Sentinel lymph node (SLN) in vivo detection is vital in breast cancer surgery. A new near-infrared
fluorescence-based surgical navigation system (SNS) imaging software, which has been developed by
our research group, is presented for SLN detection surgery in this paper. The software is based on the
fluorescence-based surgical navigation hardware system (SNHS) which has been developed in our lab,
and is designed specifically for intraoperative imaging and postoperative data analysis. The surgical
navigation imaging software consists of the following software modules, which mainly include the
control module, the image grabbing module, the real-time display module, the data saving module and
the image processing module. And some algorithms have been designed to achieve the performance of
the software, for example, the image registration algorithm based on correlation matching. Some of the
key features of the software include: setting the control parameters of the SNS; acquiring, display and
storing the intraoperative imaging data in real-time automatically; analysis and processing of the saved
image data. The developed software has been used to successfully detect the SLNs in 21 cases of breast
cancer patients. In the near future, we plan to improve the software performance and it will be
extensively used for clinical purpose.
In mathematics, optical molecular imaging including bioluminescence tomography (BLT), fluorescence tomography
(FMT) and Cerenkov luminescence tomography (CLT) are concerned with a similar inverse source problem. They all
involve the reconstruction of the 3D location of a single/multiple internal luminescent/fluorescent sources based on 3D
surface flux distribution. To achieve that, an accurate fusion between 2D luminescent/fluorescent images and 3D
structural images that may be acquired form micro-CT, MRI or beam scanning is extremely critical. However, the
absence of a universal method that can effectively convert 2D optical information into 3D makes the accurate fusion
challengeable. In this study, to improve the fusion accuracy, a new fusion method for dual-modality tomography
(luminescence/fluorescence and micro-CT) based on natural light surface reconstruction (NLSR) and iterated closest
point (ICP) was presented. It consisted of Octree structure, exact visual hull from marching cubes and ICP. Different
from conventional limited projection methods, it is 360° free-space registration, and utilizes more
luminescence/fluorescence distribution information from unlimited multi-orientation 2D optical images. A mouse
mimicking phantom (one XPM-2 Phantom Light Source, XENOGEN Corporation) and an in-vivo BALB/C mouse with
implanted one luminescent light source were used to evaluate the performance of the new fusion method. Compared with
conventional fusion methods, the average error of preset markers was improved by 0.3 and 0.2 pixels from the new
method, respectively. After running the same 3D internal light source reconstruction algorithm of the BALB/C mouse,
the distance error between the actual and reconstructed internal source was decreased by 0.19 mm.
As a high-sensitivity imaging modality, bioluminescence tomography can reconstruct the three-dimensional (3-D) location of an internal luminescent source based on the 3-D surface light distribution. However, we can only get the multi-orientation two-dimensional (2-D) bioluminescence distribution in the experiments. Therefore, developing an accurate universal registration method is essential for following bioluminescent source reconstruction. We can then map the multi-orientation 2-D bioluminescence distribution to the 3-D surface derived from anatomical information with it. We propose a 2-D -to-3-D registration method based on iterated optimal projection and applied it in a registration and reconstruction study of three transgenic mice. Compared with traditional registration methods based on the fixed points, our method was independent of the markers and the registration accuracy of the three experiments was improved by 0.3, 0.5, and 0.4 pixels, respectively. In addition, based on the above two registration results using the two registration methods, we reconstructed the 3-D location of the inner bioluminescent source in the three transgenic mice. The reconstruction results showed that the average error distance between the center of the reconstructed element and the center of the real element were reduced by 0.32, 0.48, and 0.39 mm, respectively.
Photoacoustic imaging is a noninvasive biomedical imaging modality for visualizing the internal structure and function
of soft tissues. In this paper we present a combined iterative reconstruction algorithm to recover the distribution of
optical absorption coefficients. Such a combination of the simultaneous algebraic reconstruction technique (SART)
algorithm and the filtered back projection (FBP) algorithm bears the promise to improve the image quality of fast but not
accurate. The different between the present proposal and traditional ART algorithm is that we computer a full error
image with FBP, which is used to projection differences between original and reconstruction image, and to update the
solution in each iteration step. The combined iterative reconstruction algorithm works well in rectification of the
measurement and converges quickly onto an accurate estimate of the distribution of absolute absorption coefficients.
