Detector-trigger-based cardiac multiphase micro-CT imaging for small animals.

BACKGROUND: Micro-computed tomography is important in cardiac imaging for preclinical small animal models, but motion artifacts may appear due to the rapid heart rates. To avoid influence of motion artifacts, the prospective ECG gating schemes based on an X-ray source trigger have been investigated. However, due to the lack of pulsed X-ray exposure modes, high-resolution micro-focus X-ray sources do not support source triggering in most cases. OBJECTIVE: To develop a fast-cardiac multiphase acquisition strategy using prospective ECG gating for micro-focus X-ray tubes with a continuous emission mode. METHODS: The proposed detector-trigger-based prospective ECG gating acquisition scheme (DTB-PG) triggers the X-ray detector at the R peak of ECG, and then collects multiple phase projections of the heart in one ECG cycle by sequence acquisition. Cardiac multiphase images are reconstructed after performing the same acquisition in all views. The feasibility of this strategy was verified in multiphase imaging experiments of a phantom with 150 ms motion period and a mouse heart on a micro-focus micro-CT system with continuous emission mode. RESULTS: Using a high frame-rate CMOS detector, DTB-PG discriminates the positions of the motion phantom well in 10 different phases and enables to distinguish the changes in the cardiac volume of the mouse in different phases. The acquisition rate of DTB-PG is much faster than other prospective gating schemes as demonstrated by theoretical analysis. CONCLUSIONS: DTB-PG combines the advantages of prospective ECG gating strategies and X-ray detector-trigger mode to suppress motion artifacts, achieve ultra-fast acquisition rates, and relax hardware limitations.

Compact fiber-free parallel-plane multi-wavelength diffuse optical tomography system for breast imaging.

To facilitate the clinical applicability of the diffuse optical inspection device, a compact multi-wavelength diffuse optical tomography system for breast imaging (compact-DOTB) with a fiber-free parallel-plane structure was designed and fabricated for acquiring three-dimensional optical properties of the breast in continuous-wave mode. The source array consists of 56 surface-mounted micro light-emitting diodes (LEDs), each integrating three wavelengths (660, 750, and 840 nm). The detector array is arranged with 56 miniaturized surface-mounted optical sensors, each encapsulating a high-sensitivity photodiode (PD) and a low-noise current amplifier with a gain of 24×. The system provides 3,136 pairs of source-detector measurements at each wavelength, and the fiber-free design largely ensures consistency between source/detection channels while effectively reducing the complexity of system operation and maintenance. We have evaluated the compact-DOTB system's characteristics and demonstrated its performance in terms of reconstruction positioning accuracy and recovery contrast with breast-sized phantom experiments. Furthermore, the breast cancer patient studies have been carried out, and the quantitative results indicate that the compact-DOTB system is able to observe the changes in the functional tissue components of the breast after receiving the neoadjuvant chemotherapy (NAC), demonstrating the great potential of the proposed compact system for clinical applications, while its cost and ease of operation are competitive with the existing breast-DOT devices.

Highly Erbium-Doped Nanoplatform with Enhanced Red Emission for Dual-Modal Optical-Imaging-Guided Photodynamic Therapy.

Generally, luminescence quenching at high doping concentrations typically limits the concentration of doped ions in the lanthanide material to less than 0.05-20 mol %, and this is still a major hindrance in designing nanoplatforms with improved brightness. In this research, a nanoplatform capable of dual-modal imaging and synergetic antitumor cells therapy was designed. NaYF4: x%Er@NaXF4 ( x = 5, 25, 50, and 100; X = Lu and Y) core@shell nanoparticles with Er3+ ion concentration up to 100 mol % were synthesized, and the luminescence properties under near-infrared (NIR) excitation were detected. The results show the strong coupled of surface and concentration quenching effects in upconversion nanoparticles (UCNP). Upconversion luminescence (UCL) and NIR-II emission intensity increased with negligible concentration quenching effect under 980 and 800 nm NIR lasers because of the growth of epitaxial shells. Therefore, the enhanced red luminescence transfers energy to photosensitizer ZnPc as the photodynamic therapy (PDT) agent for tumor inhibition efficacy.

