Coronal proton density weighted magnetic resonance image of a 9 year old child's left ankle and foot

BMJ ◽  
2014 ◽  
Vol 349 (dec18 11) ◽  
pp. g7702-g7702
Author(s):  
O. Kayode ◽  
S. Perera
Keyword(s):  
Magnetic Resonance ◽  
Magnetic Resonance Image ◽  
Proton Density ◽  
Left Ankle ◽  
Ankle And Foot

Magnetic resonance image attributes of the bovine ovarian follicle antrum during development and regression

Reproduction ◽  
2000 ◽  
pp. 311-323 ◽  
Author(s):  
JL Hilton ◽  
GE Sarty ◽  
GP Adams ◽  
RA Pierson
Keyword(s):  
Magnetic Resonance ◽  
Relaxation Rate ◽  
Follicular Fluid ◽  
Magnetic Resonance Image ◽  
Follicular Development ◽  
Physiological Status ◽  
Proton Density ◽  
Dominant Follicle ◽  
Rate Heterogeneity ◽  
Pixel Value

The magnetic resonance images and maps of bovine ovaries acquired at defined phases of follicular development and regression were studied to determine whether magnetic resonance image attributes of the follicular antrum reflect the physiological status of dominant and subordinate ovarian follicles. Ovariectomies were performed at day 3 of wave one, day 6 of wave one, day 1 of wave two and at >/= day 17 after ovulation. The timings of ovariectomies were selected to acquire growing, early static, late static and regressing follicles of the first wave and preovulatory follicles of the ovulatory wave. Pre-selection and subordinate follicles were also available for analysis. Serum samples were taken on the day of ovariectomy and follicular fluid samples were taken after imaging. Numerical pixel value and pixel heterogeneity in a spot representing approximately 95% of the follicular antrum were quantified in T(1)- and T(2)-weighted images. T(1) and T(2) relaxation rates (T(1) and T(2)), proton density, apparent diffusion coefficients and their heterogeneities were determined from the computed magnetic resonance maps. The antra of early atretic dominant follicles showed higher T(2)-weighted mean pixel value (P < 0.008) and heterogeneity (P < 0. 01) and lower T(2) heterogeneity (P < 0.008) than growing follicles. Subordinate follicles in the presence of a preovulatory dominant follicle had higher T(1), T(1) heterogeneity, proton density, proton density heterogeneity, and lower mean pixel value in T(1)-weighted images than subordinate follicles of the anovulatory wave (P < 0.04). T(1) relaxation rate heterogeneity and proton density heterogeneity were positively correlated with follicular fluid oestradiol concentration (r = 0.4 and 0.3; P < 0.04). T(2) relaxation rate heterogeneity was positively correlated with follicular fluid progesterone concentration (r = 0.4; P < 0.008). Quantitative differences in magnetic resonance image attributes of the antrum observed among phases of follicular development and regression coincided with changes in the ability of the dominant follicle to produce steroid hormones and ovulate, and thus were indicative of physiological status and follicular health.

scholarly journals To Analyse the Effect of Relaxation Type on Magnetic Resonance Image Compression Using Compressive Sensing

2021 ◽  
Vol 17 (04) ◽  
pp. 21
Author(s):  
Vivek Upadhyaya ◽  
Mohammad Salim
Keyword(s):  
Magnetic Resonance ◽  
Compressive Sensing ◽  
Magnetic Resonance Image ◽  
Similarity Index ◽  
Structural Similarity ◽  
Scanning Speed ◽  
Proton Density ◽  
Body Parts ◽  
Mr Image ◽  
Reconstruction Process

<span>Medical Imaging and scanning technologies are used to provide better resolution of body and tissues. To achieve a better quality Magnetic Resonance (MR) image with a minimum duration of processing time is a tedious task. So our purpose in this paper is to find out a solution that can minimize the reconstruction time of an MRI signal. </span><span>Compressive sensing can be used to accelerate Magnetic Resonance Image (MRI) acquisition by acquiring fewer data through the under-sampling of k-space, so it can be used to minimize the time. But according to the relaxation time, we can further classify the MRI signal into T1, T2, and Proton Density (PD) weighted images. These weighted images represent different signal intensities for different types of tissues and body parts. It also affects the reconstruction process conducted by using the Compressive Sensing Approach. This study is based on finding out the effect of T1, T2, and Proton Density (PD) weighted images on the reconstruction process as well as various image quality parameters like MSE, PSNR, &amp; SSIM also calculated to analyze this effect. Meanwhile, we can analyze how many samples are enough to reconstruct the MR image so the problem associated with time and scanning speed can be reduced up to an extent. In this paper, we got the Structural Similarity Index Measure (SSIM) value up to 0.89 &amp; PSNR value 37.83451 dB at an 85 % compression ratio for the T2 weighted image. </span>

Magnetic resonance image of the left ankle

BMJ ◽  
2010 ◽  
Vol 341 (aug18 1) ◽  
pp. c4409-c4409
Author(s):  
A. Nair
Keyword(s):  
Magnetic Resonance ◽  
Magnetic Resonance Image ◽  
Left Ankle

Presurgical Evaluation in Magnetic Resonance Image-Negative Frontal Lobe Epilepsy

2014 ◽  
Vol 45 (S 01) ◽  
Author(s):  
I. Borggräfe ◽  
C. Vollmar ◽  
A. Lösch ◽  
B. Ertl-Wagner ◽  
L. Gerstl ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Frontal Lobe ◽  
Magnetic Resonance Image ◽  
Frontal Lobe Epilepsy ◽  
Presurgical Evaluation

Reliability of Hammersmith Examination in Prediction of Neurological Outcomes (REHAPENO) - Research Protocol (Preprint)

2018 ◽  
Author(s):  
Mohamed Fleifel ◽  
Rawya Abdelghani ◽  
Mohamed Ameen
Keyword(s):  
Magnetic Resonance ◽  
Magnetic Resonance Image ◽  
Current Practice ◽  
Surgical Intervention ◽  
Brain Mri ◽  
Developmental Outcomes ◽  
Research Protocol ◽  
Neurological Outcomes ◽  
Mri Brain ◽  
Brain Ultrasound

BACKGROUND Background: Studying the neurological developmental outcomes and comparing correlations with MRI (Magnetic resonance image) versus the Hammersmith Infant Neurological Examination (HINE) OBJECTIVE Objective: To investigate the non-inferiority of MRI to HINE in infant developmental outcomes METHODS Settings: Hospital settings including pediatrics and neonatal care units Intervention: No medical or surgical intervention is planned, only correlation and extra analyses would take place to standardize the current practice Measurements: HINE, Brain MRI, Brain Ultrasound and developmental outcomes after 12 months RESULTS Results: The observations collected and correlations measured to figure out the reliability of both HINE and MRI in order to figure to what extent can we rely on HINE alone in expecting the developmental outcomes CONCLUSIONS The more reliability would expressed by HINE assessment the accurate expectation of developmental in preterm infants CLINICALTRIAL https://clinicaltrials.gov/ct2/show/NCT03580252

Deep learning in magnetic resonance image reconstruction

2021 ◽  
Author(s):  
Shekhar S Chandra ◽  
Marlon Bran Lorenzana ◽  
Xinwen Liu ◽  
Siyu Liu ◽  
Steffen Bollmann ◽  
...  
Keyword(s):  
Deep Learning ◽  
Magnetic Resonance ◽  
Image Reconstruction ◽  
Magnetic Resonance Image
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