scholarly journals Perfusion Magnetic Resonance Imaging to Assess Brain Tumor Responses to New Therapies

1998 ◽  
Vol 5 (2) ◽  
pp. 115-123 ◽  
Author(s):  
Michael H. Lev ◽  
Fred Hochberg
Keyword(s):  
Magnetic Resonance Imaging ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Imaging Techniques ◽  
Perfusion Mri ◽  
Perfusion Magnetic Resonance Imaging ◽  
Normal Brain ◽  
Resonance Imaging ◽  
New Therapies ◽  
Magnetic Resonance Imaging Mri

Background: Although magnetic resonance imaging (MRI) is effective in detecting the location of intracranial tumors, new imaging techniques have been studied that may enhance the specificity for the prediction of histologic grade of tumor and for the distinction between recurrence and tumor necrosis associated with cancer therapy. Methods: The authors review their experience and that of others on the use of perfusion magnetic resonance imaging to evaluate responses of brain tumors to new therapies. Results: Functional imaging techniques that can distinguish tumor from normal brain tissue using physiological parameters. These new approaches provide maps of tumor perfusion to monitor the effects of novel compounds that restrict tumor angiogenesis. Conclusions: Perfusion MRI not only may be as effective as radionuclide-based techniques in sensitivity and specificity in assessing brain tumor responses to new therapies, but also may offer higher resolution and convenient co-registration with conventional MRI, as well as time- and cost-effectiveness. Further study is needed to determine the role of perfusion MRI in assessing brain tumor responses to new therapies.

Functional Information on Meningiomas Through Perfusion Magnetic Resonance Imaging

2009 ◽  
Vol 4 (1) ◽  
pp. 88
Author(s):  
Hao Zhang ◽  
Guixiang Zhang ◽  
Matthijs Oudkerk ◽  
◽  
◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Imaging Techniques ◽  
Perfusion Mri ◽  
Perfusion Magnetic Resonance Imaging ◽  
Dynamic Susceptibility ◽  
Resonance Imaging ◽  
Dynamic Susceptibility Contrast ◽  
Dsc Mri ◽  
Susceptibility Contrast

This article focuses on the use of perfusion magnetic resonance imaging (MRI), and in particular dynamic susceptibility contrast-enhanced MRI (DSC-MRI), to assess haemodynamics in meningiomas. We first introduce the basic principles of DSC-MRI and the most popular imaging techniques and perfusion parameters for data analysis of DSC-MRI. We then review the blood supply characteristics of meningiomas and how perfusion MRI is applied in meningiomas to help the subtyping of different meningiomas and to differentiate between benign and malignant meningiomas. Our first-hand experiences are also included. We conclude that DSC perfusion MRI can provide critical information on the vascularity of meningiomas that is not available with conventional MRI. DSC perfusion MRI measurements are helpful in the pre-operative subtyping and grading of meningiomas.

Functional Information on Meningiomas Through Perfusion Magnetic Resonance Imaging

US Neurology ◽  
2009 ◽  
Vol 05 (01) ◽  
pp. 78
Author(s):  
Hao Zhang ◽  
Guixiang Zhang ◽  
Matthijs Oudkerk ◽  
◽  
◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Imaging Techniques ◽  
Perfusion Mri ◽  
Perfusion Magnetic Resonance Imaging ◽  
Dynamic Susceptibility ◽  
Resonance Imaging ◽  
Dynamic Susceptibility Contrast ◽  
Dsc Mri ◽  
Susceptibility Contrast

This article focuses on the use of perfusion magnetic resonance imaging (MRI), and in particular dynamic susceptibility contrast-enhanced MRI (DSCMRI), to assess hemodynamics in meningiomas. We first introduce the basic principles of DSC-MRI and the most popular imaging techniques and perfusion parameters for data analysis of DSC-MRI. We then review the blood supply characteristics of meningiomas and how perfusion MRI is applied in meningiomas to help the subtyping of different meningiomas and to differentiate between benign and malignant meningiomas. Our firsthand experiences are also included. We conclude that DSC perfusion MRI can provide critical information on the vascularity of meningiomas that is not available with conventional MRI. DSC perfusion MRI measurements are helpful in the pre-operative subtyping and grading of meningiomas.