Gamma rays excited radioluminescence tomographic imaging.

BACKGROUND: Radionuclide-excited luminescence imaging is an optical radionuclide imaging strategy to reveal the distributions of radioluminescent nanophosphors (RLNPs) inside small animals, which uses radioluminescence emitted from RLNPs when excited by high energy rays such as gamma rays generated during the decay of radiotracers used in clinical nuclear medicine imaging. Currently, there is no report of tomographic imaging based on radioluminescence. METHODS: In this paper, we proposed a gamma rays excited radioluminescence tomography (GRLT) to reveal three-dimensional distributions of RLNPs inside a small animal using radioluminescence through image reconstruction from surface measurements of radioluminescent photons using an inverse algorithm. The diffusion equation was employed to model propagations of radioluminescent photons in biological tissues with highly scattering and low absorption characteristics. RESULTS: Phantom and artificial source-implanted mouse model experiments were employed to test the feasibility of GRLT, and the results demonstrated that the ability of GRLT to reveal the distribution of RLNPs such as Gd2O2S:Tb using the radioluminescent signals when excited by gamma rays produced from 99mTc. CONCLUSIONS: With the emerging of targeted RLNPs, GRLT can provide new possibilities for in vivo and noninvasive examination of biological processes at cellular levels. Especially, combining with Cerenkov luminescence imaging, GRLT can achieve dual molecular information of RLNPs and nuclides using single optical imaging technology.

Reconstruction of fluorescence molecular tomography with a cosinoidal level set method.

BACKGROUND: Implicit shape-based reconstruction method in fluorescence molecular tomography (FMT) is capable of achieving higher image clarity than image-based reconstruction method. However, the implicit shape method suffers from a low convergence speed and performs unstably due to the utilization of gradient-based optimization methods. Moreover, the implicit shape method requires priori information about the number of targets. METHODS: A shape-based reconstruction scheme of FMT with a cosinoidal level set method is proposed in this paper. The Heaviside function in the classical implicit shape method is replaced with a cosine function, and then the reconstruction can be accomplished with the Levenberg-Marquardt method rather than gradient-based methods. As a result, the priori information about the number of targets is not required anymore and the choice of step length is avoided. RESULTS: Numerical simulations and phantom experiments were carried out to validate the proposed method. Results of the proposed method show higher contrast to noise ratios and Pearson correlations than the implicit shape method and image-based reconstruction method. Moreover, the number of iterations required in the proposed method is much less than the implicit shape method. CONCLUSIONS: The proposed method performs more stably, provides a faster convergence speed than the implicit shape method, and achieves higher image clarity than the image-based reconstruction method.

In Vivo Dual-Modality Fluorescence and Magnetic Resonance Imaging-Guided Lymph Node Mapping with Good Biocompatibility Manganese Oxide Nanoparticles.

Multifunctional manganese oxide nanoparticles (NPs) with impressive enhanced T1 contrast ability show great promise in biomedical diagnosis. Herein, we developed a dual-modality imaging agent system based on polyethylene glycol (PEG)-coated manganese oxide NPs conjugated with organic dye (Cy7.5), which functions as a fluorescence imaging (FI) agent as well as a magnetic resonance imaging (MRI) imaging agent. The formed Mn3O4@PEG-Cy7.5 NPs with the size of ~10 nm exhibit good colloidal stability in different physiological media. Serial FI and MRI studies that non-invasively assessed the bio-distribution pattern and the feasibility for in vivo dual-modality imaging-guided lymph node mapping have been investigated. In addition, histological and biochemical analyses exhibited low toxicity even at a dose of 20 mg/kg in vivo. Since Mn3O4@PEG-Cy7.5 NPs exhibited desirable properties as imaging agents and good biocompatibility, this work offers a robust, safe, and accurate diagnostic platform based on manganese oxide NPs for tumor metastasis diagnosis.

A fast method based on NESTA to accurately reconstruct CT image from highly undersampled projection measurements.