Numerical simulations demonstrate that the proposed algorithm effectively reduces the artifacts and blurs, and yields
better quality of reconstruction image than that with FBP algorithm in terms of both accuracy and robustness to noise.
structural and functional imaging. Image reconstruction of PAI requires the solution of an inverse source
problem, where the source represents the optical energy absorption distribution in the object. PAI in
spherical or circular geometry gives good image resolution yet is slow in signal acquisition and image
formation. Reducing the number of detection angles can ameliorate such issues. Besides, it is almost
impossible to cover the entire surface of tissue. This will restrict it in the medical application with
incomplete projection data. To resolve such limiting factors, in this thesis, a preconditioned conjugate
gradient method is applied to the normal equations (PCCGNR method) for reconstructing the absorption
distribution. Under the common assumption, a zero-mean Gaussian noise is added to the projection signals
and a computer simulated has been used for the evaluation. This algorithm works well in rectification of the
measurement and converges quickly onto an accurate estimate of the distribution of absolute absorption. It
not only runs much faster than the FBP algorithm, but also shows stronger robustness in that it provides
better image quality with detection data. We observed that diagonal preconditioners offer some
improvement in convergence rate for image reconstruction, and reconstructed image preconditioning
with ω = 0 (diagonal scaling) is closer to the true image than with ω> 0 . In addition, a physical experiment
that will be done with our experiment equipment system further demonstrates the potential of the proposed
algorithm in practical applications.
Solution with adjustable sparsity to tomographic imaging of Cerenkov photons is presented in this work. The
sparsity of radionuclides' distribution in tissues is an objective but unknown fact, and the inverse model of
qualitative data is an ill-posed problem. Based on the optimization technique, the uniqueness of numerical
solution to the ill-conditioned compact operator can be guaranteed by use of sparse regularization with the
approximate message-passing (AMP) method. After absorbing formulations with the AMP, we analyzed the
behavior of the hard thresholding operator. Iteratively numerical solutions were used to approximate the real
light source by assuming the number of non-zero solution in manual mode. This modified AMP algorithm was
performed in numerical simulation and physical experiments with 2-[18F]fluoro-2-deoxy-D-glucose. Experimental
results indicated that the proposed method was a kind of low-complexity iterative thresholding algorithms for
reconstructing 3D sparse distribution from a small set of optical measurements.
Bioluminescence tomography (BLT) is a novel optical molecular imaging (MI) modality. It can reconstruct
the inner bioluminescent light source distribution, according to the surface light distribution. The trust region
method (TRM) can overcome the ill-posedness of BLT for its regularization property. As there exists a "TRUST"
function that can solve the trust region subproblem in Matlab and Matlab's powerful matrix operation ability
suited for TRM, the TRM is implemented in Matlab. Then the Matlab code of TRM is transformed into a
dynamic linked library (DDL) and mixed together with the C++ code of the adaptive finite element (AFE)
framework, using the mixed-language programming technology (MLPT). There are two main advantages of the
MLPT. The first is taking advantages of all the participated programming languages. The second is time efficient.
The usual way of transferring data between programmes written in different programming languages is to write
the data first into files that are stored in the hard discs in one programme, and then read the files from another
programme. Besides wasting time on writing and reading, it is difficult to keep the precision of the data. The
DLL based MLPT can eliminate the need of installing code compilers in the platform running the software.
Furthermore, in DLL, the code is implemented in C/C++ with high time efficiency, while the code in Matlab
remains relatively low time efficiency. Finally, a numerical experiment is carried out to show MLPT's usage in
the source reconstruction procedure of BLT, using the MLPT based on DLL.
