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.

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.

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.

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.

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.

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.

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.

Hybrid model of photon propagation based on the analytical and Monte Carlo methods for a dual-head PET system.

The construction of photon propagation has a close relationship with the quality of reconstructed images. The classical Monte Carlo (MC) based method can model the photon propagation precisely, but it is time-consuming. The analytical method can often quickly construct a model, but its precision is a problem. How to fully exploit the advantages of the MC simulation and analytical model is an open problem. Inspired by the characteristics of the depth of interaction (DOI) detectors, which can help confirm the deposited position of a photon with DOI-encoding technology, we virtually discretize each crystal into several subcrystals to obtain the statistical distribution by MC-based simulation. Then, the statistical distribution is combined with a spatially variant solid-angle model. This combination strategy provides a hybrid model to describe photon propagation with relatively high accuracy and low computational cost. Three discretization schemes are compared to optimize the constructed photon propagation model. Several experiments are carried out to evaluate the performance of the proposed hybrid method. The metrics of full width at half maximum (FWHM), contrast recovery (CR), and coefficient of variation (COV) are adopted to quantitate the imaging results. The classical MC-based method is compared as a gold-standard reference. When a crystal is divided into two discretized positions, the convergent tendencies of CRs and COVs are consistent with that based on MC simulation method, respectively. In terms of FWHMs, the resolutions of using the MC-based model and the proposed hybrid model are 0.71 mm and 0.68 mm in the direction parallel to the detector head, respectively. This indicates the potential of the proposed method in positron emission tomography imaging.

Nonlinear iterative perturbation scheme with simplified spherical harmonics (SP3 ) light propagation model for quantitative photoacoustic tomography.

When using quantitative photoacoustic tomography (q-PAT) reconstruction to recover the optical absorption coefficients of tissue, the commonly used diffusion equation has several limitations in the case of the objects that have small geometries and high-absorption or low-scattering areas. Furthermore, the conventional perturbation reconstruction strategy is unsatisfactory when the target tissue containing large heterogeneous features. We herein present a modified q-PAT implementation that employs the higher-order photon migration model achieving the tradeoff between mathematical rigidity and computational efficiency. Besides, a nonlinear iterative method is proposed to obtain the perturbations of optical absorption considering the updating of the sensitivity matrix in calculating the fluence perturbations. Consequently, the distribution of tissue optical properties can be recovered in a robust way even if the targets with high absorption are included. The proposed approach has been validated by simulation, phantom and in vivo experiments, exhibiting promising performances in image fidelity and quantitative feasibility for practical applications.

A Data Self-Calibration Method Based on High-Density Parallel Plate Diffuse Optical Tomography for Breast Cancer Imaging.

When performing the diffuse optical tomography (DOT) of the breast, the mismatch between the forward model and the experimental conditions will significantly hinder the reconstruction accuracy. Therefore, the reference measurement is commonly used to calibrate the measured data before the reconstruction. However, it is complicated to customize corresponding reference phantoms based on the breast shape and background optical parameters of different subjects in clinical trials. Furthermore, although high-density (HD) DOT configuration has been proven to improve imaging quality, a large number of source-detector (SD) pairs also increase the difficulty of multi-channel correction. To enhance the applicability of the breast DOT, a data self-calibration method based on an HD parallel-plate DOT system is proposed in this paper to replace the conventional relative measurement on a reference phantom. The reference predicted data can be constructed directly from the measurement data with the support of the HD-DOT system, which has nearly a hundred sets of measurements at each SD distance. The proposed scheme has been validated by Monte Carlo (MC) simulation, breast-size phantom experiments, and clinical trials, exhibiting the feasibility in ensuring the quality of the DOT reconstruction while effectively reducing the complexity associated with relative measurements on reference phantoms.

Accelerated Stimulated Raman Projection Tomography by Sparse Reconstruction From Sparse-View Data.