BACKGROUND: The CT image reconstruction algorithm based compressed sensing (CS) can be formulated as an optimization problem that minimizes the total-variation (TV) term constrained by the data fidelity and image nonnegativity. There are a lot of solutions to this problem, but the computational efficiency and reconstructed image quality of these methods still need to be improved. OBJECTIVE: To investigate a faster and more accurate mathematical algorithm to settle TV term minimization problem of CT image reconstruction. METHOD: A Nesterov's algorithm (NESTA) is a fast and accurate algorithm for solving TV minimization problem, which can be ascribed to the use of most notably Nesterov's smoothing technique and a subtle averaging of sequences of iterates, which has been shown to improve the convergence properties of standard gradient-descent algorithms. In order to demonstrate the superior performance of NESTA on computational efficiency and image quality, a comparison with Simultaneous Algebraic Reconstruction Technique-TV (SART-TV) and Split-Bregman (SpBr) algorithm is made using a digital phantom study and two physical phantom studies from highly undersampled projection measurements. RESULTS: With only 25% of conventional full-scan dose and, NESTA method reduces the average CT number error from 51.76HU to 9.98HU on Shepp-Logan phantom and reduces the average CT number error from 50.13HU to 0.32HU on Catphan 600 phantom. On an anthropomorphic head phantom, the average CT number error is reduced from 84.21HU to 1.01HU in the central uniform area. CONCLUSIONS: To the best of our knowledge this is the first work that apply the NESTA method into CT reconstruction based CS. Research shows that this method is of great potential, further studies and optimization are necessary.

Enhanced model iteration algorithm with graph neural network for diffuse optical tomography.

Diffuse optical tomography (DOT) employs near-infrared light to reveal the optical parameters of biological tissues. Due to the strong scattering of photons in tissues and the limited surface measurements, DOT reconstruction is severely ill-posed. The Levenberg-Marquardt (LM) is a popular iteration method for DOT, however, it is computationally expensive and its reconstruction accuracy needs improvement. In this study, we propose a neural model based iteration algorithm which combines the graph neural network with Levenberg-Marquardt (GNNLM), which utilizes a graph data structure to represent the finite element mesh. In order to verify the performance of the graph neural network, two GNN variants, namely graph convolutional neural network (GCN) and graph attention neural network (GAT) were employed in the experiments. The results showed that GCNLM performs best in the simulation experiments within the training data distribution. However, GATLM exhibits superior performance in the simulation experiments outside the training data distribution and real experiments with breast-like phantoms. It demonstrated that the GATLM trained with simulation data can generalize well to situations outside the training data distribution without transfer training. This offers the possibility to provide more accurate absorption coefficient distributions in clinical practice.

Helical optical projection tomography.

A new technique termed Helical Optical Projection Tomography (hOPT) has been developed with the aim to overcome some of the limitations of current 3D optical imaging techniques. hOPT is based on Optical Projection Tomography (OPT) with the major difference that there is a translation of the sample in the vertical direction during the image acquisition process, requiring a new approach to image reconstruction. Contrary to OPT, hOPT makes possible to obtain 3D-optical images of intact long samples without imposing limits on the sample length. This has been tested using hOPT to image long murine tissue samples such as spinal cords and large intestines. Moreover, 3D-reconstructed images of the colon of DSS-treated mice, a model for Inflammatory Bowel Disease, allowed the identification of the structural alterations. Finally, the geometry of the hOPT device facilitates the addition of a Selective Plane Illumination Microscopy (SPIM) arm, providing the possibility of delivering high resolution images of selected areas together with complete volumetric information.

Indocyanine green-based fluorescence imaging improved by deep learning.

Intraoperative identification of malignancies using indocyanine green (ICG)-based fluorescence imaging could provide real-time guidance for surgeons. Existing ICG-based fluorescence imaging mostly operates in the near-infrared (NIR)-I (700-1000 nm) or the NIR-IIa' windows (1000-1300 nm), which is not optimal in terms of spatial resolution and contrast as their light scattering is higher than the NIR-IIb window (1500-1700 nm). It is highly desired to achieve ICG-based fluorescence imaging in the NIR-IIb window, but it is hindered by its ultra-low NIR-IIb emission tail of ICG. Herein, we employ a generative adversarial network to generate NIR-IIb ICG images directly from the acquired NIR-I ICG images. This approach was investigated by in vivo imaging of sub-surface vascular, intestine structure, and tumors, and their results demonstrated significant improvement in spatial resolution and contrast for ICG-based fluorescence imaging. It is potential for deep learning to improve ICG-based fluorescence imaging in clinical diagnostics and image-guided surgery in clinics.

