Michael Asghar¹, Rosa Sanchez Panchuelo², Denis Schluppeck¹, and Susan Francis^1
1University of Nottingham, Nottingham, United Kingdom, ²University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom

High-resolution fMRI data of somatosensory cortex were obtained at 7T (n=7 participants) using a 4x4 grid of piezoelectric stimulators. Population receptive field (pRF) mapping was performed using different model specifications (1D Gaussian within-digit, 1D Gaussian between-digit, 2D Gaussian of within-digit and between-digit, Unconstrained model) and pRF sizes were estimated. Grouping pRF response by 4 preferred locations within digits (along tip-base axis) or by Brodmann areas revealed more elliptical pRFs within digit representations of D2 to D4 and more circularly symmetric pRFs for D5. We also found relatively larger pRF sizes in D5 and towards the base of the digits.

Veronika Janacova¹, Pavol Szomolanyi¹, Dominik Vilimek^1,2, Siegfried Trattnig^1,3,4, and Vladimir Juras^1
1High Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria, ²Department of Cybernetics and Biomedical Engineering, VSB–Technical University of Ostrava, Ostrava, Czech Republic, ³CD laboratory for Clinical Molecular MR imaging (MOLIMA), Vienna, Austria, ⁴Institute for Clinical Molecular MRI in the Musculoskeletal System, Karl Landsteiner Society, Vienna, Austria

Texture analysis of quantitative T₂ maps in combination with machine learning was explored as a tool for classification and prediction of various conditions in musculoskeletal (MSK) research. We explored random forest classification algorithm as a tool for identification of important texture features for the classification of MACT grafts and native cartilage twelve months after surgery. Our model performed with high accuracy (84.6%) and identified features with highest importance were: cluster prominence, sum average, autocorrelation and correlation.

Elise Noelle Woodward¹, Gregory Lemberskiy², Matthew S Fox^1,3, and Alexei Ouriadov^1,3
1Physics and Astronomy, Western University, London, ON, Canada, ²New York University Grossman School of Medicine, New York, NY, United States, ³Lawson Health Research Institute, London, ON, Canada

In this study, we demonstrate the effectiveness of two de-noising methods on diffusion-weighted and dynamic lung imaging in healthy rats using ¹²⁹Xe and ¹⁹F. While the results showed a insignificant difference for denoising method 1 (MP-PCA), de-noising method 2 (MP-PCA preceded by hard-thresholding over a nuclear norm in k-space) showed significant difference in the overall mean Lm and LmD values for ventilation and r (fractional ventilation estimate) for ventilation images. 

Ryan McNaughton^1,2, Ning Hua², Lei Zhang³, David Kennedy⁴, Karl Kuban², and Hernan Jara^2
1Mechanical Engineering, Boston University, Boston, MA, United States, ²Boston University Medical Center, Boston, MA, United States, ³University of North Carolina at Chapel Hill, Chapel Hill, NC, United States, ⁴University of Massachusetts, Worcester, MA, United States

Purpose: To develop an integrated theoretical model and computer algorithms for mapping individually and simultaneously the spatial distributions of free water, alongside myelin and iron. Theory: We solve a fast exchange relaxation model effectively decomposing qMRI maps of nPD, R1, and R2 into maps of free water, myelin, and iron. Results: The image processing technique generates maps of free water, myelin, and iron in the brains of adolescents born extremely preterm. Conclusion: A theoretical framework and image processing pipeline for mapping the distribution of free water has been developed and tested with a cohort of adolescents born extremely preterm.

Thomas Gladytz¹, Ehsan Tasbihi¹, Jason M. Millward¹, Kathleen Cantow², Luis Hummel², Joao S. Periquito^1,2, Sonia Waiczies¹, Erdmann Seeliger², and Thoralf Niendorf^1,3
1Berlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrueck Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany, ²Institute of Physiology, Charité - Universitätsmedizin Berlin, Berlin, Germany, ³Experimental and Clinical Research Center, a joint cooperation between the Charité - Universitätsmedizin Berlin and the Max Delbrueck Center for Molecular Medicine, Berlin, Germany

Several renal pathologies are associated with changes in kidney size, offering an opportunity for magnetic resonance imaging (MRI) biomarkers of disease. An automated bean-shaped model was developed for kidney size measurements in rats using parametric MRI (T₂, T₂* mapping). The ABSM approach was applied to longitudinal renal size quantification during pathophysiologically relevant interventions affecting kidney size. A precision and accuracy similar to manual segmentation was achieved allowing size changes of 2% to be detected reliably.  This can potentially be instrumental for developing MRI-based diagnostic tools for various kidney disorders and for gaining new insight into mechanisms of renal pathophysiology.

