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Swarm Optimization and Machine Learning to Improve the Detection of Brain Tumor
Published in Shikha Agrawal, Manish Gupta, Jitendra Agrawal, Dac-Nhuong Le, Kamlesh Kumar Gupta, Swarm Intelligence and Machine Learning, 2022
A MRI uses magnetic fields to generate detailed images of a body part. A dye or contrast medium is used for getting a clear image of the scan. The dye may be injected into the patient’s body or may be given as a pill or liquid. A MRI is preferred to a CT scan as it produces better images. It is also used to detect the size of the tumor. There are different types of MRI techniques which are recommended by the doctors according to its type and its possibility of spreading to other parts of body. One of the types is a Prefusion MRI that is used to check the flow of blood into the tissues. This process helps the doctors to predict the affect of treatment and to distinguish between a recurrent tumor and a dead tumor tissue. A Functional MRI (FMRI) is used by the doctors to plan for the surgery. During this scan a person is asked to perform certain tasks and the changes caused in the brain due to these tasks, like use of oxygen and blood flow in brain, are studied by the doctors. FRMI can therefore help the surgeon avoid damage to functional parts of the brain while removing a tumor. Magnetic Resonance Spectroscopy (MRS) is used to obtain information about the chemical composition of the brain or the number of metabolites in the body. This test helps to evaluate the response of therapies by differentiating among the dead tissues caused by previous treatments and new tumor cells.
Big Data in Medical Image Processing
Published in R. Suganya, S. Rajaram, A. Sheik Abdullah, Big Data in Medical Image Processing, 2018
R. Suganya, S. Rajaram, A. Sheik Abdullah
Magnetic Resonance Imaging (MRI) is a medical imaging technology that uses radio waves and a magnetic field to generate detailed images of organs and tissues. MRI has proven to be highly effective in diagnosing a number of conditions by showing the difference between the normal and diseased soft tissues of the body. MRI is often used to evaluate: Blood vesselsAbnormal tissueBreastsBones and jointsOrgans in the pelvis, chest and abdomen (heart, liver, kidney, spleen)Spinal injuriesTendon and ligament tears
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Published in Mara Cercignani, Nicholas G. Dowell, Paul S. Tofts, Quantitative MRI of the Brain: Principles of Physical Measurement, 2018
Conventional MRI can delineate structural abnormalities and changes in vascular parameters but fails to characterise features at the molecular and cellular levels. Magnetic resonance spectroscopy (MRS) is another application of the nuclear magnetic resonance phenomena that is able to differentiate the chemical composition of small tissue metabolites. The molecules in the brain are mobile and they mostly have narrow linewidths, which makes it easy to differentiate from each other (see Figure 12.1). Compared with tissue water that is the target for conventional MRI and is approximately 41 molar in the brain, these metabolites have much lower concentrations, which are in the range of 0.5–15 mM. This means that MRS techniques have relatively low sensitivity and have required significant technical development in order to adequately detect changes in brain metabolism that are associated with normal and pathological conditions.
A detailed review of contrast-enhanced fluorescence magnetic resonance imaging techniques for earlier prediction and easy detection of COVID-19
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2023
T. Lurthu Pushparaj, E. Fantin Irudaya Raj, E. Francy Irudaya Rani
The commencement of consolidation, according to Carotti et al. (2020), is associated with tumour growth and indeed the production of fibromyxoid metabolic byproducts within the alveoli. Various levels of literature searches found that basic and higher versions of MRI are the only techniques that could be used instead of CT to detect COVID-19 infection in organs or soft tissue. Either with or without lipid concentration, T2-weighted fast rotational imaging (Pellegrini et al. 2021), T1-spin echo (Reynolds and Mahajan 2021), inversion recovery approaches (Nalbandian et al. 2021), and gradient-echo sequences (Raman et al. 2021) have all been studied for lung nodule detection using MRI so far. The most common approaches for fluid build-up inside the lung are T2-weighted and proton-density weighted sequences (Ayoubkhani et al. 2021). For example, magnetic resonance angiography (MRA-for visualising blood) and magnetic resonance spectroscopy are two new applications and indications for MRI (MRS-for imaging changes in chemical combo in tissues). Torkian et al. (2021) found ground-glass opacity (GGO), accumulation, surface roughness, as well as a reversal peripheral signal on multiple MR images.
A deep learning approach for diagnosing schizophrenic patients
Published in Journal of Experimental & Theoretical Artificial Intelligence, 2019
Srivathsan Srinivasagopalan, Justin Barry, Varadraj Gurupur, Sharma Thankachan
Magnetic Resonance Imaging (MRI) is a non-invasive imaging modality that returns valuable information about the physiology of the human brain, including size, shape, and tissue structure (Bois, Whalley, McIntosh, & Lawrie, 2015). MRI captures either structural or functional information. Functional MRI (fMRI) utilizes Blood-oxygen-level-dependent (BOLD) signals to capture an approximate measurement of activity between remote regions in the brain (Demirci & Calhoun, 2009). Structural MRI (sMRI) provides information on varying characteristics of brain tissue such as gray matter, white matter, and cerebrospinal fluid (Vemuri & Jack, 2010). The challenge with using sMRI data to diagnose based on structural changes brought on by SCZ is the overlap in structural change brought on by factors closely linked with SCZ such as alcoholism and anti-psychosis medication (Bois et al., 2015). Previous studies have shown that the combination of fMRI and sMRI data can be used in conjunction with a deep learning autoencoder to classify mental disorders including SCZ (Patel, Aggarwal, & Gupta, 2016; Silva et al., 2014; Zeng et al., 2018). In one such study (Patel et al., 2016), researchers used an autoencoder, four-layers deep in encoding and decoding, to learn the features of the input data, then used SVM to classify the data with 92% accuracy.
Hybrid deep learning algorithm for brain tumour detection
Published in The Imaging Science Journal, 2022
Jyoti Srivastava, Jay Prakash, Ashish Srivastava
If there is an unnatural rise in the total number of brain cells, then we are talking about a tumour in the brain. A powerful sense of solidity pervades the brain as the skull encloses the brain [1]. Complicating factors include any structural growth, such as a walled-in area. More often than not, brain tumours that are malignant (cancerous) are more likely to be cancerous than brain tumours that are benign (non-cancerous). There is a risk of brain injury and even death if these tumours keep growing and expanding at their current rate [2]. Two types of brain tumours can develop secondary tumours and primary tumours. It is possible to have an inoperable primary brain tumour that's non-cancerous develop in someone's brain [3]. Metastatic brain tumours are those cancers that have spread from another organ, such as the lung or the breast [4, 5]. In the end, the tumour was discovered thanks to medical imaging tools. Imaging techniques are one of the subfields of radiology. Radiological techniques can provide helpful information on the human body's anatomy and physiology, which can be used in a wide variety of contexts. Contrasting chemicals can enhance the quality of pictures [6] captured by imaging modalities such as ultrasound, CT, X-ray, and MRI. MRI (magnetic resonance imaging) is an advanced medical imaging technique that produces high-quality human body images. A radio wave-based imaging technique known as magnetic resonance imaging (MRI) is a type of MRI [7]. Tumours of the brain, ankles and feet can be detected and treated using magnetic resonance imaging (MRI). When studying, magnet fields and radio waves [8] are more successful than current imaging technologies since they are not detrimental to the human body.