3D directional gradient L0 norm minimization guided limited-view reconstruction in a dual-panel positron emission mammography.

BACKGROUND: Dual-panel PET is often used for local organ imaging, especially breast imaging, due to its simple structure, high sensitivity, good in-plane resolution, and straightforward fusion with other imaging modalities. Nevertheless, because of data loss caused by the dual-panel structure, using conventional image reconstruction methods results in limited-view artifacts and low image quality in dual-panel positron emission mammography (PEM), which may seriously affect the diagnosis. To mitigate the limited-view artifacts in the dual-panel PEM, we propose a 3D directional gradient L0 norm minimization (3D-DL0) guided reconstruction method. METHODS: The detailed derivation and reasonable simplification of the 3D-DL0 algorithm are given first. Using this algorithm, we then obtain a prior image with edge recovery but contrast loss. To limit the solution space, the 3D-DL0 prior is introduced into the Maximum a Posteriori reconstruction. Meanwhile, a space-invariant point spread function is also implemented to restore image contrast and boundaries. Finally, the reconstructed images with limited-view artifact suppression are obtained. The proposed method was evaluated using the data acquired from physical phantoms and patients with breast tumors on a commercial dual-panel PET system. RESULTS: The qualitative and quantitative studies for phantom data and the blind reader study for clinical data show that the proposed method is more effective in reaching a balance between artifact elimination and image contrast improvement compared with various limited-view reconstruction methods. In addition, the iteration process of the method is proved convergent numerically. CONCLUSIONS: The image quality improvement confirms the potential value of the proposed reconstruction algorithm to address the limited-view problem, and thus improve diagnostic accuracy in dual-panel PEM imaging.

Label-free structural and functional volumetric imaging by dual-modality optical-Raman projection tomography.

Mesoscale volumetric imaging is of great importance for the study of bio-organisms. Among others, optical projection tomography provides unprecedented structural details of specimens, but it requires fluorescence label for chemical targeting. Raman spectroscopic imaging is able to identify chemical components in a label-free manner but lacks microstructure. Here, we present a dual-modality optical-Raman projection tomography (ORPT) technology, which enables label-free three-dimensional imaging of microstructures and components of millimeter-sized samples with a micron-level spatial resolution on the same device. We validate the feasibility of our ORPT system using images of polystyrene beads in a volume, followed by detecting biomolecules of zebrafish and Arabidopsis, demonstrating that fused three-dimensional images of the microstructure and molecular components of bio-samples could be achieved. Last, we observe the fat body of Drosophila melanogaster at different developmental stages. Our proposed technology enables bimodal label-free volumetric imaging of the structure and function of biomolecules in a large sample.

Total variation regularization for bioluminescence tomography with the split Bregman method.

Regularization methods have been broadly applied to bioluminescence tomography (BLT) to obtain stable solutions, including l2 and l1 regularizations. However, l2 regularization can oversmooth reconstructed images and l1 regularization may sparsify the source distribution, which degrades image quality. In this paper, the use of total variation (TV) regularization in BLT is investigated. Since a nonnegativity constraint can lead to improved image quality, the nonnegative constraint should be considered in BLT. However, TV regularization with a nonnegativity constraint is extremely difficult to solve due to its nondifferentiability and nonlinearity. The aim of this work is to validate the split Bregman method to minimize the TV regularization problem with a nonnegativity constraint for BLT. The performance of split Bregman-resolved TV (SBRTV) based BLT reconstruction algorithm was verified with numerical and in vivo experiments. Experimental results demonstrate that the SBRTV regularization can provide better regularization quality over l2 and l1 regularizations.

Mapping of bioluminescent images onto CT volume surface for dual-modality BLT and CT imaging.

