MRM Q&A posts
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.
By Blake DeweyBernhard Strasser
This week we gathered across multiple continents (as I feel we always do!) to discuss the ins and outs of spectroscopic imaging with Bernhard Strasser and Wolfgang Bogner, two authors of “(2 + 1)D-CAIPIRINHA accelerated MR spectroscopic imaging of the brain at 7T”, one of the MRM Editor’s Picks for August 2017. In this paper, Bernhard and his colleagues propose a new method of acceleration for magnetic resonance spectroscopic imaging (MRSI) that combines 2D CAIPIRINHA with simultaneous multi-slice (SMS) to accelerate imaging in all three spatial dimensions.
By Mathieu BoudreauXiao-Yong Zhang
The August 2017 Editor’s Pick is from Xiao-Yong Zhang and Zhongliang Zu, researchers at Vanderbilt University in Nashville. Their paper presents a newly discovered Nuclear Overhauser Effect (NOE) signal at -1.6 ppm from water. They measured this signal in normal rat brains at 9.4 T, and found that it changed significantly in a rodent tumor model. Using reconstituted phospholipids and cultured cell experiments, they hypothesize that this signal may originate from membrane choline phospholipids. We recently spoke with Xiao-Yong and Zhongliang Zu about their work.
By Brian ChungPatrick Schuenke
This week we ventured across continents to speak with Drs. Patrick Schünke and Moritz Zaiss, two primary authors of a recent paper from the German Cancer Research Center (DKFZ) in Heidelberg, Germany titled: “Adiabatically Prepared Spin-Lock Approach for T1ρ-Based Dynamic Glucose Enhanced MRI at Ultrahigh Fields.” In this paper, the authors developed an NMR method for imaging glucose using an ultrahigh field MR scanner and a spin-lock approach to gain sensitivity to chemical exchange. At ultrahigh field strengths, distinct artifacts appear predominantly resulting from RF field inhomogeneities. Thus, adiabatic pulses were implemented to enable the application of spin-lock MRI at fields such as 7T. This adiabatic spin-lock approach is explained, its feasibility for application in vivo at 7T is verified, the technique’s sensitivity to glucose is investigated, and a first proof of concept of spin-lock based glucose imaging for the detection of cancer in humans is presented.
By Pinar Ozbay
It is our pleasure to present one of the Editor’s picks for July, Preconditioned Total Field Inversion (TFI) Method for Quantitative Susceptibility Mapping (QSM), from Cornell University. In this work Zhe Liu, Pascal Spincemaille and colleagues proposed an algorithm which allows mapping of tissue magnetic susceptibility in regions with large dynamic susceptibility ranges, such as cavities, bones, and hemorrhages in the head. There are two main steps in QSM algorithm which are removal of background fields to calculate the local field, and solving the local field-to-susceptibility problem. The latter is an ill-posed problem by nature, hence is mainly referred to as the step of inverse problem in the literature of QSM. Their method calculates susceptibility maps via ‘total field inversion’, which generalizes those two steps as one optimization problem, and further employs preconditioning to achieve fast convergence.