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MRI Scans

Magnetic Resonance Imaging (MRI) Scans: A Powerful Diagnostic Tool

Magnetic Resonance Imaging (MRI) is a non-invasive imaging technique that uses strong magnetic fields and radio waves to produce detailed images of the body's internal structures.
MRI scans allow healthcare professionals to diagnose a wide range of medical conditions, from brain disorders to musculoskeletal injuries, with unparalleled precision.
Unlike X-rays or CT scans, MRI technology does not use ionizing radiation, making it a safer option for patients.
The images generated by MRI scans provide excellent contrast between different soft tissues, enabling clinicians to identify and evaluate abnormalities with a high degree of accuracy.
Whether you're a researcher exploring new MRI techniques or a healthcare provider seeking to optimize your diagnostic protocols, PubCompare.ai can help you unlock the full potential of this powerful imaging modality.
Discover the best MRI protocols from the latest literature, preprints, and patents, and enhance your research outcomes with the power of advanced AI comparisons.

Most cited protocols related to «MRI Scans»

1
Automated MRI Brain Atlas Construction

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Publication 2010
Brain Microtubule-Associated Proteins MRI Scans Radionuclide Imaging
2
Mitigating Noise in Resting-State fMRI

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Publication 2013
Brain Carps Cerebral Ventricles derivatives fMRI Gray Matter Head MRI Scans Nervousness Plant Roots Protein Biosynthesis Reading Frames Tissues White Matter
3
Reliability of Knee Osteoarthritis Imaging Biomarkers

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Publication 2011
Biological Markers Cells Degenerative Arthritides Disease Progression Knee MRI Scans Public Domain
4
Amyloid PET Imaging Data Analysis Protocols
PiB and florbetapir image data were analyzed using 2 processing streams. The PET-template analysis method was described in a separate study (10 (link)). This method was applied to the raw and unsmoothed datasets. Briefly, image data were spatially normalized to standard atlas coordinates in Talairach space using statistical parametric mapping software (11 ). Mean tracer retention was calculated for 6 predefined target cortical regions of interest (medial orbital frontal, temporal, parietal, anterior cingulate, posterior cingulate, and precuneus) that resulted from a statistical contrast of AD patients and cognitively normal subjects (1 (link)).
The Freesurfer method for quantifying cortical Aβ was applied to the unsmoothed and smoothed datasets. This method was described in detail elsewhere (2 (link),12 (link)) and online (13 ). Structural 1.5-T or 3-T MRI scans (T1-weighted images) were used to define cortical regions of interest and the cerebellar reference region. In general, 2 structural MRI scans were acquired at each visit across several years of follow-up, with the result that several MR images were available for each subject. For processing the PiB images, we chose the T1 scans acquired concurrently with (or closest in time to) the first PiB scan; and for the florbetapir processing, we chose the T1 scans acquired concurrently with (or closest in time to) the florbetapir scan. Structural MR images were segmented and parceled into individual cortical regions with Freesurfer (version 4.5.0; surfer.nmr.mgh.harvard.edu/) and subsequently used to extract mean PiB and florbetapir cortical retention ratios from gray matter within lateral and medial frontal, anterior and posterior cingulate, lateral parietal, and lateral temporal regions.
To examine several reference regions, the unscaled cortical means for each analysis method were divided by mean retention in the following 3 reference regions: brain stem–pons, whole cerebellum (white and gray matter), and cerebellar gray matter, yielding 3 cortical retention ratios for each preprocessing method. Because Freesurfer creates a brain stem, but not pons, region as part of its automated processing stream, the brain stem was used for the Free-surfer processing analysis method and the pons was used for the PET-template processing method.
To summarize, for each of 3 PET sessions (2 PiB scans and 1 florbetapir scan), every subject had cortical retention ratios for 2 levels of processing and 2 analysis methods (raw and unsmoothed for the PET-template method and unsmoothed and smoothed for the Freesurfer method), using 3 reference regions (brain stem–pons, whole cerebellum, cerebellar gray matter), resulting in 36 mean cortical retention ratios per subject that were compared in subsequent statistical analyses.
Landau S.M., Breault C., Joshi A.D., Pontecorvo M., Mathis C.A., Jagust W.J, & Mintun M.A. (2012). Amyloid-β Imaging with Pittsburgh Compound B and Florbetapir: Comparing Radiotracers and Quantification Methods. Journal of nuclear medicine : official publication, Society of Nuclear Medicine, 54(1), 70-77.
Publication 2012
Brain Stem Cerebellar Gray Matter Cerebellum Cortex, Cerebral florbetapir Gray Matter Gyrus, Anterior Cingulate MRI Scans Patients Pons Posterior Cingulate Cortex Precuneus Radionuclide Imaging Retention (Psychology) Temporal Lobe
5
Multimodal Neuroimaging Dataset Comparison

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Publication 2015
MRI Scans

Most recents protocols related to «MRI Scans»

1
Automating Carotid Plaque Detection in MRI
For automatic segmentation of MRI scans to detect carotid plaque for stroke risk assessment, there is a need for a computer-aided autonomous framework to classify MRI scans automatically. Deep learning technology has recently permeated several areas of medical study and has taken center stage in modern science and technology (13 (link)). Deep learning technology can fully utilize vast amounts of data, automatically learn the features in the data, accurately and rapidly support clinicians in diagnosis, and increase medical efficiency (14 (link)). In this research, we proposed a deep learning framework based on transfer learning to detect carotid plaque from MRI scans for stroke risk assessment. We used YOLO V3, Mobile Net, and RCNN pre-trained models, fine-tuned them and adjusted hyperparameters according to our dataset. All experiments in this paper are conducted on Intel(R) Celeron(R) CPU N3150 @ 1.60 GHz. The operating system is Windows 64-bit, Python 3.6.6, TensorFlow deep Learning framework 1.8.0, and CUDA 10.1. The proposed framework to address the mentioned research problem is shown in Figure 1.
Chen Y.F., Chen Z.J., Lin Y.Y., Lin Z.Q., Chen C.N., Yang M.L., Zhang J.Y., Li Y.Z., Wang Y, & Huang Y.H. (2023). Stroke risk study based on deep learning-based magnetic resonance imaging carotid plaque automatic segmentation algorithm. Frontiers in Cardiovascular Medicine, 10, 1101765.
Publication 2023
Carotid Arteries Cerebrovascular Accident Dental Plaque Diagnosis Health Risk Assessment MRI Scans Python Transfer, Psychology
2
Cognitive Impairment in Type 2 Diabetes
Two hundred and ten individuals were willing to join this study (May 10, 2021, to July 1, 2022). The exclusion criteria for the two groups were as follows: type 1 diabetes mellitus, impaired fasting glucose or impaired glucose tolerance58 (link), hypertension, hypoglycemia (blood sugar levels < 3.9 mmol/L), hyperlipidemia, serious eye diseases (e.g., blindness), symptoms of neurological conditions (e.g., cerebral infarction or hemorrhage), history of neurological abnormality (e.g., Parkinson’s disease), severe head injuries or chronic head discomfort (e.g., migraine), BMI > 31 kg/m2, left- or mixed-handedness, substance (tobacco, alcohol, or psychoactive drug) abuse, taking medications that may affect cognition and memory within 6 months, specific abnormalities detected on conventional MRI scans or any other factors that may influence brain structure or function (e.g., extreme physical weakness, chronic infections, and other endocrine diseases). Patients with T2DM were diagnosed by two experienced endocrinologists following international clinical standards59 . MCI was evaluated via Mini-Mental State Examination (MMSE) and MoCA-B (21 ≤ MoCA-B score < 26, and MMSE score > 24 were diagnosed with MCI)60 ,61 (link).
Participants with brain tumors (n = 3), neuropsychiatric diseases (n = 4) (e.g., major depression or schizophrenia), or developmental disorders (n = 4) were excluded. Finally, 37 patients with T2DM-MCI, 93 patients with T2DM-NCI, and 69 NC were enrolled in this study. The source of patients with T2DM and NC corresponded with our previous study37 (link). This study was approved by the ethics committee of The First Affiliated Hospital of Guangzhou University of Chinese Medicine (ID: NO. JY [2020] 288). Written informed consent was obtained from all participants. In addition, the study was conducted following approved guidelines.
Wu J., Fang Y., Tan X., Kang S., Yue X., Rao Y., Huang H., Liu M., Qiu S, & Yap P.T. (2023). Detecting type 2 diabetes mellitus cognitive impairment using whole-brain functional connectivity. Scientific Reports, 13, 3940.
Publication 2023
Asthenia Blindness Blood Glucose Brain Brain Neoplasms Cerebral Infarction Chinese Chronic Infection Cognition Congenital Abnormality Craniocerebral Trauma Developmental Disabilities Diabetes Mellitus, Insulin-Dependent Drug Abuse Endocrine System Diseases Endocrinologists Ethanol Ethics Committees, Clinical Eye Disorders Glucose Head Hemorrhage High Blood Pressures Hyperlipidemia Hypoglycemia Major Depressive Disorder Memory Migraine Disorders Mini Mental State Examination MRI Scans Nervous System Abnormality Nervous System Disorder Patients Pharmaceutical Preparations Physical Examination Psychotropic Drugs Schizophrenia Tobacco Products
3
Longitudinal Evaluation of PH Progression
Baseline visit details (study and control groups characterizations, inclusion/exclusion criteria of the study) are presented in our previous article [5 (link)]. Patients were clinically followed-up on regular three-month basis, however the first structured (including PET-MRI, echo, 6MWT, biochemical analysis) follow-up visit (FU-1) was done after 24 months from the baseline visits (graphical presentation of the study Fig. 1). PET/MRI scans were done together with other tests during the same hospitalization, in the same patients’ hemodynamic state (Fig. 2). Occurrence of clinical end-points (CEP) was checked between baseline visits and FU-1 visits. CEP was defined as death or WHO class worsening and/or with hospitalisation due to pulmonary hypertension progression or right heart failure (as described previously) [5 (link)]. All CEP( +) patients eventually had PAH therapy escalation within 1 month since CEP. After FU-1 visits we observed study group for next 24 months for occurrence of CEP (defined the same as above), ending in second follow-up visit (FU-2). PET/MRI scans were not repeated after FU-1 visit (during FU-2 only WHO class, laboratory tests, 6MWT and RHC were done). The clinical follow-up lasted in total 48 months.

Graphical presentation of the study

Cardiac 18-fluorodeoxyglucose uptake in pulmonary hypertension patient before A initiation of specific therapy and after B 24 months of treatment

Therapy escalation was defined as initiation of prostacyclin (PGI) treatment (parenteral or oral) or adding second-line oral therapy according to ESC Guidelines [1 (link)]. Right heart catheterization was repeated at FU-1 and FU-2 visits in standard technique within median 6 [2 (link)–9 (link)] days of PET/MRI scans with a use of previously described protocol [5 (link)].
Kazimierczyk R., Szumowski P., Nekolla S.G., Malek L.A., Blaszczak P., Hladunski M., Sobkowicz B., Mysliwiec J, & Kaminski K.A. (2023). The impact of specific pulmonary arterial hypertension therapy on cardiac fluorodeoxyglucose distribution in PET/MRI hybrid imaging–follow-up study. EJNMMI Research, 13, 20.
Publication 2023
Catheterizations, Cardiac Disease Progression ECHO protocol Epoprostenol F18, Fluorodeoxyglucose Heart Failure Hemodynamics MRI Scans Parenteral Nutrition Patients Positron-Emission Tomography Pulmonary Hypertension Therapeutics
4
Comprehensive Ischemic Stroke Outcomes
The patient baseline characteristics, the anatomical structure of vascular access, medical history, procedure-related time points, stroke severity, recanalization devices, number of mechanical thrombectomy attempts, need for rescue therapy and 3-month follow-up data were collected. The degree of vessel occlusion before and after treatment was defined by the extended Thrombolysis in Cerebral Infarction (eTICI) classification, and a postoperative eTICI score 2c/3 was defined as successful recanalization of the vessel. The NIHSS score was used to determine the level of neurological severity (ranges from 0 to 42, with higher scores indicating a greater degree of severity), and improvements of at least 4 points on the NIHSS score within 24 h or at discharge than that of admission were considered short-term neurological improvement. The modified Rankin Scale (mRS) was used to assess neurological recovery at 90 days postoperative, and mRS score of 0 to 2 was defined as favorable neurological recovery. All patients were reexamined by head CT or MRI scan within 24 h after the operation to determine whether intracranial hemorrhage occurred, and symptomatic intracerebral hemorrhage (sICH) was defined as the presence of hemorrhage after the procedure, with worsening of clinical examination by ≥4 points on the NIHSS.
Fan H., Li Z., Li Y., Tan Y., Mao Z., Liu Q, & Zhu Y. (2023). Comparison of a direct aspiration first pass technique vs. stent retriever thrombectomy for the treatment of acute large vessel occlusion stroke in the anterior circulation with atrial fibrillation. Frontiers in Neurology, 14, 1138993.
Publication 2023
Aftercare Blood Vessel Cerebral Hemorrhage Cerebral Infarction Cerebrovascular Accident Dental Occlusion Fibrinolytic Agents Head Hemorrhage Intracranial Hemorrhage Medical Devices MRI Scans Patient Discharge Patients Physical Examination Therapeutics Thrombectomy
5
Cocaine Dependence Functional Connectivity
All study procedures were approved by the Institutional Review Board of Virginia Commonwealth University. CD and HC were recruited from Richmond, Virginia, via flyers, advertisements, and in-person recruitment at outpatient addiction treatment clinics (CD only). CD were excluded if they tested positive for any illicit drug other than cocaine or cannabis, but no restrictions regarding cocaine or cannabis use were imposed during recruitment. Written informed consent was obtained from all subjects. Subjects underwent screenings for medical, psychiatric, and substance use histories, and a physical examination. The Structured Clinical Interview for DSM-IV [(35 ); SCID-IV] was used to diagnose DSM-IV Cocaine Dependence (36 ). Inclusion criteria were DSM-IV diagnosed Cocaine Dependence (for CD) and age between 18 and 70 years. Exclusion criteria were history of schizophrenia, seizure disorder, major head trauma, any changes to psychoactive medications within the previous 30 days, or any other DSM-IV substance use disorder diagnosis. Additional HC exclusion criteria were any history of substance use disorder. Subject data was pooled from three separate studies – two studies in which delay discounting and MRI measures were obtained during a baseline period and one study in which the delay discounting and MRI measures were obtained two hours after administration of a placebo dose in a mirtazapine medication study (i.e., subjects had received either no mirtazapine dose or a single low mirtazapine dose 7 days prior to the measurement of delay discounting and MRI data used for this study). Participants were asked to refrain from tobacco use one hour before and caffeine consumption 3 hours before their MRI scan. Urine drug screens (UDS) and alcohol breath screens were obtained before their MRI scan on the day of the scan. 28 CD and 28 HC met the inclusion and exclusion criteria. Given that these two groups differed statistically in mean age and also in mean years of education attained, we performed a planned analysis after matching the two groups more closely for age and years of education. This more closely matched group analysis included 22 CD and 22 HC. We included an equal number of subjects in each group per the recommendations of the authors of the FSL software which we used for our functional connectivity analysis [(37 ), p. 67].
Woisard K., Steinberg J.L., Ma L., Zuniga E., Lennon M, & Moeller F.G. (2023). Executive control network resting state fMRI functional and effective connectivity and delay discounting in cocaine dependent subjects compared to healthy controls. Frontiers in Psychiatry, 14, 1117817.
Publication 2023
Addictive Behavior Caffeine Cannabis Cocaine Cocaine Dependence Craniocerebral Trauma Diagnosis Epilepsy Ethanol Ethics Committees, Research Illicit Drugs Mirtazapine MRI Scans Physical Examination Placebos Psychotropic Drugs Radionuclide Imaging Schizophrenia SCID Mice Substance Abuse Detection Substance Use Substance Use Disorders Urine

Top products related to «MRI Scans»

1
Tim trio by Siemens
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The Tim Trio is a versatile laboratory equipment that serves as a temperature-controlled magnetic stirrer. It features three independent stirring positions, allowing simultaneous operation of multiple samples. The device maintains a consistent temperature range to ensure accurate and reliable results during experimental procedures.
2
Discovery mr750 by GE Healthcare
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The Discovery MR750 is a magnetic resonance imaging (MRI) system developed by GE Healthcare. It is designed to provide high-quality imaging for a variety of clinical applications.
3
32 channel head coil by Siemens
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The 32-channel head coil is a key component in magnetic resonance imaging (MRI) systems. It is designed to acquire high-quality images of the human head, enabling detailed visualization and analysis of brain structure and function.
4
Ingenia by Philips
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The Philips Ingenia is a magnetic resonance imaging (MRI) system designed for diagnostic imaging. It provides high-quality images of the body's internal structures to aid in the detection and diagnosis of various medical conditions.
5
Magnetom skyra by Siemens
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The MAGNETOM Skyra is a magnetic resonance imaging (MRI) system developed by Siemens. It is designed to provide high-quality imaging for various medical applications. The MAGNETOM Skyra utilizes advanced technology to generate detailed images of the body's internal structures without the use of ionizing radiation.
6
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The MAGNETOM Prisma is a magnetic resonance imaging (MRI) system designed and manufactured by Siemens. It is a powerful diagnostic imaging device that uses strong magnetic fields and radio waves to generate detailed images of the human body. The MAGNETOM Prisma is capable of providing high-quality, high-resolution imaging data to support clinical decision-making and patient care.
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The Magnetom Verio is a magnetic resonance imaging (MRI) system produced by Siemens. It is designed to acquire high-quality images of the human body. The core function of the Magnetom Verio is to generate a strong magnetic field and radio waves, which interact with the hydrogen protons in the body to produce detailed images of internal structures and organs.
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More about "MRI Scans"

Magnetic Resonance Imaging (MRI) is a powerful diagnostic tool that utilizes strong magnetic fields and radio waves to produce detailed, high-contrast images of the body's internal structures.
Unlike ionizing radiation-based techniques like X-rays or CT scans, MRI is a non-invasive and safer imaging modality, making it a preferred choice for many healthcare applications.
MRI scans can be used to diagnose a wide range of medical conditions, from neurological disorders and brain abnormalities to musculoskeletal injuries and diseases.
The excellent soft tissue contrast provided by MRI allows clinicians to identify and evaluate pathologies with a high degree of accuracy, enabling more precise diagnoses and improved patient outcomes.
Whether you're a researcher exploring innovative MRI techniques or a healthcare provider seeking to optimize your diagnostic protocols, advanced MRI imaging systems like the Tim Trio, Discovery MR750, 32-channel head coil, Ingenia, MAGNETOM Skyra, Achieva, MAGNETOM Prisma, Prisma, Magnetom Verio, and Magnetom Tim Trio can help you unlock the full potential of this versatile imaging modality.
PubCompare.ai is a leading platform that can assist you in discovering the best MRI protocols from the latest literature, preprints, and patents, using advanced AI-powered comparisons.
By leveraging our platform, you can enhance your research outcomes and stay at the forefront of MRI technology.
Experience the future of MRI research and diagnostics with PubCompare.ai today!