Magnetic Resonance Image Reconstruction: Theory, Methods, and Applications

Magnetic Resonance Image Reconstruction: Theory, Methods, and Applications is a comprehensive book that discusses MR image reconstruction, including its inverse problem, models, and optimization methods for various applications. It is suitable for physicists, engineers, technologists, and clinicians interested in medical image reconstruction and MRI.

Format: Paperback / softback
Length: 516 pages
Publication date: 11 November 2022
Publisher: Elsevier Science Publishing Co Inc

Magnetic Resonance Image Reconstruction: Theory, Methods, and Applications delves into the intricate realm of MR image reconstruction, encompassing its conceptual framework as an inverse problem and the diverse models and optimization techniques employed to reconstruct MR images with precision. The book explores a range of applications, including non-Cartesian imaging, undersampled reconstruction, motion correction, dynamic imaging, and quantitative MRI. Designed for physicists, engineers, technologists, and clinicians with a keen interest in medical image reconstruction and MRI, this comprehensive resource offers a valuable insights into the field.

The theory of magnetic resonance image reconstruction is a fundamental aspect of medical imaging that involves the conversion of raw MR data into meaningful visual representations. This process involves several steps, including image acquisition, preprocessing, reconstruction algorithms, and post-processing.

Image acquisition is the first step in MR image reconstruction, where a magnetic field is applied to the body and radio waves are emitted. These radio waves are then detected by a receiver array and converted into digital signals. The raw MR data obtained from this process is typically complex and contains a large amount of information.

Preprocessing is the second step in MR image reconstruction, where the raw MR data is processed to remove noise, artifacts, and other unwanted components. This step is crucial in improving the quality of the reconstructed images. Preprocessing techniques include spatial and temporal filtering, noise reduction, and image registration.

Reconstruction algorithms are the third step in MR image reconstruction, where the raw MR data is used to generate a reconstructed image. There are several reconstruction algorithms available, including filtered backprojection, algebraic reconstruction techniques (ART), and iterative reconstruction techniques (IRT). Each algorithm has its own advantages and disadvantages, and the choice of algorithm depends on the specific application and the characteristics of the MR data.

Post-processing is the fourth step in MR image reconstruction, where the reconstructed image is further processed to enhance its visual quality and interpretability. Post-processing techniques include contrast enhancement, spatial resolution enhancement, and image registration.

In conclusion, magnetic resonance image reconstruction is a complex process that involves several steps, including image acquisition, preprocessing, reconstruction algorithms, and post-processing. By understanding the theory and principles of MR image reconstruction, researchers and clinicians can develop new and improved reconstruction techniques to improve the accuracy and quality of medical images.

Weight: 1100g
Dimension: 236 x 192 x 33 (mm)
ISBN-13: 9780128227268