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Noninvasive characterization of in situ forming implant diffusivity using diffusion-weighted MRI.

ABSTRACT: In situ forming implants (ISFIs) form a solid drug-eluting depot, releasing drug for an extended period of time after a minimally-invasive injection. Clinical use of ISFIs has been limited because many factors affect drug release kinetics. The aim of this study was to use diffusion-weighted MRI (DWI) to noninvasively quantify spatial-temporal changes in implant diffusivity in situ. ISFIs were formed using poly(lactic-co-glycolic) acid, with a molecular weight of either 15?kDa or 52?kDa, and fluorescein as the mock drug. Drug release, polymer erosion, polymer degradation, and implant diffusivity were analyzed in vitro over 21 days. DWI was also performed in vivo over 5 days. Spatial diffusivity maps of the implant were generated using DWI data. Results showed constant diffusivity at the implant shell ((1.17?±?0.13)?×?10-3 mm2/s) and increasing diffusivity within the interior over time (from (0.268?±?0.081)?×?10-3 mm2/s during day 1 to (1.88?±?0.04)?×?10-3 mm2/s at 14 d), which correlated with increasing porosity of the implant microstructure. Implants formed in vivo followed the same diffusivity trend as those in vitro. This study validates the use of DWI to provide novel functional information about implant behavior through its ability to noninvasively characterize transport properties within the implant both in vitro and in vivo.

SUBMITTER: Hopkins KA

PROVIDER: S-EPMC6815723 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Noninvasive characterization of in situ forming implant diffusivity using diffusion-weighted MRI.

Hopkins Kelsey A KA Vike Nicole N Li Xin X Kennedy Jacqueline J Simmons Emma E Rispoli Joseph J Solorio Luis L

Journal of controlled release : official journal of the Controlled Release Society 20190716

In situ forming implants (ISFIs) form a solid drug-eluting depot, releasing drug for an extended period of time after a minimally-invasive injection. Clinical use of ISFIs has been limited because many factors affect drug release kinetics. The aim of this study was to use diffusion-weighted MRI (DWI) to noninvasively quantify spatial-temporal changes in implant diffusivity in situ. ISFIs were formed using poly(lactic-co-glycolic) acid, with a molecular weight of either 15 kDa or 52 kDa, and fluo ...[more]

PMID: 31323243

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Project description:Diffusion weighted imaging (DWI) constitutes a major functional parameter performed in Magnetic Resonance Imaging (MRI). The DW sequence is performed by acquiring a set of native images described by their b-values, each b-value representing the strength of the diffusion MR gradients specific to that sequence. By fitting the data with models describing the motion of water in tissue, an apparent diffusion coefficient (ADC) map is built and allows the assessment of water mobility inside the tissue. The high cellularity of tumors restricts the water diffusion and decreases the value of ADC within tumors, which makes them appear hypointense on ADC maps. The role of this sequence now largely exceeds its first clinical apparitions in neuroimaging, whereby the method helped diagnose the early phases of cerebral ischemic stroke. The applications extend to whole-body imaging for both neoplastic and non-neoplastic diseases. This review emphasizes the integration of DWI in the genitourinary system imaging by outlining the sequence's usage in female pelvis, prostate, bladder, penis, testis and kidney MRI. In gynecologic imaging, DWI is an essential sequence for the characterization of cervix tumors and endometrial carcinomas, as well as to differentiate between leiomyosarcoma and benign leiomyoma of the uterus. In ovarian epithelial neoplasms, DWI provides key information for the characterization of solid components in heterogeneous complex ovarian masses. In prostate imaging, DWI became an essential part of multi-parametric Magnetic Resonance Imaging (mpMRI) to detect prostate cancer. The Prostate Imaging-Reporting and Data System (PI-RADS) scoring the probability of significant prostate tumors has significantly contributed to this success. Its contribution has established mpMRI as a mandatory examination for the planning of prostate biopsies and radical prostatectomy. Following a similar approach, DWI was included in multiparametric protocols for the bladder and the testis. In renal imaging, DWI is not able to robustly differentiate between malignant and benign renal tumors but may be helpful to characterize tumor subtypes, including clear-cell and non-clear-cell renal carcinomas or low-fat angiomyolipomas. One of the most promising developments of renal DWI is the estimation of renal fibrosis in chronic kidney disease (CKD) patients. In conclusion, DWI constitutes a major advancement in genitourinary imaging with a central role in decision algorithms in the female pelvis and prostate cancer, now allowing promising applications in renal imaging or in the bladder and testicular mpMRI.

Project description:IntroductionAcquisition and pre-processing pipelines for diffusion-weighted imaging (DWI) volumes are resource- and time-consuming. Generating synthetic DWI scalar maps from commonly acquired brain MRI sequences such as fluid-attenuated inversion recovery (FLAIR) could be useful for supplementing datasets. In this work we design and compare GAN-based image translation models for generating DWI scalar maps from FLAIR MRI for the first time.MethodsWe evaluate a pix2pix model, two modified CycleGANs using paired and unpaired data, and a convolutional autoencoder in synthesizing DWI fractional anisotropy (FA) and mean diffusivity (MD) from whole FLAIR volumes. In total, 420 FLAIR and DWI volumes (11,957 images) from multi-center dementia and vascular disease cohorts were used for training/testing. Generated images were evaluated using two groups of metrics: (1) human perception metrics including peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), (2) structural metrics including a newly proposed histogram similarity (Hist-KL) metric and mean squared error (MSE).ResultsPix2pix demonstrated the best performance both quantitatively and qualitatively with mean PSNR, SSIM, and MSE metrics of 23.41 dB, 0.8, 0.004, respectively for MD generation, and 24.05 dB, 0.78, 0.004, respectively for FA generation. The new histogram similarity metric demonstrated sensitivity to differences in fine details between generated and real images with mean pix2pix MD and FA Hist-KL metrics of 11.73 and 3.74, respectively. Detailed analysis of clinically relevant regions of white matter (WM) and gray matter (GM) in the pix2pix images also showed strong significant (p < 0.001) correlations between real and synthetic FA values in both tissue types (R = 0.714 for GM, R = 0.877 for WM).Discussion/conclusionOur results show that pix2pix's FA and MD models had significantly better structural similarity of tissue structures and fine details than other models, including WM tracts and CSF spaces, between real and generated images. Regional analysis of synthetic volumes showed that synthetic DWI images can not only be used to supplement clinical datasets, but demonstrates potential utility in bypassing or correcting registration in data pre-processing.

Dataset Information

Noninvasive characterization of in situ forming implant diffusivity using diffusion-weighted MRI.

Publications

Noninvasive characterization of in situ forming implant diffusivity using diffusion-weighted MRI.

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