Abstract
Magnetic resonance spectroscopy (MRS) allows non invasive detection of tissue metabolism. Absolute or relative quantification of metabolites and chemical compounds detected by 1H and 31P MRS can today be performed in a number of pathological tissues including different types of cancer lesions, consequently improving accuracy in disease diagnosis and prognosis. Methods allowing quantification of tumour metabolites under in vivo MR spectral profiles have been progressively developed in the past and are now entering the clinical routine. Several methods of quantification have been proposed and validated in healthy tissues in a number of single- and multi-centre studies. Most of these approaches are not always directly applicable to cancer lesions because of their inherent heterogeneity or variability in water content, thus peak area ratios can, in some cases, represent better indicators of disease progression and response to therapy. An additional important limitation may derive from the excessive duration of some MRS measurement protocols, which would be added to a routine clinical MRI examination in the same session. We propose a critical overview of the principal methods currently applied for the quantification of metabolites and chemical compounds, mostly detected by 1H MRS in tumours.
Keywords: Magnetic resonance spectroscopy (MRS), magnetic resonance spectroscopic imaging (MRSI), quantification, metabolism, tumours
Call for Papers in Thematic Issues
Advancements and Applications of Photon Counting CT: Transforming Imaging Precision and Quantification
Photon Counting Computed Tomography (CT) represents a transformative advancement in medical imaging technology, offering unprecedented levels of precision and diagnostic capability. This special issue will explore the latest innovations and applications of photon counting CT, highlighting its advantages over traditional energy-integrating detectors. The scope includes an in-depth examination of its ...read more
Advancements in Deep Learning based Medical Image and Signal Processing for Healthcare Applications
The accurate predication from medical data (signals and images) analytics will be achieved through the use of cutting-edge AI tools for tailoring medical treatment to each patient. When the agents are done analysing the user's health data, the user can consult with the agents to receive advice and the most ...read more
AI-Driven Diagnostics in Radiology and Medical Imaging
Medical imaging data, such as X-rays, MRIs, ultrasounds, CT scans, and DXAs, can be analysed by AI algorithms to help healthcare professionals identify and diagnose diseases more rapidly and accurately. Among the most cutting-edge applications of artificial intelligence (AI) in healthcare are imaging-related applications like cancer detection. AI could revolutionise ...read more
Applying medical image processing techniques for early cancer detection in mammograms
Breast cancer survival and successful treatment are significantly boosted by early identification. Breast cancer screening is most commonly accomplished with mammography. The radiologist's experience and the image quality, among several other factors, might affect how well a mammogram is interpreted. Enhancing mammography analysis and increasing cancer early detection are made ...read more
27