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Quantitative susceptibility mapping (QSM), one of the advanced MRI techniques for evaluating magnetic susceptibility, offers precise quantitative measurements of spatial distributions of magnetic susceptibility. Magnetic susceptibility describes the magnetizability of a material to an applied magnetic field and is a substance-specific value. Recently, QSM has been widely used to estimate various levels of substances in the brain, including iron, hemosiderin, and deoxyhemoglobin (paramagnetism), as well as calcification (diamagnetism). By visualizing iron distribution in the brain, it is possible to identify anatomic structures that are not evident on conventional images and to evaluate various neurodegenerative diseases. It has been challenging to apply QSM in areas outside the brain because of motion artifacts from respiration and heartbeats, as well as the presence of fat, which has a different frequency to the proton. In this review, the authors provide a brief overview of the theoretical background and analyze methods of converting MRI phase images to QSM. Moreover, we provide an overview of the current clinical applications of QSM. RSNA, 2022.
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http://dx.doi.org/10.1148/rg.210054 | DOI Listing |
Eur J Orthop Surg Traumatol
September 2025
Department of Orthopedics, Shanghai Changzheng Hospital, Shanghai, China.
Purpose: To investigate the images and treatment differences for Type IIIa atlantoaxial rotary dislocation (AARD) by comparing the imaging characteristics of patients with Type III and Type IIIa AARD.
Methods: The present study retrospectively analyzed a cohort of 35 patients who underwent posterior C1-C2 intra-articular fusion due to AARD from our hospital database. Among them, 23 patients were diagnosed with Type III AARD, while the remaining 12 patients were diagnosed with Type IIIa AARD.
Radiology
September 2025
Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, Md.
Background Elevated brain iron is a potential marker for neurodegeneration, but its role in predicting onset of mild cognitive impairment (MCI) and prospective cognitive trajectories remains unclear. Purpose To investigate how brain iron and amyloid-β (Aβ) levels, measured using quantitative susceptibility mapping (QSM) MRI and PET, help predict MCI onset and cognitive decline. Materials and Methods In this prospective study conducted between January 2015 and November 2022, cognitively unimpaired older adults underwent baseline QSM MRI.
View Article and Find Full Text PDFAnal Chem
September 2025
Environmental NMR Center, University of Toronto, 1265 Military Trail, Toronto, Ontario M1C 1A4, Canada.
Nuclear Magnetic Resonance (NMR) spectroscopy is a powerful analytical technique with a wide range of applications. To support the analysis of diverse and complex samples, various NMR tools and accessories have been created. Three-dimensional (3D) printing is an underutilized production method for NMR hardware, mainly due to the lack of H NMR background-free resins.
View Article and Find Full Text PDFAdv Healthc Mater
September 2025
Krembil Research Institute, Toronto Western Hospital, University Health Network, Toronto, ON, M5T 0S8, Canada.
Accurate brain signal recording and precise electrode placement are critical for the success of neuromodulation therapies such as deep brain stimulation (DBS). Addressing these challenges requires deep brain electrodes that provide high-quality, stable recordings while remaining compatible with high-resolution medical imaging modalities like magnetic resonance imaging (MRI). Moreover, such electrodes shall be cost-effective, easy to manufacture, and patient-compatible.
View Article and Find Full Text PDFJ Obes
September 2025
School of Natural Sciences, University of Lincoln, Lincoln, UK.
To investigate the genetic determinants of fat distribution across anatomical sites and their implications for health outcomes. We analyzed neck-to-knee MRI data from the UK Biobank ( = 37,589) to measure fat at various locations and used Mendelian randomization to assess effects on 26 obesity-related diseases and 94 biomarkers from FinnGen and other consortia. We identified genetic loci associated with 10 fat depots: abdominal subcutaneous adipose tissue ( = 2 loci), thigh subcutaneous adipose tissue (25), thigh intermuscular adipose tissue (15), visceral adipose tissue (7), liver proton density fat fraction (PDFF) (8), pancreas PDFF (11), paraspinal adipose tissue (9), pelvic bone marrow fat (28), thigh bone marrow fat (27), and vertebrae bone marrow fat (5).
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