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Complementary information from multi-modal MRI is widely used in clinical practice for disease diagnosis. Due to scan time limitations, image corruptions, and different acquisition protocols, one or more contrasts may be missing or unusable. Recently developed CNN models for contrast synthesis are unable to capture the intricate dependencies between input contrasts and are not dynamic to the varying number of inputs. This work proposes a novel Multi-contrast and Multi-scale vision Transformer (MMT) that can take any number and combination of input sequences and synthesize the missing contrasts.

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Complementary information from multi-contrast MRI data is used in deep learning algorithms for reducing contrast dosage in brain MRI. Though existing models produce clinically equivalent post-contrast images, they lack explainability in terms of mapping the source of contrast information from input to output. In this work we explore the feasibility of an explainable deep learning model for gadolinium dose reduction in contrast-enhanced brain MRI.

Image registration is a crucial preprocessing step for many downstream analysis tasks. Existing iterative methods for affine registration are accurate but time consuming. We propose a deep learning (DL) based unsupervised affine registration algorithm that executes orders of magnitude faster when compared to conventional registration toolkits. The proposed algorithm aligns 3D volumes from the same modality (e.g. T1 vs T1-CE) as well as different modalities (e.g. T1 vs T2). We train the model and perform quantitative evaluation using a pre-registered brain MRI public dataset.