New Imaging Frontiers in Cardiology: Fast and Quantitative Maps from Raw Data

Area of Science:

• Cardiovascular Imaging
• Medical Image Analysis
• Quantitative Imaging

Background:

• Quantitative parametric maps are crucial in cardiovascular imaging research.
• These maps are typically generated by post-processing dynamic image sets acquired at different temporal intervals and contrasts.
• Magnetic resonance imaging (MRI) and emission tomography (PET, SPECT) are key techniques for quantitative map formation.

Purpose of the Study:

• To present fast methods for obtaining parametric maps directly from acquired raw data.
• To review both established clinical research methods and innovative approaches.
• To highlight advancements in accelerating raw data generation and map formation.

Main Methods:

• Discussing methods for accelerating magnetic resonance imaging (MRI) raw data acquisition through k-space sub-sampling.
• Reviewing recently developed methods for generating parametric maps from MRI data.
• Overviewing conventional and direct estimation algorithms for parametric image reconstruction from dynamic positron emission tomography (PET) data.

Main Results:

• Magnetic resonance imaging (MRI) techniques can accelerate raw data generation using k-space sub-sampling.
• New methods enable faster generation of MR parametric maps.
• Direct estimation algorithms offer efficient parametric image reconstruction from dynamic PET data.

Conclusions:

• An overview of approaches for creating useful parametric maps from imaging raw data is provided.
• The review progresses from conventional methods to recent, efficient techniques for accelerating data generation and map formation.
• These advancements aim to improve the speed and efficiency of quantitative cardiovascular imaging.