scholarly journals Reasons for discordances between ultrasonography and magnetic resonance imaging in the evaluation of the ankle, hindfoot and heel of the patients with rheumatoid arthritis

2019 ◽  
Vol 21 (4) ◽  
pp. 405 ◽  
Author(s):  
Oana Șerban ◽  
Daniela Fodor ◽  
Iulia Papp ◽  
Mihaela Cosmina Micu ◽  
Dan Gabriel Duma ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Disease Activity ◽  
Activity Index ◽  
Imaging Techniques ◽  
Disease Activity Index ◽  
Resonance Imaging ◽  
Magnetic Resonance Imaging Mri ◽  
Definition Of

Aim: To compare the ultrasonography (US) performance with magnetic resonance imaging (MRI) in identifying pathology in ankles, hindfeet and heels of rheumatoid arthritis (RA) patients and to evaluate the reasons for discordances between the two imaging methods.Material and methods: RA patients were enrolled and evaluated using the Clinical Disease Activity Index (CDAI) and the Disease Activity Score 28 with C-reactive Protein (DAS28-CRP). The ankle (tibiotalar joint, tendons), hindfoot (talonavicular, subtalar joints) and heel of the most symptomatic or dominant foot (for the asymptomatic patient)were evaluated by two pairs of examiners using US and contrast-enhanced MRI.Results: Totally, 105 joints, 245 tendons and 35 heels in 35 patients [mean age 59.2±11.25 years old, median disease duration 36 (16.5-114), mean CDAI 19.87±12.7] were evaluated. The interobserver agreements between the two sonographers, and the two radiologists were good and very good (k=0.624-0.940). The overall agreement between US and MRI was very good for subcalcaneal panniculitis (k=0.928, p<0.001), moderate for synovitis (k=0.463, p<0.001) and tenosynovitis (k=0.514, p<0.001), fair for osteophytes (k=0.260, p=0.004), and poor for erosions (k=0.063, p=0.308) and heel’s structures. MRI found more erosions, synovitis, osteophytes,tenosynovitis and retrocalcaneal bursitis, but US found more enthesophytes and plantar fasciitis. Many of the discordancesbetween the two imaging techniques have explanations related to the technique itself or definition of the pathologic findings.Conclusions: US is comparable to MRI for the evaluation of ankle, hindfoot and heel in RA patients and discordances in theinterpretation of the pathological findings/normal structures must be carefully analyzed.

scholarly journals Magnetic resonance imaging of salivary glands

2019 ◽  
Author(s):  
Danielle Ayumi Nishimura ◽  
Ana Luiza Esteves Carneiro ◽  
Kaisermann Costa ◽  
Wladimir Gushiken de Campos ◽  
Jefferson Xavier de Oliveira ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Salivary Glands ◽  
Imaging Techniques ◽  
Neck Region ◽  
Evaluation Instrument ◽  
Resonance Imaging ◽  
Tissue Contrast ◽  
Excellent Method ◽  
Magnetic Resonance Imaging Mri

Salivary glands tumors account for 2-5% of tumors in the head and neck region, possibly being benign or malignant. Magnetic resonance imaging (MRI) presents high soft tissue contrast resolution, thus being an excellent method for salivary gland analysis. The objective of this literature review is to analyze MRI as an evaluation instrument for the diagnosis of salivary glands lesions. Compared to other imaging techniques, MRI can better evaluate the relationship between adjacent anatomical structures, presenting greater sensitivity and specificity.

scholarly journals Brain Tumor Segmentation and Classification using Multiple Feature Extraction and Convolutional Neural Networks

2021 ◽  
Vol 10 (6) ◽  
pp. 23-27
Author(s):  
Tasmiya Tazeen ◽  
◽  
Mrinal Sarvagya ◽  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Feature Extraction ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Early Detection ◽  
Tumor Segmentation ◽  
Resonance Imaging ◽  
Brain Tumor Segmentation ◽  
Segmentation Accuracy ◽  
Magnetic Resonance Imaging Mri