Fluorescence molecular tomography (FMT) has become a promising imaging modality for in vivo small animal
molecular imaging, and has many successful applications. This is partly due to the wealth of the fluorescent probes. By
labeling the regions of interest with fluorescent probes, FMT can achieve non-invasive investigation of the biological
process by localizing the targeted probes based on certain inverse mathematical models. However, FMT is usually an illposed
problem, and some form of regularization should be included to stabilize the problem, which can be considered as
the a priori information of the fluorescent probe bio-distribution. When FMT is used for the early detection of tumors,
an important characteristic is the sparsity of the fluorescent sources. This is because tumors are usually very small and
sparse at this stage. Considering this, general sparsity-promoting Lp-norm regularization is utilized in this paper. The
iterated shrinkage based reconstruction method is adopted to solve the general Lp regularization problem. However, the
original iterated shrinkage method is proved to have a linear convergence rate, and a large number of iterations are
needed to obtain satisfactory results. In this paper, an improved iterated shrinkage based FMT reconstruction algorithm
is proposed. By using the solutions from two previous iterations to determine the current solution, the convergence rate
can be greatly increased. Heterogeneous simulation experiment shows that the proposed method can obtain comparable
results with greatly reduced number of iterations compared with the original iterated shrinkage based method, which
makes it a practical reconstruction algorithm.
As a newly emerged optical imaging method, fluorescence molecular imaging technique has been receiving increasing
attention for its ability of non-invasive visualization of the cellular and molecular activities. However, as a kind of
background noise, autofluorescence is a major disturbing factor in fluorescence molecular imaging. In this paper, we
proposed a novel method to eliminate autofluorescence of small animals. The method is based on the fact that most
autofluorescent signal has a broad excitation and emission spectrum, whereas specific fluorescent probe has a narrow
one. First, two fluorescent images are obtained at two different excitation wavelengths. Then we divide the two obtained
fluorescent images into blocks with the size of 8×8 pixel. The two blocks from the same position of the two different
images respectively constitute a block pair. The ratio of one block's summation of total pixel value to that of ther other
block belonging to the same block pair is calculated. After that, we classify all block pairs into fluorescent and nonfluorescent
ones by ratio. The former are considered to be actual fluorescent regions. In next step, we adopt an adaptive
cluster analysis method to classify all fluorescent block pairs into multiple interest regions. A general centroid algorithm
is then applied to locate the center of each interest regions. We recover the fluorescent interest regions using flood filling
algorithm. Finally, we choose a GFP-transfected tumor mouse model and a GFP-transplanted mouse skin model to
validate our algorithm.
KEYWORDS: Bioluminescence, Bone, Finite element methods, Tomography, Tissues, Chemiluminescence, Molecular imaging, Reconstruction algorithms, 3D image processing, Chemical elements
Among many optical molecular imaging modalities, bioluminescence imaging (BLI) has more and
more wide application in tumor detection and evaluation of pharmacodynamics, toxicity,
pharmacokinetics because of its noninvasive molecular and cellular level detection ability, high
sensitivity and low cost in comparison with other imaging technologies. However, BLI can not present
the accurate location and intensity of the inner bioluminescence sources such as in the bone, liver or
lung etc. Bioluminescent tomography (BLT) shows its advantage in determining the bioluminescence
source distribution inside a small animal or phantom. Considering the deficiency of two-dimensional
imaging modality, we developed three-dimensional tomography to reconstruct the information of the
bioluminescence source distribution in transgenic mOC-Luc mice bone with the boundary measured
data. In this paper, to study the osteocalcin (OC) accumulation in transgenic mOC-Luc mice bone, a
BLT reconstruction method based on multilevel adaptive finite element (FEM) algorithm was used for
localizing and quantifying multi bioluminescence sources. Optical and anatomical information of the
tissues are incorporated as a priori knowledge in this method, which can reduce the ill-posedness of
BLT. The data was acquired by the dual modality BLT and Micro CT prototype system that was
developed by us. Through temperature control and absolute intensity calibration, a relative accurate
intensity can be calculated. The location of the OC accumulation was reconstructed, which was
coherent with the principle of bone differentiation. This result also was testified by ex vivo experiment
in the black 96-plate well using the BLI system and the chemiluminescence apparatus.
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