Introduction Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI) is a quantitative MRI method, where the diffusion of water molecules is exploited as a source of contrast among tissues. By sensitising DW-MR-measurements to diffusion, we enable the non-invasive quantification of tissue microstructure. The signal observed in a voxel arises from contributions of various diffusion processes within different cellular components (i.e. the tissue microstructure). Many methods exist aiming to disentangle these signal contributions and to compute biologically meaningful microstructure indices. A new, innovative method is using Artificial Intelligence (AI) to directly predict microstructure from DW-MR-signal data, instead of relying on signal representations or biophysical models.
Objective The objective of this project is the development of a novel AI-based method to estimate microstructural parameters in the liver, specifically cell size and intracellular fraction, given paired examples of DW-MRI signals and microstructural features as seen on histology. We aim to outperform the estimation of microstructure by a well-established biophysical model. The method will be developed on pre-clinical data, and will be tested by deployment on in vivo patient data.
Methods Seven algorithms were implemented in Python, of which four voxel-wise Machine Learning (ML) (Linear Regression, Piecewise Linear Regression, K-Nearest Neighbour, Random-Forest Regres sor (RFR)), one patch-wise ML (PW-RFR), one voxel-wise Deep Learning (DL) (Multi-Layer-Perceptron (MLP)) and one patch-wise DL (PW-MLP). The algorithms were trained in a supervised manner, using MRI-derived data co-registered to histology-derived parametric maps of the targets (i.e. the ground truth) of seven mouse livers. The performance of the algorithms based on three input feature types is analysed: (i) the raw DW-MR-data, (ii) signal representations, (iii) parametric maps computed by fitting a multi-exponential biophysical model. Quantitative evaluation metrics (Mean Absolute Error (MAE) and Relative MAE) and visual inspection are used for performance assessment. The most promising pre-trained algorithm from the pre-clinical study is consequently tested on clinical data of three patients.
Results Based on quantitive evaluation metrics and visual inspection of the predictions in comparison with the histology-derived ground truth, the PW-RFR was found to be the most promising algorithm. With the PW-MLP exhibiting the second best results, the importance of incorporating spatial information, here via the patch-wise approach, is supported. No large differences were observed in the performance of the best algorithm, the PW-RFR, for the three input feature types. From the clinical study appeared that the PW-RFR is able to at least partly capture notable contrasts in the lesion microstructure, and the predictions are in a realistic range in comparison with histology-derived microstructure values as obtained via biopsy of the lesions.
Conclusion The PW-RFR algorithm demonstrates a promising performance in both the pre-clinical and the clinical experiment. This points towards the feasibility of developing tools that learn how to predict histopathological characteristics from DW-MRI on pre-clinical MRI-histology data, and consequently deploy these tools in a clinical setting. However, these results are preliminary, and further validation on larger and more diverse datasets is required.

Introduction: Magnetic Resonance Imaging is a commonly used technique for the initial diagnosis of gliomas. T1, T2, T2-FLAIR, and post-contrast T1 with gadolinium-based contrast agents (GBCAs) can show tumor characteristics. However, using this contrast agent poses a risk to patients with kidney failures, has environmental impact, and increases cost. To address these issues, we aimed to evaluate the potential of deep learning in generating synthetic post-contrast T1 images without using contrast agents.

Method: The project investigates the potential of using deep learning (DL) to generate synthetic post-contrast T1 images based on T1, T2, and T2-FLAIR provided by the Erasmus Glioma Database. Exploring different model architectures, loss functions, and input sizes to discover the optimal approach.

Results: Results show that individual loss functions, input size, and model complexity slightly impact the accuracy of synthetic post-contrast T1 images. Combining loss functions, however, was the most promising approach for image generation. Models trained with ℒm could generate low detail enhancement. Resulting in 0.0478±0.0076, 0.0139±0.0036, and 0.879±0.024 for MAE, MSE, and SSIM, respectively.

Conclusion: The study's findings indicate that DL is promising for generating synthetic post-contrast T1 images without using GBCAs. However, further research is required to generate realistic synthetic post-contrast T1 images. The study, however, provides a basis for future work and highlights the importance of reducing the use of GBCAs in clinical practice.