scholarly journals Image Reconstruction in Surgical Field Using Deep Learning

2021 ◽  
Vol 11 (2) ◽  
pp. 1489-1496
Author(s):  
Divya S
Keyword(s):  
Magnetic Resonance ◽  
Image Reconstruction ◽  
Mean Squared Error ◽  
Back Propagation ◽  
Magnetic Resonance Images ◽  
Image Features ◽  
Squared Error Loss Function ◽  
Positron Emission ◽  
Magnetic Resonance Imaging Mri ◽  
Fully Connected

The field of medical image reconstruction helps to improve image quality by manipulating image features and artefact with Filtered-Back Propagation for X-ray Computer Tomography (CT), Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). This project focuses on detection of tumour cells using Radiomics application that aims to extract extensive quantitative features from magnetic resonance images. In this paper image discretization models and image interpolation techniques are used to segment the MR images and train them for Image Reconstruction. The image based gray level segmentation is carried out for required feature extraction to improve the clustering analysis for segmentation. Convolution Neural Network is used for image classification and recognition because of its high accuracy. The CNN follows a hierarchical model which works on building a network and finally gives out a fully-connected layer where all the neurons are connected to each other and the output is processed. The JPEG approach is a commonly used type of compression of lossy images that centres on the Discrete Cosine Transform. By splitting images into components of varying frequencies, the DCT functions. Finally the output from the Radiomics application is compared with the existing methodology for determining the Mean Squared Error - Loss Function to ensure the image compression quality.

scholarly journals Semantic Dementia Diagnosed by F-18 FDG PET/MRI: Co-registered Images

2013 ◽  
Vol 3 ◽  
pp. 35 ◽  
Author(s):  
Prashant Jolepalem ◽  
Dafang Wu
Keyword(s):  
Magnetic Resonance ◽  
Functional Data ◽  
Fdg Pet ◽  
Sudden Change ◽  
Sudden Onset ◽  
Magnetic Resonance Images ◽  
Semantic Dementia ◽  
Positron Emission ◽  
Magnetic Resonance Imaging Mri ◽  
Fluorine 18 Fluorodeoxyglucose

We report a case of a 61-year-old male who presented with a sudden change in mental status. From a psychiatric standpoint, his symptoms were consistent with a bipolar disorder. A neurology consult raised suspicion for vascular dementia, given the sudden onset of symptoms; however, the magnetic resonance angiography (MRA) was unremarkable. The magnetic resonance imaging (MRI) had findings that were suggestive of both vascular and frontotemporal lobe dementia based on parenchymal atrophy and a lacunar infarct near the thalamus. However, by co-registering the magnetic resonance images with a subsequent fluorine-18 Fluorodeoxyglucose positron emission tomography (F-18 FDG PET), and combining the functional data with the anatomic appearance, the diagnosis was narrowed to semantic dementia, which is one of the lesser known subtypes of frontotemporal lobe dementia (FTD).

scholarly journals Minimal Linear Networks for Magnetic Resonance Image Reconstruction

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Gilad Liberman ◽  
Benedikt A. Poser
Keyword(s):  
Deep Learning ◽  
Magnetic Resonance ◽  
Image Reconstruction ◽  
Simulated Data ◽  
Linear Networks ◽  
Learning Framework ◽  
Scan Time ◽  
Learning Techniques ◽  
Ill Posed ◽  
Magnetic Resonance Imaging Mri

AbstractModern sequences for Magnetic Resonance Imaging (MRI) trade off scan time with computational challenges, resulting in ill-posed inverse problems and the requirement to account for more elaborated signal models. Various deep learning techniques have shown potential for image reconstruction from reduced data, outperforming compressed sensing, dictionary learning and other advanced techniques based on regularization, by characterization of the image manifold. In this work we suggest a framework for reducing a “neural” network to the bare minimum required by the MR physics, reducing the network depth and removing all non-linearities. The networks performed well both on benchmark simulated data and on arterial spin labeling perfusion imaging, showing clear images while preserving sensitivity to the minute signal changes. The results indicate that the deep learning framework plays a major role in MR image reconstruction, and suggest a concrete approach for probing into the contribution of additional elements.

Magnetic resonance imaging and computerized tomography scanning of herpes simplex encephalitis

1987 ◽  
Vol 67 (4) ◽  
pp. 592-594 ◽  
Author(s):  
Eric W. Neils ◽  
Robert Lukin ◽  
Thomas A. Tomsick ◽  
John M. Tew
Keyword(s):  
Magnetic Resonance Imaging ◽  
Herpes Simplex ◽  
Magnetic Resonance ◽  
Computerized Tomography ◽  
Herpes Simplex Encephalitis ◽  
Ct Scanning ◽  
Magnetic Resonance Images ◽  
Resonance Imaging ◽  
Content Type ◽  
Magnetic Resonance Imaging Mri

✓ The authors present two cases of herpes simplex encephalitis (HSE) in which computerized tomography (CT) scanning and magnetic resonance imaging (MRI) were performed. They also review the literature on the use of these imaging modalities in cases of HSE. The striking changes noted in these cases on T2-weighted magnetic resonance images in comparison to the CT findings suggest that MRI will help speed recognition of nonhemorrhagic HSE abnormalities.

Imaging-based stereotaxic serial biopsies in untreated intracranial glial neoplasms

1987 ◽  
Vol 66 (6) ◽  
pp. 865-874 ◽  
Author(s):  
Patrick J. Kelly ◽  
Catherine Daumas-Duport ◽  
David B. Kispert ◽  
Bruce A. Kall ◽  
Bernd W. Scheithauer ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Contrast Enhancement ◽  
Tumor Tissue ◽  
Magnetic Resonance Images ◽  
Low Grade ◽  
Content Type ◽  
Mri Scans ◽  
Isolated Tumor Cell ◽  
Magnetic Resonance Imaging Mri ◽  
Ct And Mri

✓ Forty patients with previously untreated intracranial glial neoplasms underwent stereotaxic serial biopsies assisted by computerized tomography (CT) and magnetic resonance imaging (MRI). Tumor volumes defined by computer reconstruction of contrast enhancement and low-attenuation boundaries on CT and T1 and T2 prolongation on MRI revealed that tumor volumes defined by T2-weighted MRI scans were larger than those defined by low-attenuation or contrast enhancement on CT scans. Histological analysis of 195 biopsy specimens obtained from various locations within the volumes defined by CT and MRI revealed that: 1) contrast enhancement most often corresponded to tumor tissue without intervening parenchyma; 2) hypodensity corresponded to parenchyma infiltrated by isolated tumor cells or in some instances to tumor tissue in low-grade gliomas or to simple edema; and 3) isolated tumor cell infiltration extended at least as far as T2 prolongation on magnetic resonance images. This information may be useful in planning surgical procedures and radiation therapy in patients with intracranial glial neoplasms.

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