To fully realize the potential of photoacoustic tomography (PAT) in preclinical and clinical applications, rapid measurements and robust reconstructions are needed. Sparse-view measurements have been adopted effectively to accelerate the data acquisition. However, since the reconstruction from the sparse-view sampling data is challenging, both of the effective measurement and the appropriate reconstruction should be taken into account. In this study, we present an iterative sparse-view PAT reconstruction scheme where a virtual parallel-projection concept matching for the proposed measurement condition is introduced to help to achieve the “compressive sensing” procedure of the reconstruction, and meanwhile the spatially adaptive filtering fully considering the a priori information of the mutually similar blocks existing in natural images is introduced to effectively recover the partial unknown coefficients in the transformed domain. Therefore, the sparse-view PAT images can be reconstructed with higher quality compared with the results obtained by the universal back-projection (UBP) algorithm in the same sparse-view cases. The proposed approach has been validated by simulation experiments, which exhibits desirable performances in image fidelity even from a small number of measuring positions.
In fluorescence molecular tomography (FMT), the reconstruction results can greatly benefit from a priori information of accurate tissue optical-structures, which is difficult to be obtained in vivo with the traditional diffuse optical tomography (DOT) alone. One of the solutions is to apply a priori anatomical-structures obtained with anatomical imaging systems such as X-ray computed tomography (XCT) to constrain the reconstruction process of DOT. However, since the X-ray imaging mechanism limits the contrast between soft-tissues, it is difficult to segment the abdominal organs from XCT images. In order to overcome the challenges, the anatomical-structures of a target mouse are approximately obtained through registering a standard mouse anatomical atlas, i.e., the Digimouse, to its XCT volume with non-rigid image registration, and the optical-structures of the target mouse is approximately estimated through anatomical-structures guided time-resolve DOT strategy. Results of numerical simulations reveals that the an effective target atlas can be obtained through the registration method, and the results show that the absorption and reduced scattering coefficients of each organs can be recovered with reasonable accuracies.
Shape-parameterized diffuse optical tomography (DOT), which is based on a priori that assumes the uniform distribution
of the optical properties in the each region, shows the effectiveness of complex biological tissue optical heterogeneities
reconstruction. The priori tissue optical structure could be acquired with the assistance of anatomical imaging methods
such as X-ray computed tomography (XCT) which suffers from low-contrast for soft tissues including different optical
characteristic regions. For the mouse model, a feasible strategy of a priori tissue optical structure acquisition is proposed
based on a non-rigid image registration algorithm. During registration, a mapping matrix is calculated to elastically align
the XCT image of reference mouse to the XCT image of target mouse. Applying the matrix to the reference atlas which
is a detailed mesh of organs/tissues in reference mouse, registered atlas can be obtained as the anatomical structure of
target mouse. By assigning the literature published optical parameters of each organ to the corresponding anatomical
structure, optical structure of the target organism can be obtained as a priori information for DOT reconstruction
algorithm. By applying the non-rigid image registration algorithm to a target mouse which is transformed from the
reference mouse, the results show that the minimum correlation coefficient can be improved from 0.2781 (before
registration) to 0.9032 (after fine registration), and the maximum average Euclid distances can be decreased from
12.80mm (before registration) to 1.02mm (after fine registration), which has verified the effectiveness of the algorithm.
Diffuse optical tomography (DOT) is a biomedical imaging technology for noninvasive visualization of spatial variation
about the optical properties of tissue, which can be applied to in vivo small-animal disease model. However, traditional
DOT suffers low spatial resolution due to tissue scattering. To overcome this intrinsic shortcoming, multi-modal
approaches that incorporate DOT with other imaging techniques have been intensively investigated, where a priori
information provided by the other modalities is normally used to reasonably regularize the inverse problem of DOT.
Nevertheless, these approaches usually consider the anatomical structure, which is different from the optical structure.
Photoacoustic tomography (PAT) is an emerging imaging modality that is particularly useful for visualizing lightabsorbing
structures embedded in soft tissue with higher spatial resolution compared with pure optical imaging. Thus, we
present a PAT-guided DOT approach, to obtain the location a priori information of optical structure provided by PAT
first, and then guide DOT to reconstruct the optical parameters quantitatively. The results of reconstruction of phantom
experiments demonstrate that both quantification and spatial resolution of DOT could be highly improved by the
regularization of feasible-region information provided by PAT.
The modulation transfer function (MTF) is widely used to describe the spatial resolution of x-ray imaging systems.
Extensive works have been conducted to achieve accurate and precise measurement of MTF by using a slanted edge test
device. The noise level of the slanted edge image is an important factor influencing the accuracy of MTF measurement.
Thus in this work, a comparison study was made on the MTF measurement results obtained by using different curve
fitting algorithms for ESF determination when analyzing the same image data with different noise levels. The results
indicated that the averaged MTF measurement errors got increased with the decrease of the signal-to-noise ratio of the
slanted edge images for all of the ESF processing algorithms. But for the same noisy slanted edge image, monotonic
fitting algorithm outperformed Gaussian smoothing method or moving polynominal fitting method on MTF
The full time-resolved methods of diffuse fluorescence tomography (DFT) are known to improve image resolution and accuracy significantly. However, these methods usually suffer from low practical efficacy due to the influence of the instrumental response function (IRF) and the tradeoff between the used data time-resolution and the required signal-to-noise ratio (SNR). We herein present a full time-resolved approach that combines an IRF-calibrated time-resolved Born normalization and an overlap-delaying time-gating strategy for attaining high SNR without sacrificing the time-resolved information content. Phantom experiments demonstrate that the approach outperforms the traditional DFT methods in spatial resolution and reconstruction fidelity.