In vivo pentamodal tomographic imaging for small animals

Liu, Muhan; Guo, Hongbo; Liu, Hongbo; Zhang, Ze-Yu; Chi, Chongwei; Dong, Di; Hu, Zhenhua; Hui, Hui

doi:10.1364/boe.8.001356

Cited by 34 publications

(14 citation statements)

References 38 publications

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“…Previous studies have proven that radiopharmaceuticals emitting β + , β − and even α particles are capable to perform CLI, which significantly expands the type of probes for molecular imaging . By combining the advantages of both nuclear imaging and optical imaging, CLI owns the merits of high sensitivity, great imaging resolution and promising potential for clinical translation . So far, the outstanding performance of CLI has been demonstrated in the areas of tumor diagnosis, photodynamic therapy, treatment evaluation, and so on .…”

Section: Introductionmentioning

confidence: 99%

A novel in vivo Cerenkov luminescence image‐guided surgery on primary and metastatic colorectal cancer

Zhang

Cao

et al. 2019

Journal of Biophotonics

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Intraoperative Cerenkov luminescence imaging (CLI) can effectively improve the performance of tumor surgery. Nevertheless, the existing approaches are still unsatisfying to the clinical demands of open surgery. This study develops a novel intraoperative in vivo CLI approach to investigate the potential and value of Cerenkov luminescence (CL) image‐guided surgery. A system characterized with high sensitivity (19.61 kBq mL−1 18F‐FDG) and desirable spatial resolution (88.34 μm) is developed. CL image‐guided surgery is performed on colorectal cancer (CRC) models of mice and swine. Tumor surgery is guided by the static CL images, and the resection quality is evaluated quantitatively and contrasted with other imaging modalities exemplified by bioluminescence imaging (BLI). The in vivo results demonstrated the effectiveness of the proposed intraoperative CLI approach for removing primary and metastatic CRC. Safety of performing in vivo CL image‐guided surgery is verified as well through radiation measurements of related staffs. Overall, the developed intraoperative in vivo CLI approach can efficiently improve the cancer treatment.

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Section: Introductionmentioning

confidence: 99%

A novel in vivo Cerenkov luminescence image‐guided surgery on primary and metastatic colorectal cancer

Zhang

Cao

et al. 2019

Journal of Biophotonics

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“…30,34 More recently, tri-modality 35 and even pentamodality, tomographic imaging systems were also investigated. 36 One of major challenges in the multimodality FMT system is how to utilize anatomical information properly and easily in the FMT reconstruction. Soft prior method is a widely accepted approach, which allows variations within the regions.…”

Section: Introductionmentioning

confidence: 99%

Anatomical image-guided fluorescence molecular tomography reconstruction using kernel method

et al. 2017

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Abstract. Fluorescence molecular tomography (FMT) is an important in vivo imaging modality to visualize physiological and pathological processes in small animals. However, FMT reconstruction is ill-posed and illconditioned due to strong optical scattering in deep tissues, which results in poor spatial resolution. It is well known that FMT image quality can be improved substantially by applying the structural guidance in the FMT reconstruction. In this paper, a new approach to introducing anatomical information into the FMT reconstruction is presented using the kernel method. In contrast to conventional methods that incorporate anatomical information with a Laplacian-type regularization matrix, the proposed method introduces the anatomical guidance into the projection model of FMT. The primary advantage of the proposed method is that it does not require segmentation of targets in the anatomical images. Numerical simulations and phantom experiments have been performed to demonstrate the proposed approach's feasibility. Numerical simulation results indicate that the proposed kernel method can separate two FMT targets with an edge-to-edge distance of 1 mm and is robust to false positive guidance and inhomogeneity in the anatomical image. For the phantom experiments with two FMT targets, the kernel method has reconstructed both targets successfully, which further validates the proposed kernel method. We have compared the proposed kernel method with the soft prior method thoroughly and found that the kernel method without target segmentation is able to achieve similar anatomical guided results as the soft prior method.

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“…However, reconstructing the source distribution is extremely challenging because of how ill‐posed and ill‐conditioned the inverse problem is. In the last decade, extensive efforts have been made to develop image reconstruction algorithms for BLT, and many algorithms have been proposed, including using high‐order light propagation models , designing an effective regularization term , integrating a priori information provided by structural/functional imaging modalities or using multispectral measurements .…”

Section: Introductionmentioning

confidence: 99%

“…However, the assumption of a homogeneous optical property background can lead to inaccurate source localization in the reconstructed images when the tissues of mouse are heterogeneous . To overcome this, a promising alternative strategy is to incorporate structural information provided by computed tomography/magnetic resonance image (CT/MRI) images into the BLT reconstruction . The structural images can be segmented into several different regions.…”

Section: Introductionmentioning

confidence: 99%

“…The structural images can be segmented into several different regions. It is assumed that all finite elements within each region have the same optical properties , which has been proven to be feasible and efficient strategy for BLT reconstructions. This strategy is referred to as “hard‐prior.” An alternative, so‐called soft‐prior, approach allows optical property variation with a given region .…”

Section: Introductionmentioning

confidence: 99%

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Bayesian sparse‐based reconstruction in bioluminescence tomography improves localization accuracy and reduces computational time

Feng

Jia

et al. 2017

Journal of Biophotonics

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Bioluminescence tomography (BLT) provides fundamental insight into biological processes in vivo. To fully realize its potential, it is important to develop image reconstruction algorithms that accurately visualize and quantify the bioluminescence signals taking advantage of limited boundary measurements. In this study, a new 2-step reconstruction method for BLT is developed by taking advantage of the sparse a priori information of the light emission using multispectral measurements. The first step infers a wavelength-dependent prior by using all multi-wavelength measurements. The second step reconstructs the source distribution based on this developed prior. Simulation, phantom and in vivo results were performed to assess and compare the accuracy and the computational efficiency of this algorithm with conventional sparsity-promoting BLT reconstruction algorithms, and results indicate that the position errors are reduced from a few millimeters down to submillimeter, and reconstruction time is reduced by 3 orders of magnitude in most cases, to just under a few seconds. The recovery of single objects and multiple (2 and 3) small objects is simulated, and the recovery of images of a mouse phantom and an experimental animal with an existing luminescent source in the abdomen is demonstrated. Matlab code is available at https://github.com/jinchaofeng/code/tree/master.

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In vivo pentamodal tomographic imaging for small animals

Cited by 34 publications

References 38 publications

A novel in vivo Cerenkov luminescence image‐guided surgery on primary and metastatic colorectal cancer

A novel in vivo Cerenkov luminescence image‐guided surgery on primary and metastatic colorectal cancer

Anatomical image-guided fluorescence molecular tomography reconstruction using kernel method

Bayesian sparse‐based reconstruction in bioluminescence tomography improves localization accuracy and reduces computational time

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