# Image Reconstruction: From Sparsity to Data-adaptive Methods and Machine Learning

## TL;DR

This paper reviews recent advances in medical image reconstruction, emphasizing sparsity, low-rank models, and machine learning approaches that adapt to data for improved image quality and efficiency.

## Contribution

It provides a comprehensive overview of the evolution from analytical to data-driven methods, highlighting recent trends in sparsity, low-rank models, and machine learning techniques.

## Key findings

- Data-driven methods outperform traditional models in certain scenarios.
- Sparsity and low-rank models enable reduced data acquisition.
- Machine learning approaches improve reconstruction quality and speed.

## Abstract

The field of medical image reconstruction has seen roughly four types of methods. The first type tended to be analytical methods, such as filtered back-projection (FBP) for X-ray computed tomography (CT) and the inverse Fourier transform for magnetic resonance imaging (MRI), based on simple mathematical models for the imaging systems. These methods are typically fast, but have suboptimal properties such as poor resolution-noise trade-off for CT. A second type is iterative reconstruction methods based on more complete models for the imaging system physics and, where appropriate, models for the sensor statistics. These iterative methods improved image quality by reducing noise and artifacts. The FDA-approved methods among these have been based on relatively simple regularization models. A third type of methods has been designed to accommodate modified data acquisition methods, such as reduced sampling in MRI and CT to reduce scan time or radiation dose. These methods typically involve mathematical image models involving assumptions such as sparsity or low-rank. A fourth type of methods replaces mathematically designed models of signals and systems with data-driven or adaptive models inspired by the field of machine learning. This paper focuses on the two most recent trends in medical image reconstruction: methods based on sparsity or low-rank models, and data-driven methods based on machine learning techniques.