As the Chief MRI Physicist at the Center for Brain Imaging, of New York University, I'm responsible for the overall functioning of the Center, as well as development of pre-processing tools and automated reproducible workflows, user support, protocol development and optimization, data quality, equipment, software and graduate student teaching.
My team and I are passionate about helping our users advance their research programs by automating data extraction and pre-processing, allowing them to spend less time on those tedious time-consuming tasks and more on doing actual science. We are strong supporters of research reproducibility, by using Docker images (which are publicaly available) and by keeping detailed logs of what tools (and versions) were run on the data. We also support open science by developing new open source tools to make neuroimaging data analysis easier. As well, I enjoy educating our users on MRI physics, guiding them during their study setup, helping them during the data collection phase and troubleshooting image preprocessing.
Our users come from diverse departments -- NYU (Psychology, Center for Neural Science, Neuroeconomics, Nursing) or NYU School of Medicine (Psychiatry, Ophthalmology, Epilepsy Center) or external users (Child Mind Institute, Fordham University).
Drawing on my physics background, I'm conducting research in MRI image analysis, in projects with direct application to our users' fields of studies:
- Image and data extraction. I developed bidsphysio, a tool to extract physiology data in BIDS format. It supports data in DICOM format, AcqKnowledge and Siemens PMU data. Our team is currently working on adding functionality, like converting eye tracking data and subjects responses to BIDS. I'm also a contributor to heudiconv, a widely-used tool to convert DICOM images to BIDS format.
- Scanner Quality Assurance (QA). When running functional MRI experiments, we are trying to detect very small changes in signal intensity. Many times, these changes are of the same order as the noise in the images. Therefore, it is crucial to make sure that all the different components of the scanner are working properly, that the scanner is running at peak performance and that the signal-to-noise ratio doesn't change significantly from day to day. I have developed a daily QA protocol for our center and I have developed a software tool that automatically analyzes the images, tracks them over time and eventually will email automatically a report with the results.
- Automated Image Quality Control (QC). Even when the scanner is performing at its best, there might be other factors detrimental to the image quality (subject motion, wrong parameters, etc.) We have made it very easy for users to automatically run MRIQC, an image QC tool, when they extract their data. However, I'm now working on a tool that will run MRIQC on the images as soon as the session is completed, so that users will get the report automatically within a couple of hours.
- EPI Artifact reduction. Fast imaging techniques like EPI (used in functional imaging, diffusion weighted imaging, arterial spin-labeling, etc.) have long readout times. This means that in areas of the brain close to air pockets (sinuses, etc.), the pronounced magnetic field gradients produce severe artifacts in the image. Standard tools cannot track changes in the magnetic field over the duration of the session. I'm working on non-standard readout schemes that allow in great part to correct these distortions.
- Real-Time data monitoring. We have set up a system that allows us to monitor data quality in real time (as the images are being reconstructed by the scanner). It monitors motion, background noise, ghosting and spikes. It allows our users to stop a run if the subject is moving too much and try to reacquire it while the subject is still available, or to try to find the sources of artifacts at the time of scanning.
Teaching. Since 2009, I've been teaching one third of the graduate level Functional Magnetic Resonance Imaging Lab course for the Psychology Department at NYU. I cover the physical principles behind MRI (spins, precession, magnetization, relaxation), the different components of an MRI scanner, MR safety, image formation (encoding, k-space), image reconstruction, image contrast, pulse sequences, the physics behind the BOLD contrast, parallel imaging and artifacts. I've also given an invited lecture every year in the "Advanced MRI" course at the Vilcek Institute of Graduate Biomedical Sciences (NYU School of Medicine), about the physics behind the BOLD contrast and functional brain imaging.
Looking forward, my plans for CBI include among other things porting all of our preprocessing pipelines to the universityâs HPC, developing tools to convert stimulus and subject responses files to BIDS, and setting up an automated pipeline to run all of the distortion corrections for animal anatomical imaging. These improvements will add significant value to our users' research and maintain our status as an MRI center of excellence.
Before working in MRI, I worked for two years in human visual perception as a postdoctoral researcher at the Center for Neural Science, also at New York University, under the supervision of Prof. Nava Rubin. I studied the perception of motion, specifically the aperture problem. This problem arises from the relatively small size receptive field of neurons in visual area V1. Therefore, the information that we receive is somehow "ambiguous." I studied in detail the different models that have been proposed to explain "illusory" perceptions and how the brain manages to solve the ambiguity. I showed that none of the models explain well enough the experimental data that I collected, and --together with Prof. Rubin-- we proposed an alternate model. What makes my results especially relevant is the fact that they reconcile two different research approaches to address the problem of visual perception: motion perception and structure from motion.
Before coming to NYU, I worked at the Materials Science Institute of Madrid, in the Physical Properties of Materials Group. There, I conducted the main part of the research for my Ph. D. thesis, which I defended in 2002, at the Universidad Autónoma de Madrid. My thesis focused on new magnetic materials, showing Colossal MagnetoResistance (CMR). My Thesis is available at this link (PDF, 6 MB).
Further information can be found on my C.V.