Self-supervised multicontrast super-resolution for diffusion-weighted prostate MRI

Purpose: This study addresses the challenge of low resolution and signal-to-noise ratio (SNR) in diffusion-weighted images (DWI), which are pivotal for cancer detection. Traditional methods increase SNR at high b-values through multiple acquisitions, but this results in diminished image resolution due to motion-induced variations. Our research aims to enhance spatial resolution by exploiting the global structure within multicontrast DWI scans and millimetric motion between acquisitions. Methods: We introduce a novel approach employing a "Perturbation Network" to learn subvoxel-size motions between scans, trained jointly with an implicit neural representation (INR) network. INR encodes the DWI as a continuous volumetric function, treating voxel intensities of low-resolution acquisitions as discrete samples. By evaluating this function with a finer grid, our model predicts higher-resolution signal intensities for intermediate voxel locations. The Perturbation Network's motion-correction efficacy was validated through experiments on biological phantoms and in vivo prostate scans. Results: Quantitative analyses revealed significantly higher structural similarity measures of super-resolution images to ground truth high-resolution images compared to high-order interpolation (p Conclusion: High-resolution details in DWI can be obtained without the need for high-resolution training data. One notable advantage of the proposed method is that it does not require a super-resolution training set. This is important in clinical practice because the proposed method can easily be adapted to images with different scanner settings or body parts, whereas the supervised methods do not offer such an option.</p

Redefining the biophysical basis of contrast in diffusion-weighted MRI of the prostate

Purpose: The study investigates the hypothesis that the clinically observed decrease in ADC with increasing prostate cancer Gleason grade can be attributed to an increasing volume of low diffusivity epithelial cells, and corresponding decreasing volumes of higher diffusivity stroma and lumen space, rather than increased cell density. Methods: Whole human prostates and tissue cores were imaged post radical prostatectomy on 9.4T and 16.4T Bruker MRI systems respectively. Digital histology images were analysed andDWI measurements were correlated with regions of interest in the prostate specimens. The morphometric measured volumes and diffusivities of these distinct tissue components were used to predict ADC. Results: There was a stronger correlationof Gleason pattern with gland component volumes (n=553) of epithelium (ρ=0.898), stroma (ρ=-0.651), and lumen (ρ=-0.912), than with cellularity metrics (n=288) nuclear area (ρ=0.422) or count (ρ=0.081). There was a stronger correlation between measured ADC and epithelium (r=0.688) and lumen volume (r=-0.647), than between measured ADC and nuclear count (r =-0.598) or area (r=-0.569) (n=57). The differences between cancer and normal tissue, and correlations with pathology are stronger, for the gland component volumes than for the cellularity metrics. ADC predicted (n=118) showed the strongest correlation (r=0.935) with measured ADC and follows the trends seen clinically. Conclusion: Differences in the gland compartment volumes of prostate tissue having distinct diffusivities, rather than changes in the conventionally cited ‘cellularity’ metrics, are the major contributor to clinically observed variations of ADC in prostate tissue. The findings affect the interpretation of prostate DWI in terms of tissue structure and indicate a closer relationship between diffusion contrast and the histopathologic features of cancer than previously assumed

A radiomics based method for prediction of prostate cancer Gleason score using enlarged region of interest

Abstract Background Prostate cancer (PCa) is one of the most common cancers in men worldwide, and its timely diagnosis and treatment are becoming increasingly important. MRI is in increasing use to diagnose cancer and to distinguish between non-clinically significant and clinically significant PCa, leading to more precise diagnosis and treatment. The purpose of this study is to present a radiomics-based method for determining the Gleason score (GS) for PCa using tumour heterogeneity on multiparametric MRI (mp-MRI). Methods Twenty-six patients with biopsy-proven PCa were included in this study. The quantitative T2 values, apparent diffusion coefficient (ADC) and signal enhancement rates (α) were calculated using multi-echo T2 images, diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MRI (DCE-MRI), for the annotated region of interests (ROI). After texture feature analysis, ROI range expansion and feature filtering was performed. Then obtained data were put into support vector machine (SVM), K-Nearest Neighbor (KNN) and other classifiers for binary classification. Results The highest classification accuracy was 73.96% for distinguishing between clinically significant (Gleason 3 + 4 and above) and non-significant cancers (Gleason 3 + 3) and 83.72% for distinguishing between Gleason 3 + 4 from Gleason 4 + 3 and above, which was achieved using initial ROIs drawn by the radiologists. The accuracy improved when using expanded ROIs to 80.67% using SVM and 88.42% using Bayesian classification for distinguishing between clinically significant and non-significant cancers and Gleason 3 + 4 from Gleason 4 + 3 and above, respectively. Conclusions Our results indicate the research significance and value of this study for determining the GS for prostate cancer using the expansion of the ROI region

Multi-model sequential analysis of MRI data for microstructure prediction in heterogeneous tissue

We propose a general method for combining multiple models to predict tissue microstructure, with an exemplar using in vivo diffusion-relaxation MRI data. The proposed method obviates the need to select a single ’optimum’ structure model for data analysis in heterogeneous tissues where the best model varies according to local environment. We break signal interpretation into a three-stage sequence: (1) application of multiple semi-phenomenological models to predict the physical properties of tissue water pools contributing to the observed signal; (2) from each Stage-1 semi-phenomenological model, application of a tissue microstructure model to predict the relative volumes of tissue structure components that make up each water pool; and (3) aggregation of the predictions of tissue structure, with weightings based on model likelihood and fractional volumes of the water pools from Stage-1. The multiple model approach is expected to reduce prediction variance in tissue regions where a complex model is overparameterised, and bias where a model is underparameterised. The separation of signal characterisation (Stage-1) from biological assignment (Stage-2) enables alternative biological interpretations of the observed physical properties of the system, by application of different tissue structure models. The proposed method is exemplified with human prostate diffusion-relaxation MRI data, but has potential application to a wide range of analyses where a single model may not be optimal throughout the sampled domain