Application Progress of MRI-IVIM, T1mapping and T2mapping in Diagnosis and Treatment of Breast Diseases

This article summarizes the application progress of MRI-IVIM, T1mapping, and T2mapping techniques in the diagnosis and treatment of breast diseases. These techniques, leveraging their advantages of being non-radiative, high soft tissue resolution, and multi-functional imaging, provide significant support for the diagnosis of breast diseases. IVIM technology measures the microscopic motion of water molecules to reflect the microcirculatory perfusion of lesions; T1mapping technology measures T1 relaxation time to reveal the physical properties of tissues, aiding in distinguishing different types of breast tissue; T2mapping technology measures T2 relaxation time to reflect changes in the microscopic structure of tissues, providing sensitive indicators for early detection of breast diseases. These techniques can distinguish between benign and malignant lesions, assess the tumor microenvironment, predict breast cancer subtypes, and evaluate treatment efficacy, thus providing a basis for the selection and adjustment of treatment plans. However, these techniques also face challenges such as high costs and limited accessibility, lack of standardized data interpretation, and prolonged examination times that affect patient comfort. Future development directions will focus on the integration of technologies and multi-modal imaging, artificial intelligence and automation, and technological optimization and cost reduction, aiming to achieve personalized diagnosis and precise treatment of breast diseases, thereby improving treatment outcomes and patient quality of life.

Authors

Publication Details

Journal
International Journal of Biology and Life Sciences
Published
2024-11-22
DOI
https://doi.org/10.54097/pkwysa27
Citations
1
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.40
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article

Application Progress of MRI-IVIM, T1mapping and T2mapping in Diagnosis and Treatment of Breast Diseases

Hao Xiong, Xiong Zhang, Yao Zhang
1 citations
International Journal of Biology and Life Sciences
Radiomics and Machine Learning in Medical Imaging
0.40
article

Application Progress of MRI-IVIM, T1mapping and T2mapping in Diagnosis and Treatment of Breast Diseases

Hao Xiong, Xiong Zhang, Yao Zhang
article en
1 citations

Abstract

This article summarizes the application progress of MRI-IVIM, T1mapping, and T2mapping techniques in the diagnosis and treatment of breast diseases. These techniques, leveraging their advantages of being non-radiative, high soft tissue resolution, and multi-functional imaging, provide significant support for the diagnosis of breast diseases. IVIM technology measures the microscopic motion of water molecules to reflect the microcirculatory perfusion of lesions; T1mapping technology measures T1 relaxation time to reveal the physical properties of tissues, aiding in distinguishing different types of breast tissue; T2mapping technology measures T2 relaxation time to reflect changes in the microscopic structure of tissues, providing sensitive indicators for early detection of breast diseases. These techniques can distinguish between benign and malignant lesions, assess the tumor microenvironment, predict breast cancer subtypes, and evaluate treatment efficacy, thus providing a basis for the selection and adjustment of treatment plans. However, these techniques also face challenges such as high costs and limited accessibility, lack of standardized data interpretation, and prolonged examination times that affect patient comfort. Future development directions will focus on the integration of technologies and multi-modal imaging, artificial intelligence and automation, and technological optimization and cost reduction, aiming to achieve personalized diagnosis and precise treatment of breast diseases, thereby improving treatment outcomes and patient quality of life.

International Journal of Biology and Life SciencesVol. 8(1)
Good health and well-being
Openalex Percentile: Top 32%
Radiomics and Machine Learning in Medical Imaging
0.40
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