A5050934244- Advanced Neuroimaging Techniques and Applications
- Advanced MRI Techniques and Applications
- MRI in cancer diagnosis
- Health Systems, Economic Evaluations, Quality of Life
- Parkinson's Disease Mechanisms and Treatments
- Photoacoustic and Ultrasonic Imaging
- Acute Ischemic Stroke Management
- Artificial Intelligence in Healthcare
- Hemispheric Asymmetry in Neuroscience
- Neurological disorders and treatments
- Brain Tumor Detection and Classification
- Medical Imaging Techniques and Applications
- Genetic Neurodegenerative Diseases
- Ultrasound Imaging and Elastography
- Advanced Measurement and Metrology Techniques
- Electrochemical sensors and biosensors
- Biofield Effects and Biophysics
- Engineering Applied Research
- Advanced X-ray and CT Imaging
- Radiomics and Machine Learning in Medical Imaging
- Industrial Vision Systems and Defect Detection
Boston University
2023
Universidad de Valladolid
2017-2021
The University of Texas Health Science Center at Houston
2019
University of Wisconsin–Madison
2019
Background and Objective: Synthetic magnetic resonance imaging (MRI) is a low cost procedure that serves as bridge between qualitative quantitative MRI.However, the proposed methods require very specific sequences or private protocols which have scarcely found integration in clinical scanners.We propose learning-based approach to compute T1, T2, PD parametric maps from only pair of T1-and T2-weighted images customarily acquired routine.Methods: Our based on convolutional neural network (CNN)...
To present a novel Optimized Diffusion-weighting Gradient waveform Design (ODGD) method for the design of minimum echo time (TE), bulk motion-compensated, and concomitant gradient (CG)-nulling waveforms diffusion MRI.ODGD motion-compensated were designed various moment-nullings Mn (n = 0, 1, 2), range b-values, spatial resolutions, both without (ODGD-Mn ) with CG-nulling -CG). Phantom in-vivo (brain liver) experiments conducted ODGD to compare motion robustness, signal-to-noise ratio (SNR),...
Purpose To develop motion‐robust, blood‐suppressed diffusion‐weighted imaging (DWI) of the liver with optimized diffusion encoding waveforms and evaluate accuracy reproducibility quantitative apparent coefficient (ADC) measurements. Methods A novel approach for design weighting waveforms, termed M 1‐optimized (MODI), is proposed. MODI includes an echo time‐optimized motion‐robust gradient waveform design, a small nonzero first‐moment motion sensitivity ( 1) value to enable blood signal...
Parkinson's disease is the second most prevalent neurodegenerative disorder in Western world. It estimated that neuronal loss related to precedes clinical diagnosis by more than 10 years (prodromal phase) which leads a subtle decline translates into non-specific signs and symptoms. By leveraging diffusion magnetic resonance imaging brain (MRI) data evaluated longitudinally, at least two different time points, we have opportunity of detecting measuring changes early on process, thereby...
FiberMapper 2.0 facilitates automatic (pre-) processing of MRI data, reconstructs major white matter tracts and derives imaging biomarkers along the sensitive to a wide range microstructural properties. Additionally, Q-FiberMapper implements tract lesion burden analysis which together with quantitative MR could be very useful for early diagnosis, evaluation monitoring different neurodegenerative neuroinflammatory diseases that affect such as traumatic brain injury, multiple sclerosis, others.
Motivation: The implementation of QC in T1w MRI scans remains unstandardized and still involves human specialists, without consensus on a systematic approach to identify the presence artifactsGoal(s): Our goal was compare relative performance three algorithms with visual QC, their capabilities detecting simulated blurring, ghosting, noise artifacts new unseen dataset. Approach: Synthetic were introduced into that passed quality control, thresholds determined for CAT12 LONIQC, classifier...
Motivation: Hemispheric asymmetries have shown a great potential for early detection of variety neurological disorders such as traumatic brain injury. However, the lack consistency in study outcomes is common issue, largely stemming from restricted sample sizes and methodological variations. Goal(s): To develop unified framework processing multimodal imaging data comprehensive evaluation asymmetry. Approach: Combination state-of-the-art algorithms to estimate different metrics Results: By...
Motivation: Imaging biomarkers are becoming a cornerstone to increase throughput and efficiency of large clinical trials. However, the diversity imaging modalities used derive them creates complexity for both protocols image archiving systems. Goal(s): To develop tool automatically classify assess their adherence predefined acquisition protocol. Approach: The combination few-shot learning classifier trained according contrast characteristics deterministic heuristic approach based on DICOM...
Registration of diffusion weighted datasets remains a challenging task in the process quantifying indexes. Respiratory and cardiac motion, as well echo-planar characteristic geometric distortions, may greatly limit accuracy on parameter estimation, specially for liver. This work proposes methodology non-rigid registration multiparametric abdominal imaging by using different well-known metrics under groupwise paradigm. A three-stage validation is carried out computational phantom, watery...
The Apparent Diffusion Coefficient (ADC) is a quantitative measure derived from MRI that able to asses the amount of diffusion within living tissues. It employed characterize different diseases and evaluate response therapy. For ADC estimation, signal needs be sampled using small number values, presenting distortions due aliasing windowing effect. In this work we theoretically study these effects propose some new robust estimators for based on Fourier Transform signal.
May 8, 2019April 9, 2019Free AccessLongitudinal Connectivity Outperforms Volumetric Change As A Marker For Prodromal Parkinson’s Disease (S41.003)Danilo Pena, Jessika Suescun, Óscar Peña-Nogales, Rodrigo Luis-Garcia, Timothy Ellmore, Mya Schiess, and Luca GiancardoAuthors Info & AffiliationsApril 2019 issue92 (15_supplement)https://doi.org/10.1212/WNL.92.15_supplement.S41.003 Letters to the Editor