We present a method for mapping the two-dimensional (2D) bioluminescent images (BLIs) onto a three-dimensional (3D) body surface derived from the computed tomography (CT) volume data. This mapping includes two closely-related steps, the spatial registration of the 2D BLIs into the coordinate system of the CT volume data and the light flux recovering on the body surface from BLIs. By labeling markers on the body surface, we proposed an effective registration method to achieve the spatial position alignment. The subsequent light flux recovering is presented based on the inverse process of the free-space light transport model and taking the influence of the camera lens diaphragm into account. Incorporating the mapping procedure into the bioluminescence tomography (BLT) reconstruction, we developed a dual-modality BLT and CT imaging framework to provide both optical and anatomical information. The accuracy of the registration and the light flux recovering methods were evaluated via physical phantom experiments. The registration method was found to have a mean error of 0.41 mm and 0.35 mm in horizontal and vertical direction, and the accuracy of the light flux recovering method was below 5%. Furthermore, we evaluated the performance of the dual-modality BLT/CT imaging framework using a mouse phantom. Preliminary results revealed the potential and feasibility of the dual-modality imaging framework.

Quantitative chemical sensing of drugs in scattering media with Bessel beam Raman spectroscopy.

Scattering can seriously affect the highly sensitive detection and quantitative analysis of chemical substances in scattering media and becomes a significant challenge for in vivo application of Raman spectroscopy. In this study, we demonstrated a proof of concept for using the self-reconstructing Bessel beam for Raman spectroscopic sensing of the chemicals in the handmade scattering media and biological tissue slices. The homebuilt Bessel beam Raman spectroscopy (BRS) was capable of accurately detecting the Raman spectra of the chemicals buried in the scattering media, and had a superiority in quantitative analysis. The feasibility of the developed technique was verified by detecting the Raman spectra of pure samples in air. Compared with the spectra acquired by the Gaussian beam Raman spectroscope, the performance of the BRS system in terms of Raman spectrum detection and Raman peak recognition was confirmed. Subsequently, by employing the technique for the detection of acetaminophen buried in the scattering media, the application of the new technology in detecting and quantitating the chemicals in the scattering media were underlined, offering greater detection depth and better linear quantification capability than the conventional Gaussian beam Raman spectroscopy. Finally, we explored the potential of the BRS system for chemical sensing of acetaminophen in biological tissue slices, indicating a significant development towards the evaluation of drug in vivo.

Two-stage deep learning network-based few-view image reconstruction for parallel-beam projection tomography.

Background: Projection tomography (PT) is a very important and valuable method for fast volumetric imaging with isotropic spatial resolution. Sparse-view or limited-angle reconstruction-based PT can greatly reduce data acquisition time, lower radiation doses, and simplify sample fixation modes. However, few techniques can currently achieve image reconstruction based on few-view projection data, which is especially important for in vivo PT in living organisms. Methods: A 2-stage deep learning network (TSDLN)-based framework was proposed for parallel-beam PT reconstructions using few-view projections. The framework is composed of a reconstruction network (R-net) and a correction network (C-net). The R-net is a generative adversarial network (GAN) used to complete image information with direct back-projection (BP) of a sparse signal, bringing the reconstructed image close to reconstruction results obtained from fully projected data. The C-net is a U-net array that denoises the compensation result to obtain a high-quality reconstructed image. Results: The accuracy and feasibility of the proposed TSDLN-based framework in few-view projection-based reconstruction were first evaluated with simulations, using images from the DeepLesion public dataset. The framework exhibited better reconstruction performance than traditional analytic reconstruction algorithms and iterative algorithms, especially in cases using sparse-view projection images. For example, with as few as two projections, the TSDLN-based framework reconstructed high-quality images very close to the original image, with structural similarities greater than 0.8. By using previously acquired optical PT (OPT) data in the TSDLN-based framework trained on computed tomography (CT) data, we further exemplified the migration capabilities of the TSDLN-based framework. The results showed that when the number of projections was reduced to 5, the contours and distribution information of the samples in question could still be seen in the reconstructed images. Conclusions: The simulations and experimental results showed that the TSDLN-based framework has strong reconstruction abilities using few-view projection images, and has great potential in the application of in vivo PT.