Intracranial tumors are a type of cancer that grows spontaneously inside the skull. Brain tumor is the cause for one in four deaths. Hence early detection of the tumor is important. For this aim, a variety of segmentation techniques are available. The fundamental disadvantage of present approaches is their low segmentation accuracy. With the help of magnetic resonance imaging (MRI), a preventive medical step of early detection and evaluation of brain tumor is done. Magnetic resonance imaging (MRI) offers detailed information on human delicate tissue, which aids in the diagnosis of a brain tumor. The proposed method in this paper is Brain Tumour Detection and Classification based on Ensembled Feature extraction and classification using CNN.

scholarly journals Magnetic Resonance Imaging of Rectal Cancer

2019 ◽  
Vol 02 (01) ◽  
pp. 018-032
Author(s):  
Ekta Maheshwari ◽  
Gitanjali Bajaj ◽  
Kedar Jambhekar ◽  
Tarun Pandey ◽  
Roopa Ram
Keyword(s):  
Magnetic Resonance Imaging ◽  
Rectal Cancer ◽  
Magnetic Resonance ◽  
High Resolution ◽  
Imaging Techniques ◽  
Postoperative Recurrence ◽  
Response To Therapy ◽  
Resonance Imaging ◽  
Primary Rectal Cancer ◽  
Magnetic Resonance Imaging Mri

AbstractHigh-resolution magnetic resonance imaging (MRI) plays a pivotal role in the pretreatment assessment of primary rectal cancer. The success of this technique depends on obtaining good-quality high-resolution T2-weighted images of the primary tumor, orthogonal to rectal lumen. The goal of magnetic resonance staging is to identify patients who will benefit from neoadjuvant therapy prior to surgery to minimize postoperative recurrence and planning of optimal surgical approach. MRI also facilitates optimal identification of important prognostic factors, which improves both treatment selection and posttreatment follow-up. The objective of this article is to review the existing literature and provide a concise update on various aspects of rectal cancer imaging, discuss the current role of advanced imaging techniques such as diffusion-weighted and perfusion imaging in the evaluation of rectal cancer, and to assess response to therapy.

scholarly journals Clustering of Brain Tumors in Brain MRI Images based on Extraction of Textural and Statistical Features

2020 ◽  
Vol 16 (12) ◽  
pp. 116
Author(s):  
Hamed Samadi Ghoushchi ◽  
Yaghoub Pourasad
Keyword(s):  
Magnetic Resonance Imaging ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Imaging Techniques ◽  
Brain Mri ◽  
Support Vector ◽  
Statistical Features ◽  
Resonance Imaging ◽  
The Brain

<p>The purpose of this article is to investigate techniques for classifying tumor grade from magnetic resonance imaging (MRI). This requires early diagnosis of the brain tumor and its grade. Magnetic resonance imaging may show a clear tumor in the brain, but doctors need to measure the tumor in order to treat more or to advance treatment. For this purpose, digital imaging techniques along with machine learning can help to quickly identify tumors and also treatments and types of surgery. These combined techniques in understanding medical images for researchers are an important tool to increase the accuracy of diagnosis. In this paper, classification methods for MRI images of tumors of the human brain are performed to review the astrocytoma-containing glands. Methods used to classify brain tumors, including preprocessing, screening, tissue extraction, and statistical features of the tumor using two types of T<sub>1</sub>W and Flair brain MRI images and also the method of dimensionality reduction of extracted features and how to train them in classification are also explained. Determine the tumor area using three classification of Fuzzy Logic <em>C</em><em>-</em><em>Means</em><em> </em>Clustering (FCM), Probabilistic Neural Networks (PNN) and Support Vector Machines (SVM). In this paper, simulated and real MRI images are used. The results obtained from the proposed methods in this paper are compared with the reference results and the results show that the proposed approach can increase the reliability of brain tumor diagnosis.</p>

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