MRM Q&A posts
BY ELENA KLEBAN
For this month, we discuss the importance of timescale in NMR experiments with Donghan “Mo” Yang, Joseph “Joe” Ackerman, and Joel Garbow. Their work examines the pre-exchange lifetime using ‘brains on beads’ – a delicate in vitro system of neuronal cells grown on polymer beads. In addition to this marvel, we also consider the accuracy of Joe’s premonition regarding MRI.
BY RYAN TOPFER
Having spent over a decade developing MR hardware, the Zaitsev group in Freiburg has tried their hand at “basically everything but the magnet itself.” Lacking the Big Budget of industry, they favor a different approach to innovation: creating “strange things” with the “means at hand.” Sebastian and Maxim here discuss one of the fruits of this design philosophy: their 84-channel gradient system.
BY TANGUY DUVAL
Kawin Setsompop and Larry Wald are old friends of MRM Highlights, and their work has been prominently featured in our magazines (here, here and here). In their most recent MRM paper, they complement their simultaneous multi-slice (SMS) acquisition with an additional 3D RF-encoding for each 3mm slice (or thin slab), in order to push the resolution of diffusion imaging to 600 µm isotropic in the brain. This technique nicely solves the issue of phase corruption in multi-shot MRI acquisition.
In their paper entitled “Multishot echo-planar MREIT for fast imaging of conductivity, current density, and electric field distributions”, Drs. Munish Chauhan and Rosalind Sadleir propose an accelerated technique to image electrical conductivity based on MRI. Their goal is not only to image conductivity of biological tissues, but more ambitiously to map neural activity using this fast technique. Let’s hear their story behind the paper.
By Zahra Hosseini & Phillip WardHongjiang Wei
Recently, we had the pleasure to sit down and have a chat with Dr. Hongjiang Wei and Dr. Chunlei Liu about their editor-selected manuscript entitled “Investigating Magnetic Susceptibility of Human Knee Joint at 7 Tesla.” The conversation took a technical turn right away, when we asked the authors about their chosen technique, quantitative susceptibility mapping (or QSM). Hongjiang explained that, “quantitative susceptibility mapping is an approach to, as the name implies, extract a map of the underlying tissue susceptibility on a pixel-by-pixel basis. QSM computes the magnetic susceptibility from the phase signal of gradient recalled echoes with the assumption that phase shift is mainly due to susceptibility induced field inhomogeneity.” In simple terms, it is a map that demonstrates how tissues interact with the magnetic field of the MRI scanner.
By Maria Eugenia CaligiuriKathleen M.Ropella
Kathleen Ropella received her Bachelor’s Degree in Biomedical Engineering at Marquette University, and her Master’s Degree at the University of Michigan, where she will defend her PhD thesis this semester (busy times ahead!).
Douglas C. Noll did his PhD in Electrical Engineering with Al Macovski at Stanford, after being introduced by his intramural basketball pals, Dwight Nishimura, Steve Conolly, and Craig Meyer. In 1991, he started his first faculty position at the University of Pittsburgh, working on functional MRI with the first 3T magnet GE ever made. Doug later transitioned to be a professor of biomedical engineering at the University of Michigan – so he’s been in the field of MRI for about 30 years now!
Their paper, “A Regularized, Model-Based Approach to Phase-Based Conductivity Mapping Using MRI,” was focused on two things: first, achieving accurate measurements of conductivity – which describes the ability of a tissue to conduct electric current – at tissue boundaries; and second, the possibility of using non-quadratic regularizers, thanks to advances in compressed sensing.
By Ryan TopferJohanna Vannesjo
A Highlights Halloween special: For those less than BOLD researchers who remain frightful of Nyquist ghosts, fear not! Johanna and Klaas herein reveal their trick for treating shim and gradient coil-induced field distortions with full cross-term pre-emphasis and, more generally, some tricks of the trade – “How to Make It” in the world of MR engineering research.
By Jessica McKayMichael Dieckmeyer
This month we talked to Michael Dieckmeyer and Dimitrios (Dimitris) Karampinos about their work to measure apparent diffusion coefficient (ADC) values in bone marrow. Michael has a very diverse education that includes a master’s degree in mathematics, and he is currently completing his final year of medical school. His mentor Dimitris leads a multidisciplinary research team in Munich that focuses on the development of quantitative MRI, targeting musculoskeletal diseases and metabolic diseases like obesity and diabetes. In this paper, they use modeling to overcome some of the challenges of ADC quantification in the presence of fat. By including the proton density fat fraction (PDFF) and the T2 of water, they can reduce the bias in the ADC measurements that is introduced by residual fat.
By Nikola StikovStephen Cauley
The Martinos center in Boston recently brought us wave-CAIPI, an accelerated 3D imaging technique that uses helixes in k-space to encode information and speed up MRI acquisition. However, differences in the calibration of the gradient systems made it difficult to generalize the wave-CAIPI technique and deploy it on any clinical scanner. This is where the Editor’s Pick for September comes in; Stephen Cauley and his colleagues proposed a joint optimization approach to estimate k-space trajectory discrepancies simulataneously with the underlying image. We asked Steve and senior author Larry Wald to tell us the story of autocallibrated wave-CAIPI.