Lars Kasper

Lars Kasper

he/him

An MR physicist based in Toronto. Trying to enhance fMRI sensitivity by efficient acquisition (spirals), recon & noise characterization (field+physiological).

About Lars

Welcome!

I am an MR physicist specializing in fMRI, with a particular focus on physiological noise correction, magnetic field monitoring, and image reconstruction and more than 15 years of experience in ultra-high field human MRI (7 Tesla). Currently, I am the Lead MR Physicist at the Toronto Neuroimaging Facility (ToNI) in the Department of Psychology, University of Toronto.

My personal story through the lens of MRI can be found in this ISMRM Member Spotlight from 2023.

My core research interest is improving image and time series quality in MRI through understanding both numerator and denominator of the signal-to-noise ratio, i.e., optimizing sampling in MRI, as well as characterizing and correcting for the various physiological and system-induced noise sources.

I am a fervent supporter of open and accessible neuroscience, and contribute through clean code in the form of usable toolboxes (PhysIO for physiological noise correction in fMRI, UniQC for unified image quality control in MR development) and workflows (GIRFReco.jl for image reconstruction with expanded signal models, as applied, e.g,. to spirals MR sequences). Furthermore, as a member of the equity, diversity and inclusivity (EDI) committee of my department, as well as the EDI Task Force of the International Society for Magnetic Resonance in Medicine, I aim to improve accessibility for researchers with caregiver responsibilities.

Research Interests

My research focuses on adapting state of the art advances in MR imaging methodology in the context of neuroscientific and translational MRI applications, Therein, I aim at increasing sensitivity from both angles, i.e., maximizing the available signal of interest by advanced MR acquisition methods, while also reducing the underlying noise floor, by modeling physiological noise and imperfections (artifacts) in functional MRI (fMRI) measurements. A key instrument for identifying and disentangling noise sources in this framework is the integration of independent measurements from contactless or peripheral sensors concurrent to the MR data collection, such as magnetic field probes, pulse plethysmographs or breathing belts.

  • Advanced fMRI methodologies (spiral imaging, magnetic field monitoring)
  • Physiological noise correction in MRI (PhysIO Toolbox)
  • Bayesian modeling & computational neuroscience (Dynamic Causal Modeling)
  • Research software engineering for MRI analysis
  • Open-source tool development (PhysIO, UniQC, TAPAS, GIRFReco.jl, Neurodesk)

Academic Metrics

  • h-index: 27
  • Publications: 43 peer-reviewed articles
  • Citations: 3599 (Google Scholar, Feb 2025)
  • Key Awards: ISMRM Junior Fellow 2017, ISMRM Conference Highlight Abstract 2020