Medical Image Reconstruction: From Analytical and Iterative Methods to Machine Learning

-15% su kodu: ENG15
91,69 
Įprasta kaina: 107,87 
-15% su kodu: ENG15
Kupono kodas: ENG15
Akcija baigiasi: 2025-03-03
-15% su kodu: ENG15
91,69 
Įprasta kaina: 107,87 
-15% su kodu: ENG15
Kupono kodas: ENG15
Akcija baigiasi: 2025-03-03
-15% su kodu: ENG15
2025-02-28 107.8700 InStock
Nemokamas pristatymas į paštomatus per 11-15 darbo dienų užsakymams nuo 10,00 

Knygos aprašymas

This textbook introduces the essential concepts of tomography in the field of medical imaging. The medical imaging modalities include x-ray CT (computed tomography), PET (positron emission tomography), SPECT (single photon emission tomography) and MRI. In these modalities, the measurements are not in the image domain and the conversion from the measurements to the images is referred to as the image reconstruction.

The work covers various image reconstruction methods, ranging from the classic analytical inversion methods to the optimization-based iterative image reconstruction methods. As machine learning methods have lately exhibited astonishing potentials in various areas including medical imaging the author devotes one chapter to applications of machine learning in image reconstruction.

Based on college level in mathematics, physics, and engineering the textbook supports students in understanding the concepts. It is an essential reference for graduate students and engineers with electrical engineering and biomedical background due to its didactical structure and the balanced combination of methodologies and applications,

Informacija

Autorius: Gengsheng Lawrence Zeng
Serija: De Gruyter Textbook
Leidėjas: Walter de Gruyter
Išleidimo metai: 2023
ISBN-10: 3111055035
ISBN-13: 9783111055039
Formatas: 240 x 170 x 17 mm. Knyga minkštu viršeliu
Kalba: Anglų

Pirkėjų atsiliepimai

Parašykite atsiliepimą apie „Medical Image Reconstruction: From Analytical and Iterative Methods to Machine Learning“

Būtina įvertinti prekę

Goodreads reviews for „Medical Image Reconstruction: From Analytical and Iterative Methods to Machine Learning“