Search or ask a question

Which body plane is used in the MRI scan?

Electromagnetic coil

Convolutional neural network

Imaging phantom

Magnetic resonance imaging

Answers from top 5 papers

PDF

Open Access

More filters

Papers (5)	Insight
Open access•Journal Article•DOI Fully automated detection of breast cancer in screening MRI using convolutional neural networks. Mehmet Ufuk Dalmış, Suzan Vreemann, Thijs Kooi, Ritse M. Mann, Nico Karssemeijer, Albert Gubern-Mérida - Show less +5 more 11 Jan 2018-Journal of medical imaging 59 Citations	In conclusion, we developed a CADe system that is able to exploit the spatial information obtained from the early-phase scans and can be used in screening programs where abbreviated MRI protocols are used.
Journal Article•DOI Feed and Wrap MRI Technique in Infants. Nina K. Antonov, Carrie Ruzal-Shapiro, Kimberly D. Morel, William S. Millar, Sudha Kashyap, Christine T. Lauren, Maria C. Garzon - Show less +6 more 01 Oct 2017-Clinical Pediatrics 93 Citations	It can be used prior to an magnetic resonance imaging (MRI) scan, avoiding sedation or anesthesia.
Journal Article•DOI Nasopalatine duct cysts and the role of magnetic resonance imaging. Henry M. Spinelli, Isenberg Js, O'Brien M - Show less +2 more 01 Feb 1994-Journal of Craniofacial Surgery 11 Citations	An MRI scan is useful diagnostic tool; it provides exceptional definition of the anatomy in this region.
Journal Article•DOI An Image Correction Protocol to Reduce Distortion for 3-T Stereotactic MRI Wagner Malagó Tavares, Francisco Tustumi, Claudia da Costa Leite, Lionel Fernel Gamarra, Edson Amaro, Manoel Jacobsen Teixeira, Erich Talamoni Fonoff - Show less +6 more 01 Jan 2014-Neurosurgery 17 Citations	The technique is applicable to all routinely used spin-echo MRI.
Journal Article•DOI A proposed scheme for comprehensive characterization of the measured geometric distortion in magnetic resonance imaging using a three-dimensional phantom. Deming Wang, David M. Doddrell - Show less +1 more 01 Aug 2004-Medical Physics 20 Citations	The scheme can be potentially used as a standard procedure for the assessment of geometric distortion in MRI.

My columns

Related Questions

What is bodyimage?5 answersBody image refers to an individual's perception and feelings about their physical appearance, encompassing aspects like body shape, size, and overall self-image. It is influenced by various factors such as lifestyle, attitudes, and societal standards. Research shows that body image can be affected by anthropometric indicators like body mass index and waist circumference, particularly leading to body dissatisfaction in adolescents. Disturbances in body image can result in extreme behaviors like self-starvation in eating disorders such as anorexia nervosa. Additionally, the concept of body image extends to the emotional and lived experiences of communicative interactions, highlighting the importance of understanding the body's role in communication dynamics. Overall, body image is a complex and multidimensional construct that plays a significant role in shaping individuals' self-perception and behaviors.

How are fetal mri brain images aligned in for transverse planes?5 answersFetal MRI brain images are aligned in transverse planes using various methods. One approach is to define a mid-sagittal plane (MSP) that separates the two cerebral hemispheres. This plane is commonly used to standardize the visualization of important anatomy in MRI scans. To automatically define the MSP, algorithms have been proposed that detect the position of the head and establish a symmetrical axis that minimizes the difference between the image on either side. Another method involves aligning the images to a referential coordinate system based on skull boundaries, location of the eye sockets, and head pose. This information is used to estimate an affine transformation that aligns the volumetric image to the skull-based coordinate system. These alignment techniques ensure accurate positioning of fetal brain images in transverse planes for further analysis and diagnosis.

How many images are taken during an MRI?4 answers

How to pay for a MRI scan?4 answers

What are the disadvantages of an MRI scan?4 answers

What does a MRI scan stand for?5 answers

See what other people are reading

What is optimizers ADAMAX?

AdaMax is an optimizer used in Deep Learning models, specifically in the context of optimizing the AlexNet architecture for tasks like biometric authentication and face recognition. It has been compared to other optimizers like Adam, SGD, AdaGrad, RMSProp, and AdaDelta in various studies. AdaMax has shown superior performance in optimizing the AlexNet architecture when applied to datasets containing audio files or images of celebrities. The research indicates that AdaMax outperforms Adam in terms of model accuracy when used with small datasets and k-fold cross-validation techniques. Additionally, AdaMax has been modified and fine-tuned in studies to enhance its efficiency and compatibility with different deep learning models, ensuring better accuracy and training process optimization.Are there ensemble techinques used for classification of breast cancer data?

Ensemble techniques are extensively utilized for the classification of breast cancer data. Various studies have proposed ensemble models combining different machine learning algorithms to enhance predictive accuracy. These ensemble approaches often involve multiple levels of learning, incorporating diverse base models like decision trees, logistic regression, and neural networks, among others. By leveraging the strengths of individual models and combining their predictions, ensemble methods significantly improve classification performance, surpassing the capabilities of standalone models. The use of ensemble techniques, such as stacking, bagging, and boosting, has demonstrated superior accuracy in diagnosing breast cancer, making them valuable tools for improving the prognosis and treatment outcomes for patients.What is the source of the TMD and myofacial pain?

The source of Temporomandibular Disorders (TMD) and myofascial pain can be multifactorial. TMD encompasses musculoskeletal and neuromuscular conditions affecting the temporomandibular joint (TMJ) and masticatory muscles, often linked to trigeminal nerve branches. Myofascial pain syndrome, a common cause of TMD, is characterized by trigger points in masticatory muscles, exacerbated by parafunctional activities like teeth clenching. Etiological models suggest that myogenous TMD pain may result from muscle overload due to behaviors like sleep bruxism, possibly compounded by psychological factors. Treatment for TMD and myofascial pain involves a multidisciplinary approach, including patient education, biobehavioral strategies, therapeutic exercises, trigger point dry needling, pharmacotherapy, and oral appliances. Additionally, myofascial pain dysfunction syndrome, prevalent in females, is associated with stress, anxiety, and bruxism, leading to symptoms like muscle tenderness, clicking TMJ noises, and limited jaw movement.How does osteoporosis make bones fragile?

Osteoporosis leads to bone fragility through a decrease in bone mineral density (BMD) and disrupted microarchitecture. This metabolic disease causes bones to weaken and become more susceptible to fractures, particularly in the hip, spine, and wrist. Factors like aging, decreased calcium and vitamin D levels, and low physical activity contribute to the weakening of bone tissue, making bones more fragile and prone to fractures. In young individuals, especially when fragility fractures occur without an obvious cause, investigations may reveal underlying monogenetic bone diseases that further exacerbate bone fragility. The loss of bone mass and strength due to osteoporosis ultimately compromises the structural integrity of bones, increasing their fragility and susceptibility to fractures, highlighting the critical importance of early diagnosis and appropriate management of this condition.Why does SVM outperform the neural network model?

Support Vector Machines (SVMs) outperform neural network models due to their excellent properties such as convexity, good generality, and efficiency. SVMs can achieve top performances in two-class classification and regression problems, while their training cost is at least quadratic on sample size, making them unsuitable for large sample problems. Additionally, SVM-based Deep Stacking Networks (SVM-DSN) leverage the convex optimization and support vector properties of SVMs within a deep learning framework, allowing for parallelizability and convergence to optimal solutions, leading to improvements in anti-saturation and interpretability compared to neural networks. Experimental results demonstrate that SVM-DSN models exhibit excellent performance compared to competitive benchmark models on image and text datasets.Detailing the mental, economic consequences, damage to livelihood, etc. following the distribution of a deep-fake video?

The distribution of deep-fake videos can have severe consequences on individuals and society. Mentally, these videos can manipulate perceptions, causing psychological harm and threatening political stability. Economically, deep-fakes can disrupt businesses by spreading false information, impacting commerce and potentially damaging livelihoods. The dissemination of fabricated content through deep-fakes can lead to the spread of disinformation, increasing cyber risks in financial technology applications and magnifying traditional fraud threats. Detecting these videos is crucial to prevent such harm, with proposed methods including the use of convolutional neural networks and long short-term memory to distinguish between real and fake video frames. Overall, the impact of deep-fake videos extends to mental health, economic stability, and cybersecurity, highlighting the need for vigilance and awareness in combating this technology.What can 3DUNet do?

The 3D U-Net model, as discussed in the provided contexts, is capable of segmenting multi-modal magnetic resonance (MR) images with enhanced efficiency and precision. By incorporating the Transformer architecture into the 3D U-Net model, the MMTrans3DUNet is able to extract global context from tokenized image blocks and fuse information from multiple imaging modes (t1, t1ce, t2, flair) to overcome limitations of single-modal images in lesion subdivision. Additionally, the 3D U-Net model can be further improved by integrating Capsule networks, which enhance robustness in part-whole representation learning and address issues related to pooling layers and sensitivity to transformations. Furthermore, advancements like the Selecting the Overlap Method (SOM) operation and the RC-3DUnet model contribute to accurate 3D pancreas segmentation while reducing memory requirements.How effective are Caenorhabditis sp. in detecting cervical cancer at an early stage?

Caenorhabditis elegans, a nematode species, has shown remarkable effectiveness in detecting various cancers, including cervical cancer, at an early stage. Studies have demonstrated the potential of using C. elegans as a biosensor for cancer detection. The Nematode Scent Detection Test (NSDT) utilizing C. elegans has shown a high sensitivity of 95.8% and specificity of 95.0% in detecting cancer, including cervical cancer, at early stages. Additionally, the use of C. elegans in combination with urine samples has significantly improved cancer detection sensitivity, even for early-stage cancers, surpassing traditional blood tumor markers. These findings highlight the promising role of Caenorhabditis elegans in early cancer detection, including cervical cancer, offering a cost-effective, non-invasive, and rapid screening method.What are the potential benefits of incorporating mindfulness meditation into everyday life?

Incorporating mindfulness meditation into everyday life can yield numerous benefits for mental and emotional well-being. Mindfulness practices have gained popularity across various domains, from work environments to school settings, due to their positive impacts on cognitive functioning, stress reduction, emotional well-being, and social skills. Research suggests that mindfulness meditation can lead to a reduction in stress levels, promote calmness, lower the production of stress hormones, enhance emotional resilience, improve cognitive function, and aid in better sleep patterns. By cultivating self-awareness, inner stability, and self-compassion, individuals can navigate stressors with clarity and efficiency, ultimately improving their overall well-being and relationships with themselves and others.What does energy consumption for training and offering artificial intelligence look like?

Energy consumption for training and deploying artificial intelligence (AI) models is a critical consideration due to its environmental impact. The demand for high computational power in AI models, especially deep learning, has led to increased energy consumption. Different computational architectures like ARM, GPU, and X86 have varying energy requirements for training AI algorithms. The energy consumption depends on the model implementation, hardware composition, and the type of compute nodes used. Strategies like Bayesian optimization during hyperparameter tuning and reducing network complexity can significantly reduce energy consumption during training. It is crucial to balance energy efficiency and accuracy in AI model design to mitigate the environmental impact of high energy consumption.What are the steps involved in disassembling an electric motor for rewinding?

The steps involved in disassembling an electric motor for rewinding include various processes. Firstly, the cable head is disassembled and grounded, followed by detaching the anchor screw, wheel screw, shell grounding wire, and cooling device. Then, the wheel is detached, and the non-load side bearing cover and end cover are removed, followed by the load side end cover and bearing cover. Additionally, the original stator bars and supports are removed, new supports with cooling channels are provided, and new stator bars are inserted into the slots. Furthermore, tools are prepared, and various components such as the detection clamp sensor support, rotary cutting fluid connector, and digital tooth disc are removed to disassemble the motor spindle. These steps ensure a systematic and efficient disassembly process for rewinding electric motors.