Confocal Laser Scanning Microscopy Based on a Silicon Photomultiplier for Multicolor In Vivo Imaging in Near-Infrared Regions I and II.

Confocal laser scanning microscopy (CLSM) is expected to exhibit a better imaging performance in the second near-infrared (NIR-II) windows with weak tissue scattering and autofluorescence. However, the indium gallium arsenide (InGaAs) detectors currently used for imaging in the NIR-II region are prohibitively expensive, hampering its extensive biomedical applications. In this study, a novel NIR-II CLSM system is developed by using the inexpensive silicon photomultiplier (SiPM) that can perform the multicolor biological imaging in vivo. Using IR-780 iodide as the contrast agent, the NIR-II imaging capability of constructed CLSM is inspected, demonstrating a spatial resolution of 1.68 µm (close to the diffraction limit) and a fluorophore detection sensitivity as low as 100 nm. In particular, it is discovered that the multicolor imaging performance in both NIR-I and NIR-II windows is comparable to those from multialkali and InGaAs photomultiplier tubes. In addition, 3D NIR-II CLSM is also conducted for in vivo imaging of the vascular structure in mouse ear and subcutaneous tumors. To the best of authors' knowledge, this is the first time that a low-cost detector based on a SiPM has been used for microscopic imaging of trailing fluorescence signals in the NIR-II region of an NIR fluorescent probe.

DH-Mammo PET: a dual-head positron emission mammography system for breast imaging.

Objective. To develop a simultaneous positron emission tomography-Optical (OPET) breast imaging dual-head PET subsystem, called DH-Mammo PET, for accurate, early diagnosis and efficacy assessment of breast cancer with high resolution and sensitivity.Approach. We developed a breast-dedicated PET based on LYSO crystal, silicon photomultiplier array and multi-voltage threshold sampling technique. It consists of two detector heads, each with a detection area of 216 mm × 145.5 mm. The distance between the detector heads is fixed at 120 mm. In order to extract coincidences and correct data, GPU-based software coincidence processing, random, scatter, normalization, gap-filling and attenuation corrections were applied in turn. The images were reconstructed using maximum likelihood expectation maximization with depth of interaction (DOI) modeling. The performance of DH-Mammo PET was evaluated referring to NEMA NU 4-2008, NU 2-2007 and Chinese industry recommended standard YY/T 1835-2022. Besides, several clinical patient images of DH-Mammo PET were compared with those of a whole-body PET/CT.Main results. The energy resolution was 14.5%, and time resolution was < 1.31 ns. Indicated by the22Na point source imaging, its spatial resolution was 2.60 mm (5.40 mm), 1.00 mm (1.04 mm), and 0.96 mm (0.93 mm) in theX,YandZdirections, respectively, using the system response matrix with (without) DOI modeling. Indicated by the Derenzo phantom imaging, the spatial resolution was ∼3.0 mm, <1.2 mm, and <1.2 mm in theX,YandZdirections. The system sensitivity was 6.87%, 4.89% and 3.37% with an energy window of 100-800, 250-750 and 350-650 keV, respectively. The scatter fraction was 26.43%, and the peak NECR was 162.6 kcps at 24.1 MBq for the modified rat-like phantom. As for the recovery coefficients, they ranged from 0.15 to 1.04 for rods between 1 and 5 mm obtained with a NEMA image quality phantom. The spill-over ratio for the air-filled and water-filled chamber was 0.05 and 0.11, respectively. DH-Mammo PET can provide more image details in clinical experiments and fulfil a fast scan with 60-120 s acquisition time.Significance. Good spatial resolution and high sensitivity of DH-Mammo PET would enable fast and accurate PET imaging of the breast. Besides, combining the DH-Mammo PET with the diffuse optical tomography would make full use of tumor metabolic imaging and tissue endogenous optical imaging, which would improve the accuracy of early clinical diagnosis of small lesions of breast cancers.

