A5078064665- Medical Imaging Techniques and Applications
- Advanced MRI Techniques and Applications
- Medical Image Segmentation Techniques
- Radiomics and Machine Learning in Medical Imaging
- Advanced X-ray and CT Imaging
- Advanced Neuroimaging Techniques and Applications
- Surgical Simulation and Training
- Nutrition and Health in Aging
- Augmented Reality Applications
- Computer Graphics and Visualization Techniques
- Artificial Intelligence in Healthcare and Education
- MRI in cancer diagnosis
- Radiation Dose and Imaging
- 3D Shape Modeling and Analysis
- Ultrasound Imaging and Elastography
- AI in cancer detection
- Lung Cancer Diagnosis and Treatment
- Meningioma and schwannoma management
- Anatomy and Medical Technology
- Intracranial Aneurysms: Treatment and Complications
- Seismic Imaging and Inversion Techniques
- Cerebrovascular and Carotid Artery Diseases
- COVID-19 diagnosis using AI
- Vascular Malformations Diagnosis and Treatment
- Neurofibromatosis and Schwannoma Cases
Mayo Clinic
2009-2025
Mayo Clinic in Arizona
2009-2025
WinnMed
1996-2020
Mayo Clinic in Florida
1996-2017
General Electric (United States)
2005-2008
General Electric (Israel)
2008
GE Global Research (United States)
2006-2007
Abstract The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a longitudinal multisite observational study of healthy elders, mild cognitive impairment (MCI), and disease. Magnetic resonance imaging (MRI), (18F)‐fluorodeoxyglucose positron emission tomography (FDG PET), urine serum, cerebrospinal fluid (CSF) biomarkers, as well clinical/psychometric assessments are acquiredat multiple time points. All data will be cross‐linked made available to the general scientific community. purpose...
SimpleITK is a new interface to the Insight Segmentation and Registration Toolkit (ITK) designed facilitate rapid prototyping, education scientific activities via high level programming languages. ITK templated C++ library of image processing algorithms frameworks for biomedical other applications, it was be generic, flexible extensible. Initially, provided direct wrapping languages such as Python Tcl through WrapITK system. Unlike WrapITK, which exposed ITK's complex interface, provide an...
Purpose: To develop and evaluate an image-domain noise reduction method based on a modified nonlocal means (NLM) algorithm that is adaptive to local level of CT images implement this in time frame consistent with clinical workflow. Methods: A computationally efficient technique for estimation directly from was developed. forward projection, 2D fan-beam approximation, used generate the projection data, model incorporating effects bowtie filter automatic exposure control. The propagation data...
Most noise reduction methods involve nonlinear processes, and objective evaluation of image quality can be challenging, since cannot fully characterized on the sole basis level at computed tomography (CT). Noise spatial correlation (or texture) is closely related to detection characterization low-contrast objects may quantified by analyzing power spectrum. High-contrast resolution measured using modulation transfer function section sensitivity profile generally unaffected reduction....
Abstract Purpose To determine whether the promise of high‐density many‐coil MRI receiver arrays for enabling highly accelerated parallel imaging can be realized in practice. Materials and Methods A 128‐channel body receiver‐coil array custom system were developed. The comprises two clamshells containing 64 coils each, with posterior built to maximize signal‐to‐noise ratio (SNR) anterior design incorporating considerations weight flexibility as well. Phantom human performed using a variety...
To evaluate the performance of an internally developed and previously validated artificial intelligence (AI) algorithm for magnetic resonance (MR)-derived total kidney volume (TKV) in autosomal dominant polycystic disease (ADPKD) when implemented clinical practice.
To determine if lower-dose computed tomographic (CT) scans obtained with adaptive image-based noise reduction (adaptive nonlocal means [ANLM]) or iterative reconstruction (sinogram-affirmed [SAFIRE]) result in reduced observer performance the detection of malignant hepatic nodules and masses compared routine-dose filtered back projection (FBP).This study was approved by institutional review board compliant HIPAA. Informed consent from patients for retrospective use medical records research...
Exam protocoling is a significant non-interpretive task burden for radiologists. The purpose of this work was to develop natural language processing (NLP) artificial intelligence (AI) solution automated standard abdomen and pelvic magnetic resonance imaging (MRI) exams from basic associated order information patient metadata. This Institutional Review Board exempt retrospective study used de-identified metadata consecutive adult abdominal MRI scans performed at our institution spanning 2.5...
Early identification of ischemic stroke plays a significant role in treatment and potential recovery damaged brain tissue. In noncontrast CT (ncCT), the differences between changes healthy tissue are usually very subtle during hyperacute phase (< 8 h from onset). Therefore, visual comparison both hemispheres is an important step clinical assessment. A quantitative symmetry-based analysis texture features lesions images may provide information for differentiation this phase.One hundred...
Purpose Accurate segmentation of lung nodules is crucial in the development imaging biomarkers for predicting malignancy nodules. Manual time consuming and affected by inter-observer variability. We evaluated robustness accuracy a publically available semiautomatic algorithm that implemented 3D Slicer Chest Imaging Platform (CIP) compared it with performance manual segmentation. Methods CT images 354 manually segmented were downloaded from LIDC database. Four radiologists performed assessed...
Ultrasound localization microscopy (ULM) has been proposed to image microvasculature beyond the ultrasound diffraction limit. Although ULM can attain microvascular images with a sub-diffraction resolution, long data acquisition time and processing are critical limitations. Deep learning-based (deep-ULM) mitigate these However, microbubble (MB) used in deep-ULMs is currently based on spatial information without use of temporal information. The highly spatiotemporally coherent MB signals...
Due to the rapid increase in use of CT imaging and recently-heightened awareness radiation-induced cancer, improving diagnostic quality low dose has become increasingly important. One potential method is signal-to-noise ratio images through denoising. Non-local means a promising approach; however, it many potentially adjustable parameters application-specific areas improvement. The filter uses weighted average similar regions denoise each image pixel. Though classic formulation only patches...
<h3>BACKGROUND AND PURPOSE:</h3> MRA is widely accepted as a noninvasive diagnostic tool for the detection of intracranial aneurysms, but still challenging task with rather low rates. Our aim was to examine performance computer-aided diagnosis algorithm detecting aneurysms on in clinical setting. <h3>MATERIALS METHODS:</h3> Aneurysm detectability evaluated retrospectively 48 subjects and without by 6 readers using 3D viewing system. Aneurysms ranged from 1.1 6.0 mm (mean = 3.12 mm, median...
Medical images such as 3D computerized tomography (CT) scans and pathology images, have hundreds of millions or billions voxels/pixels. It is infeasible to train CNN models directly on high resolution because neural activations a single image do not fit in the memory GPU/TPU, naive data model parallelism approaches work. Existing analysis alleviate this problem by cropping down-sampling input which leads complicated implementation sub-optimal performance due information loss. In paper, we...