Dataset Information

Dual-Modality Positron Emission Tomography/Optical Image-Guided Photodynamic Cancer Therapy with Chlorin e6-Containing Nanomicelles.

ABSTRACT: Multifunctional nanoparticles with combined diagnostic and therapeutic functions show great promise in nanomedicine. Herein, we develop an organic photodynamic therapy (PDT) system based on polyethylene glycol (PEG)-coated nanomicelles conjugated with ?20% chlorin e6 (PEG-Ce 6 nanomicelles), which functions as an optical imaging agent, as well as a PDT agent. The formed PEG-Ce 6 nanomicelles with the size of ?20 nm were highly stable in various physiological solutions for a long time. Moreover, Ce 6 can also be a (64)Cu chelating agent for in vivo positron emission tomography (PET). By simply mixing, more than 90% of (64)Cu was chelator-free labeled on PEG-Ce 6 nanomicelles, and they also showed high stability in serum conditions. Both fluorescence imaging and PET imaging revealed that PEG-Ce 6 nanomicelles displayed high tumor uptake (13.7 ± 2.2%ID/g) after intravenous injection into tumor-bearing mice at the 48 h time point. In addition, PEG-Ce 6 nanomicelles exhibited excellent PDT properties upon laser irradiation, confirming the theranostic properties of PEG-Ce 6 nanomicelles for imaging and treatment of cancer. In addition, PDT was not shown to render any appreciable toxicity. This work presents a theranostic platform based on polymer nanomicelles with great potential in multimodality imaging-guided photodynamic cancer therapy.

SUBMITTER: Cheng L

PROVIDER: S-EPMC4995128 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Dual-Modality Positron Emission Tomography/Optical Image-Guided Photodynamic Cancer Therapy with Chlorin e6-Containing Nanomicelles.

Cheng Liang L Kamkaew Anyanee A Sun Haiyan H Jiang Dawei D Valdovinos Hector F HF Gong Hua H England Christopher G CG Goel Shreya S Barnhart Todd E TE Cai Weibo W

ACS nano 20160728 8

Multifunctional nanoparticles with combined diagnostic and therapeutic functions show great promise in nanomedicine. Herein, we develop an organic photodynamic therapy (PDT) system based on polyethylene glycol (PEG)-coated nanomicelles conjugated with ∼20% chlorin e6 (PEG-Ce 6 nanomicelles), which functions as an optical imaging agent, as well as a PDT agent. The formed PEG-Ce 6 nanomicelles with the size of ∼20 nm were highly stable in various physiological solutions for a long time. Moreover, ...[more]

PMID: 27459277

Similar Datasets

Project description:BackgroundSpatial normalization is a prerequisite step for analyzing positron emission tomography (PET) images both by using volume-of-interest (VOI) template and voxel-based analysis. Magnetic resonance (MR) or ligand-specific PET templates are currently used for spatial normalization of PET images. We used computed tomography (CT) images acquired with PET/CT scanner for the spatial normalization for [18F]-N-3-fluoropropyl-2-betacarboxymethoxy-3-beta-(4-iodophenyl) nortropane (FP-CIT) PET images and compared target-to-cerebellar standardized uptake value ratio (SUVR) values with those obtained from MR- or PET-guided spatial normalization method in healthy controls and patients with Parkinson's disease (PD).MethodsWe included 71 healthy controls and 56 patients with PD who underwent [18F]-FP-CIT PET scans with a PET/CT scanner and T1-weighted MR scans. Spatial normalization of MR images was done with a conventional spatial normalization tool (cvMR) and with DARTEL toolbox (dtMR) in statistical parametric mapping software. The CT images were modified in two ways, skull-stripping (ssCT) and intensity transformation (itCT). We normalized PET images with cvMR-, dtMR-, ssCT-, itCT-, and PET-guided methods by using specific templates for each modality and measured striatal SUVR with a VOI template. The SUVR values measured with FreeSurfer-generated VOIs (FSVOI) overlaid on original PET images were also used as a gold standard for comparison.ResultsThe SUVR values derived from all four structure-guided spatial normalization methods were highly correlated with those measured with FSVOI (P < 0.0001). Putaminal SUVR values were highly effective for discriminating PD patients from controls. However, the PET-guided method excessively overestimated striatal SUVR values in the PD patients by more than 30% in caudate and putamen, and thereby spoiled the linearity between the striatal SUVR values in all subjects and showed lower disease discrimination ability. Two CT-guided methods showed comparable capability with the MR-guided methods in separating PD patients from controls and showed better correlation between putaminal SUVR values and the parkinsonian motor severity than the PET-guided method.ConclusionCT-guided spatial normalization methods provided reliable striatal SUVR values comparable to those obtained with MR-guided methods. CT-guided methods can be useful for analyzing dopamine transporter PET images when MR images are unavailable.

Project description:BackgroundDeep learning has recently shown great potential in medical image reconstruction tasks. For positron emission tomography (PET) images, the direct reconstruction from raw data to radioactivity images using deep learning without any constraint may lead to the production of nonexistent structures. The aim of this study was to specifically develop and test a flexibly deep learning-based reconstruction network guided by any form of prior knowledge to achieve high quality and high reliability reconstruction.MethodsWe developed a novel prior information-guided reconstruction network (PIGRN) with a dual-channel generator and a 2-scale discriminator based on a conditional generative adversarial network (cGAN). Besides the raw data channel, an additional channel is provided in the generator for prior information (PI) to guide the training phase. The PI can be reconstructed images obtained via conventional methods, nuclear medical images from other modalities, attenuation correction maps from time-of-flight-PET (TOF-PET) data, or any other physical parameters. For this study, the reconstructed images generated by filtered back projection (FBP) were chosen as the input of the additional channel. To improve the image quality, a 2-scale discriminator was adopted which can focus on both the coarse and fine field of the reconstruction images. Experiments were carried out on both a simulation dataset and a real Sprague Dawley (SD) rat dataset.ResultsTwo classic deep learning-based reconstruction networks, including U-Net and Deep-PET, were compared in our study. Compared with these two methods, our method could provide much higher quality PET image reconstruction in the study of the simulation dataset. The peak signal-to-noise ratio (PSNR) value reached 31.8498, and the structure similarity index measure (SSIM) value reached 0.9754. The real study on SD rats indicated that the proposed network also has strong generalization ability.ConclusionsThe flexible PIGRN based on cGAN for PET images combines both raw data and PI. The results of comparison experiments and a generalization experiment based on simulation and SD rat datasets demonstrated that the proposed PIGRN has the ability to improve image quality and has strong generalization ability.

Dataset Information

Dual-Modality Positron Emission Tomography/Optical Image-Guided Photodynamic Cancer Therapy with Chlorin e6-Containing Nanomicelles.

Publications

Dual-Modality Positron Emission Tomography/Optical Image-Guided Photodynamic Cancer Therapy with Chlorin e6-Containing Nanomicelles.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets