Magnetic Resonance Imaging as a Clinical Tool

Breast MRI ◽  
2005 ◽  
pp. 256-265 ◽  
Author(s):  
D. David Dershaw
2018 ◽  
Vol 31 (4) ◽  
pp. 362-371 ◽  
Author(s):  
Ravi Datar ◽  
Asuri Narayan Prasad ◽  
Keng Yeow Tay ◽  
Charles Anthony Rupar ◽  
Pavlo Ohorodnyk ◽  
...  

Background White matter abnormalities (WMAs) pose a diagnostic challenge when trying to establish etiologic diagnoses. During childhood and adult years, genetic disorders, metabolic disorders and acquired conditions are included in differential diagnoses. To assist clinicians and radiologists, a structured algorithm using cranial magnetic resonance imaging (MRI) has been recommended to aid in establishing working diagnoses that facilitate appropriate biochemical and genetic investigations. This retrospective pilot study investigated the validity and diagnostic utility of this algorithm when applied to white matter signal abnormalities (WMSAs) reported on imaging studies of patients seen in our clinics. Methods The MRI algorithm was applied to 31 patients selected from patients attending the neurometabolic/neurogenetic/metabolic/neurology clinics at a tertiary care hospital. These patients varied in age from 5 months to 79 years old, and were reported to have WMSAs on cranial MRI scans. Twenty-one patients had confirmed WMA diagnoses and 10 patients had non-specific WMA diagnoses (etiology unknown). Two radiologists, blinded to confirmed diagnoses, used clinical abstracts and the WMSAs present on patient MRI scans to classify possible WMA diagnoses utilizing the algorithm. Results The MRI algorithm displayed a sensitivity of 100%, a specificity of 30.0% and a positive predicted value of 74.1%. Cohen’s kappa statistic for inter-radiologist agreement was 0.733, suggesting “good” agreement between radiologists. Conclusions Although a high diagnostic utility was not observed, results suggest that this MRI algorithm has promise as a clinical tool for clinicians and radiologists. We discuss the benefits and limitations of this approach.


2005 ◽  
Vol 46 (6) ◽  
pp. 599-609 ◽  
Author(s):  
D. van Westen ◽  
G. Skagerberg ◽  
J. Olsrud ◽  
P. Fransson ◽  
E.-M. Larsson

Purpose: To investigate the potential of functional magnetic resonance imaging (fMRI) at 3T as a clinical tool in the preoperative evaluation of patients with intracranial tumors. High magnetic field strength such as 3T is of benefit for fMRI because signal-to-noise ratio and sensitivity to susceptibility changes are field-strength-dependent. Material and Methods: Twenty patients with tumors close to eloquent sensorimotor or language areas were studied. Motor, sensory, and two language paradigms (word generation, rhyming) were used; their effectiveness was determined as the percentage of patients in whom the functional area of interest was activated. Activation maps were calculated and their quality rated as high, adequate, or insufficient. The influence of fMRI on the neurosurgical decision regarding operability, surgical approach, and extent of the resection, was assessed. Results: Paradigm effectiveness was 90% for motor and 95% for sensory stimulation, and varied from 79% to 95% for word generation and rhyming in combination. Ninety percent of the activation maps held high or adequate quality. fMRI proved useful: in the decision to operate (9 patients), in the surgical approach (13 patients), and in extent of the resection (12 patients). Conclusion: fMRI at 3T is a clinically applicable tool in the work-up of patients with intracranial tumors.


2019 ◽  
Vol 2 (3) ◽  
pp. 257-264 ◽  
Author(s):  
Matthew Truong ◽  
Janet E. Baack Kukreja ◽  
Soroush Rais-Bahrami ◽  
Nimrod S. Barashi ◽  
Bokai Wang ◽  
...  

Author(s):  
Padmani S. Judape ◽  
Pragati Patil ◽  
Gajanan Patle

Brain tumor detection and segmentation is one in every of the foremost difficult and time overwhelming task in medical image process. Magnetic resonance imaging (MRI) may be a medical technique, in the main utilized by the radiotherapist for visualization of internal structure of the body with none surgery. Magnetic resonance imaging provides plentiful info regarding the human soft tissue that helps within the designation of neoplasm (brain tumor). Correct segmentation of MRI image is very important for the designation of brain tumor by laptop motor-assisted clinical tool. When acceptable segmentation of brain man pictures, growth is assessed to malignant and benign, that may be a troublesome task because of complexness and variation in growth tissue characteristics like its form, size, grey level intensities and site. Taking in to account the said challenges, this analysis is concentrated towards highlight the strength and limitations of earlier projected classification techniques mentioned within the up to date literature. Besides summarizing the literature, the paper additionally provides an important analysis of the surveyed literature that reveals new sides of analysis.


2020 ◽  
Vol 11 ◽  
Author(s):  
John Virostko

Magnetic resonance imaging (MRI) has the potential to improve our understanding of diabetes and improve both diagnosis and monitoring of the disease. Although the spatial resolution of MRI is insufficient to directly image the endocrine pancreas in people, the increasing awareness that the exocrine pancreas is also involved in diabetes pathogenesis has spurred new MRI applications. These techniques build upon studies of exocrine pancreatic diseases, for which MRI has already developed into a routine clinical tool for diagnosis and monitoring of pancreatic cancer and pancreatitis. By adjusting the imaging contrast and carefully controlling image acquisition and processing, MRI can quantify a variety of tissue pathologies. This review introduces a number of quantitative MRI techniques that have been applied to study the diabetic pancreas, summarizes progress in validating and standardizing each technique, and discusses the need for image analyses that account for spatial heterogeneity in the pancreas.


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