Diagnostic imaging (ionizing and non-ionizing)
In many clinical MRI scenarios, existing imaging information can be used to significantly shorten acquisition time or to improve Signal to Noise Ratio (SNR). In this paper the authors present a framework, referred to as FASTMER, for fast MRI by exploiting a reference image.
The proposed approach utilizes the possible similarity of the reference image to the acquired image, which exists in many clinical MRI imaging scenarios. Examples include similarity between adjacent slices in high resolution MRI, similarity between various contrasts in the same scan and similarity between different scans of the same patient. To account for the fact that the reference image may exhibit low similarity with the acquired image the authors develop an iterative weighted reconstruction approach, which tunes the weights according to the degree of similarity.
Experimental results demonstrate the performance of the method in three different clinical MRI scenarios: The first example demonstrates SNR improvement in high resolution brain MRI, the second scenario exploits similarity between T2-weighted and fluid-attenuated inversion recovery (FLAIR) for fast FLAIR scanning and the last application utilizes similarity between baseline and follow-up scans for fast follow-up acquisition. The results show that FASTMER outperforms image reconstruction of existing state-of-the-art methods.
The authors present a framework for fast MRI by exploiting a reference image. Recovery is based on an iterative algorithm that supports cases in which similarity to the reference scan is not guaranteed. This extends the applicability of the FASTMER to different MRI scanning scenarios. Thanks to the existence of reference images in various clinical imaging tasks, the proposed framework can play a major role in improving reconstruction in many MR applications.