Dual-modality monitoring of tumor response to cyclophosphamide therapy in mice with bioluminescence imaging and small-animal positron emission tomography.

The purpose of this study was to noninvasively monitor the therapeutic efficacy of cyclophosphamide (CTX) in a mouse model by dual-modality molecular imaging: positron emission tomography (PET) and bioluminescence imaging (BLI). Firefly luciferase (fLuc) transfected HCC-LM3-fLuc human hepatocellular carcinoma cells were injected subcutaneously into BALB/c nude mice to establish the experimental tumor model. Two groups of HCC-LM3-fLuc tumor-bearing mice (n  =  7 per group) were treated with saline or CTX (100 mg/kg on days 0, 2, 5, and 7). BLI and (18)F-fluorodeoxyglucose ((18)F-FDG) PET scans were done to evaluate the treatment efficacy. CTX induced a 25.25 ± 13.13% and 35.91 ± 25.85% tumor growth inhibition rate on days 9 and 12 posttreatment, respectively, as determined by BLI. A good linear correlation was found between the tumor sizes measured by caliper and the BLI signals determined by optical imaging (R(2)  =  .9216). (18)F-FDG imaging revealed a significant uptake reduction in the tumors of the CTX-treated group compared to that in the saline control group (5.30 ± 1.97 vs 3.00 ± 2.11% ID/g) on day 16 after CTX treatment. Dual-modality molecular imaging using BLI and small-animal PET can play important roles in the process of chemotherapy and will provide noninvasive and reliable monitoring of the therapeutic response.

An adaptive regularization parameter choice strategy for multispectral bioluminescence tomography.

PURPOSE: Bioluminescence tomography (BLT) provides an effective tool for monitoring physiological and pathological activities in vivo. However, the measured data in bioluminescence imaging are corrupted by noise. Therefore, regularization methods are commonly used to find a regularized solution. Nevertheless, for the quality of the reconstructed bioluminescent source obtained by regularization methods, the choice of the regularization parameters is crucial. To date, the selection of regularization parameters remains challenging. With regards to the above problems, the authors proposed a BLT reconstruction algorithm with an adaptive parameter choice rule. METHODS: The proposed reconstruction algorithm uses a diffusion equation for modeling the bioluminescent photon transport. The diffusion equation is solved with a finite element method. Computed tomography (CT) images provide anatomical information regarding the geometry of the small animal and its internal organs. To reduce the ill-posedness of BLT, spectral information and the optimal permissible source region are employed. Then, the relationship between the unknown source distribution and multiview and multispectral boundary measurements is established based on the finite element method and the optimal permissible source region. Since the measured data are noisy, the BLT reconstruction is formulated as l(2) data fidelity and a general regularization term. When choosing the regularization parameters for BLT, an efficient model function approach is proposed, which does not require knowledge of the noise level. This approach only requests the computation of the residual and regularized solution norm. With this knowledge, we construct the model function to approximate the objective function, and the regularization parameter is updated iteratively. RESULTS: First, the micro-CT based mouse phantom was used for simulation verification. Simulation experiments were used to illustrate why multispectral data were used rather than monochromatic data. Furthermore, the study conducted using an adaptive regularization parameter demonstrated our ability to accurately localize the bioluminescent source. With the adaptively estimated regularization parameter, the reconstructed center position of the source was (20.37, 31.05, 12.95) mm, and the distance to the real source was 0.63 mm. The results of the dual-source experiments further showed that our algorithm could localize the bioluminescent sources accurately. The authors then presented experimental evidence that the proposed algorithm exhibited its calculated efficiency over the heuristic method. The effectiveness of the new algorithm was also confirmed by comparing it with the L-curve method. Furthermore, various initial speculations regarding the regularization parameter were used to illustrate the convergence of our algorithm. Finally, in vivo mouse experiment further illustrates the effectiveness of the proposed algorithm. CONCLUSIONS: Utilizing numerical, physical phantom and in vivo examples, we demonstrated that the bioluminescent sources could be reconstructed accurately with automatic regularization parameters. The proposed algorithm exhibited superior performance than both the heuristic regularization parameter choice method and L-curve method based on the computational speed and localization error.