative accuracy of the reconstructed diffuse optical tomographic images. Photoacoustic tomography (PAT) is an emerging hybrid imaging modality combining optics with ultrasound imaging. PAT provides structural and functional imaging in diverse application areas, such as breast cancer and brain imaging. A model-based iterative reconstruction schemes are the most-popular for recovering the initial pressure in limited data case, wherein a large linear system of equations needs to be solved. Often, these iterative methods requires regularization parameter estimation, which tends to be a computationally expensive procedure, making the image reconstruction process to be performed off-line. To overcome this limitation, a computationally efficient approach that computes the optimal regularization parameter is developed for PAT. This approach is based on the least squares-QR (LSQR) decomposition, a well-known dimensionality reduction technique for a large system of equations. It is shown that the proposed framework is effective in terms of quantitative and qualitative reconstructions of initial pressure distribution.

Biomedical optical imaging is capable of providing functional information of the soft bi-ological tissues, whose applications include imaging large tissues, such breastand brain in-vivo. Biomedical optical imaging uses near infrared light (600nm-900nm) as the probing media, givin ganaddedadvantageofbeingnon-ionizingimagingmodality. The tomographic technologies for imaging large tissues encompasses diffuse optical tomogra-phyandphotoacoustictomography. Traditional image reconstruction methods indiffuse optical tomographyemploysa �2-norm based regularization, which is known to remove high frequency no is either econstructed images and make the mappearsmooth. Hence as parsity based image reconstruction has been deployed for diffuse optical tomography, these sparserecov-ery methods utilize the �p-norm based regularization in the estimation problem with 0≤ p<1. These sparse recovery methods, along with an approximation to utilizethe �0-norm, have been used forther econstruction of diffus eopticaltomographic images.The comparison of these methods was performed by increasing the sparsityinthesolu-tion. Further a model resolution matrix based framework was proposed and shown to in-duceblurinthe�2-norm based regularization framework for diffuse optical tomography. This model-resolution matrix framework was utilized in the optical imaged econvolution framework. A basis pursuitdeconvolution based on Split AugmentedLagrangianShrink-ageAlgorithm(SALSA)algorithm was used along with the Tikhonovregularization step making the image reconstruction into a two-step procedure. This new two-step approach was found to be robust with no iseandwasabletobetterdelineatethestructureswhichwasevaluatedusingnumericalandgelatinphantom experiments. Modern diffuse optical imaging systems are multi-modalin nature, where diffuse optical imaging is combined with traditional imaging modalitiessuc has Magnetic Res-onanceImaging(MRI),or Computed Tomography(CT). Image-guided diffuse optical tomography has the advantage of reducingthetota lnumber of optical parameters beingreconstructedtothenumber of distinct tissue types identified by the traditional imaging modality, converting the optical image-reconstruction problem fromunder-determined innaturetoover-determined. In such cases, the minimum required measurements might be farless compared to those of the traditional diffuse optical imaging. An approach to choose these measurements optimally based on a data-resolution matrix is proposed, and it is shown that it drastically reduces the minimum required measurements (typicalcaseof240to6) without compromising the image reconstruction performance. In the last part of the work , a model-based image reconstruction approaches in pho-toacoustic tomography (which combines light and ultra sound) arestudied as it is know that these methods have a distinct advantage compared to traditionalanalytical methods in limited datacase. These model-based methods deployTikhonovbasedregularizationschemetoreconstruct the initial pressure from the boundary acoustic data. Again a model-resolution for these cases tend to represent the blurinduced by the regularization scheme. A method that utilizes this blurringmodelandper forms the basis pursuit econ-volution to improve the quantitative accuracy of the reconstructed photoacoustic image is proposed and shown to be superior compared to other traditional methods. Moreover, this deconvolution including the building of model-resolution matrixis achievedvia the Lanczosbidiagonalization (least-squares QR) making this approach computationally ef-ficient and deployable inreal-time. Keywords Medical imaging, biomedical optical imaging, diffuse optical tomography, photoacous-tictomography, multi-modalimaging, inverse problems,sparse recovery,computational methods inbiomedical optical imaging.