scholarly journals Application of Magnetic Resonance Image Intelligent Data Acquisition in Ovarian Cancer (Preprint)

10.2196/18960 ◽  
2020 ◽  
Author(s):  
Tingting Liang ◽  
Yanlei Dong ◽  
Xinrui Zhao ◽  
Lu Wang ◽  
Hui Xu ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Magnetic Resonance ◽  
Data Acquisition ◽  
Magnetic Resonance Image ◽  
Intelligent Data Acquisition

scholarly journals Application of Magnetic Resonance Image Intelligent Data Acquisition in Ovarian Cancer (Preprint)

2020 ◽  
Author(s):  
Tingting Liang ◽  
Yanlei Dong ◽  
Xinrui Zhao ◽  
Lu Wang ◽  
Hui Xu ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Magnetic Resonance ◽  
Epithelial Cells ◽  
Data Acquisition ◽  
Cell Lines ◽  
P53 Protein ◽  
Control Group ◽  
Ovarian Epithelial Cells ◽  
Intelligent Data Acquisition ◽  
Experimental Group

UNSTRUCTURED As a diagnostic method with no radiation, high resolution of soft tissue, and different imaging methods, Magnetic Resonance Imaging (MRI) intelligent data acquisition is more and more widely used in the examination of abdominal, pelvic, and other organ lesions. In order to study the diagnostic effect of multi-mode magnetic resonance intelligent data acquisition on ovarian cancer and the ovarian cancer model modified based on p53-/-+Myc+ASAP1 gene, NSG mice were selected as experimental subjects in this study. 293FT cell lines packaging p53, Myc, and ASAP1(ArfGAP with SH3 domain, Ankyrin repeat and PH domain 1) recombinant lentivirus were inoculated into mouse ovarian epithelial cells to construct mouse ovarian epithelial cell tumor cell lines and their performance was analyzed. According to the different injection cell lines, they were divided into the experimental group and the control group. Tumor samples were collected and the mice were analyzed using immunofluorescence staining and MRI. The results showed that, in the detection of protein expression in genetically modified cell lines, for p53-/-+Myc+ASAP1 fully modified cell lines, the high expression of ASAP1 and Myc functional proteins was detected after the lentivirus containing p53-/-, ASAP1, and Myc were introduced into mouse ovarian epithelial cells, while the expression of p53 protein decreased significantly; after inoculation into mice, it was found that the expression of ASAP1 protein and Myc protein in the experimental group was significantly higher than that in the control group, while the expression of p53 protein in the experimental group was significantly lower than that in the control group, with significant statistical difference; further MRI diagnosis of two groups of mice showed that the ADC (Apparent dispersion coefficient) value of the experimental group was significantly higher than the control group, there were statistically significant differences. Therefore, it was found that p53 gene expression was down-regulated and Myc and ASAPl genes were overexpressed in the tumor tissues and tumor cells formed, and tumor formation differences between the two groups of mice could be obviously found after MRI intelligent data acquisition, which provided experimental basis for early diagnosis of breast cancer in the later clinical stage.

scholarly journals Compressive Sensing Magnetic Resonance Image Reconstruction and Denoising using Convolutional Neural Network

2022 ◽  
Vol 2161 (1) ◽  
pp. 012036
Author(s):  
Ram Singh ◽  
Lakhwinder Kaur
Keyword(s):  
Magnetic Resonance ◽  
Image Reconstruction ◽  
Image Quality ◽  
Data Acquisition ◽  
Compressive Sensing ◽  
Magnetic Resonance Image ◽  
Motion Artifacts ◽  
Acquisition Time ◽  
High Quality ◽  
Mr Image

Abstract Restoration of high-quality brain Magnetic Resonance Image (MRI) from the sparse under-sampled complex k-space signal is a widely studied ill-posed inverse transform problem. A deep learning-based data-adaptive and data-driven convolutional technique has been proposed for high-quality MRI recovery from its under-sampled complex domain k-space signal. The uniform subsampling process is very slow in phase-encoding to generate high-resolution images. The longer scan times degrade the perceptual image quality. Various factors contribute to image degradation during data acquisition such as the inception of body motion artifacts, the thermal energy effects of the body, and random noise artifacts due to voltage fluctuations. Keeping in view the patient’s critical condition and comfort, longer scan times are not preferred in practice. To reduce the image acquisition time, noise levels, and motion artifacts in the MR images, Compressive Sensing (CS) provides an accelerated way to reconstructs the high-quality MR image from very limited signal measurements acquired much below the Nyquist rate. However, such data acquisition strategies require advanced computer algorithms for the reconstruction of high-quality MRI from the undersampled MRI data. An improved CNN-based MRI reconstructed algorithm has been presented in this paper which shows better performance to reconstruct high-quality MRI than similar other MR image reconstruction algorithms. The performance of the proposed algorithm is measured by image quality checking tools such as normalized-MSE, PSNR, and SSIM.

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

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.

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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