OBJECTIVE: Stimulated Raman projection tomography (SRPT), a recently developed label-free volumetric chemical imaging technology, has been reported to quantitatively reconstruct the distribution of chemicals in a three-dimensional (3D) complex system. The current image reconstruction scheme used in SRPT is based on a filtered back projection (FBP) algorithm that requires at least 180 angular-dependent projections to rebuild a reasonable SRPT image, resulting in a long total acquisition time. This is a big limitation for longitudinal studies on live systems. METHODS: We present a sparse-view data-based sparse reconstruction scheme, in which sparsely sampled projections at 180 degrees were used to reconstruct the volumetric information. In the scheme, the simultaneous algebra reconstruction technique (SART), combined with total variation regularization, was used for iterative reconstruction. To better describe the projection process, a pixel vertex driven model (PVDM) was developed to act as projectors, whose performance was compared with those of the distance driven model (DDM). RESULTS: We evaluated our scheme with numerical simulations and validated it for SRPT by mapping lipid contents in adipose cells. Simulation results showed that the PVDM performed better than the DDM in the case of using sparse-view data. Our scheme could maintain the quality of the reconstructed images even when the projection number was reduced to 15. The cell-based experimental results demonstrated that the proposed scheme can improve the imaging speed of the current FBP-based SRPT scheme by a factor of 9-12 without sacrificing discernible imaging details. CONCLUSION: Our proposed scheme significantly reduces the total acquisition time required for SRPT at a speed of one order of magnitude faster than the currently used scheme. This significant improvement in imaging speed would potentially promote the applicability of SRPT for imaging living organisms.

Removal of random-valued impulse noise from Cerenkov luminescence images.

Cerenkov luminescence imaging(CLI) is an emerging molecular imaging technology able to optically visualize radioactive decay signals from medical isotopes and has found wide application in tumor diagnose, cancer therapy, drug development, intraoperative guidance, and so on. When Cerenkov luminescence data are collected, the high-energy particles from the radioactive nucleus will be detected by the sensitive CCD camera and lead to impulse noise. To suppress the impulse noise and improve the contrast of the useful signal to the background, the detection-based fuzzy switching median filtering framework is proposed in this paper. Several experiments were conducted respectively to investigate the statistical feature of the noise and to evaluate the performance of the proposed noise removal framework. The results show that the signal-to-noise ratio is improved after noise elimination. The proposed filtering framework outperforms the classical median filter in terms of root mean squared error and the structural similarity index. It also preserves the maximum value and the mean value in the regions of interest better than the median filter does. In addition, compared with the FLICMCDD algorithm, the proposed method works much faster while getting similar results. Graphical abstract.

Comparison of GPU reconstruction based on different symmetries for dual-head PET.

PURPOSE: Dual-head positron emission tomography (PET) scanners have increasingly attracted the attention of many researchers. However, with the compact geometry, the depth-of-interaction blurring will reduce the image resolution considerably. Monte Carlo (MC)-based system response matrix (SRM) is able to describe the physical process of PET imaging accurately and improve reconstruction quality significantly. The MC-based SRM is large and precomputed, which leads to a longer image reconstruction time with indexing and retrieving precomputed system matrix elements. In this study, we proposed a GPU acceleration algorithm to accelerate the iterative reconstruction. METHODS: It has been demonstrated that the line-of-response (LOR)-based symmetry and the Graphics Processing Unit (GPU) technology can accelerate the reconstruction tremendously. LOR-based symmetry is suitable for the forward projection calculation, but not for the backprojection. In this study, we proposed a GPU acceleration algorithm that combined the LOR-based symmetry and voxel-based symmetry together, in which the LOR-based symmetry is responsible for the forward projection, and the voxel-based symmetry is used for the backprojection. RESULTS: Simulation and real experiments verify the efficiency of the algorithm. Compared with the CPU-based calculation, the acceleration ratios of the forward projection and the backprojection operation are 130 and 110, respectively. The total acceleration ratio is 113×. In order to compare the acceleration effect of the different symmetries, we realized the reconstruction with the voxel-based symmetry and the LOR-based symmetry strategies. Compared with the LOR-based GPU reconstruction, the acceleration ratio is 3.5×. Compared with the voxel-based GPU reconstruction, the acceleration ratio is 12×. CONCLUSION: We have proposed a new acceleration algorithm for the dual-head PET system, in which both the forward and backprojection operations are accelerated by GPU.

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.

Image Restoration for Fluorescence Planar Imaging with Diffusion Model.

Fluorescence planar imaging (FPI) is failure to capture high resolution images of deep fluorochromes due to photon diffusion. This paper presents an image restoration method to deal with this kind of blurring. The scheme of this method is conceived based on a reconstruction method in fluorescence molecular tomography (FMT) with diffusion model. A new unknown parameter is defined through introducing the first mean value theorem for definite integrals. System matrix converting this unknown parameter to the blurry image is constructed with the elements of depth conversion matrices related to a chosen plane named focal plane. Results of phantom and mouse experiments show that the proposed method is capable of reducing the blurring of FPI image caused by photon diffusion when the depth of focal plane is chosen within a proper interval around the true depth of fluorochrome. This method will be helpful to the estimation of the size of deep fluorochrome.

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.