Ericky Caldas de A Araujo^1,2, Benjamin Marty^1,2, and Harmen Reyngoudt^1,2
1Neuromuscular investigation center, Institute of Myology, Paris, France, ²NMR Laboratory, CEA/DRF/IBFJ/MIRCen, Paris, France

The multiexponential T2 of water in biological tissue is known to reflect microscopic anatomical compartmentation. T2-T2 correlation relaxometry allows characterizing compartmental sizes, intrinsic T2 values and exchange rates, which are of upmost clinical relevance. However, inversion of relaxation data into T2 spectra is an ill-posed problem. Regularized Inverse Laplace transform (rILT) provides stable solutions, but these are penalized by low spectral resolution and relatively high computational complexity. Here we do T2-T2 relaxometry of a urea solution and show that, for such bi-compartment system, non-linear least squares fitting provides solutions that are more accurate while avoiding the difficulties related to rILT.

Rémy Gardier¹, Juan Luis Villarreal Haro¹, Erick Jorge Canales-Rodriguez¹, Gabriel Girard^1,2,3, Jonathan Rafael-Patiño^1,3, and Jean-Philippe Thiran^1,2,3
1Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, ²CIBM Center for Biomedical Imaging, Lausanne, Switzerland, ³Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland

For over a decade, microstructure imaging has been a hot research topic in DW-MRI. Tissue complexity compelled researchers to make assumptions about certain properties. The inverse problem of microstructure imaging, in particular, is ill-posed, and current methods fix some parameters to reduce the solution space. In this abstract, we look at how intracellular and extracellular diffusion coefficients affect the estimation of compartmentalized model parameters. We show that robust estimation of some parameters does not extend to all parameters using Monte-Carlo simulations in impermeable substrates with multiple diffusivities, and we identified the extracellular compartment as the most influential on estimation quality.

Shantanu Sinha¹, Ryan Hernandez², Vadim Malis¹, T Oda³, and Usha Sinha^2
1Radiology, UC San Diego, San Diego, CA, United States, ²Physics, San Diego State University, San Diego, CA, United States, ³Hyogo University of Teacher Education, Kato, Hyogo, Japan

The quadriceps muscle plays an extremely critical role in various aspects of human locomotion. Changes in its function from various disease such as aging, muscular disuse or modified gait can degrade mobility. VE-PC imaging provides a convenient way to study muscle motion and assess the relative contribution of the thigh muscles to force production. This study is focused on deriving the strain and strain rate tensors to explore patterns along the fiber direction and cross-section. We were able to successfully obtain strain tensor images as well as to follow the temporal variation of these indices in different muscle compartments.

Sam Sharifzadeh Javidi¹ and Hamidreza Salighehrad^2,3
1Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran (Islamic Republic of), ²Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran (Islamic Republic of), ³Quantitative MR Imaging and Spectroscopy Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran (Islamic Republic of)

IVIM-DTI imaging is capable of revealing both structural and functional maps. We conduct this study to address the long acquisition time of IVIM-DTI imaging and the inaccuracy of estimates of the model’s outputs. Diffusion-weighted images were acquired at 11 b-values and 64 orientations. We used the Kalman filter to reach higher accuracy in a lower number of images. The resulting maps indicated that diffusion maps and pseudodiffusion maps for a healthy case are of highly visual similarity. Our results also confirmed that achieving a good accuracy is possible with just half number of images.

Oscar Albert Dabrowski¹, Oscar Albert Dabrowski¹, Sébastien Courvoisier², Jean-Luc Falcone³, Antoine Klauser², Julien Songeon³, Michel Kocher², Bastien Chopard³, and Francois Lazeyras^2
1Computer science, University of Geneva, Geneva, Switzerland, ²CIBM, Geneva, Switzerland, ³University of Geneva, Geneva, Switzerland

In the context of EPI time series acquisition, fast motion occurring below the Nyquist limit (< 1 TR),  present a problem for accurate registration by 3D rigid-body algorithms. A method for temporally upsampling SMS EPI acquisitions is proposed. EPI volumes are splitted into subvolumes according to the temporal acquisition order of SMS and interpolated using a data driven approach based on pairwise affine transforms and linear interpolation. A proof of concept based EPI acquisitions of  controlled head choreographies shows that fast motion below the Nyquist rate can be more accurately estimated by our method.

Luke Reynolds¹, Alex MacKay^1,2,3, and Carl Michal^1
1Physics & Astronomy, University of British Columbia, Vancouver, BC, Canada, ²Radiology, University of British Columbia, Vancouver, BC, Canada, ³UBC MRI Research Centre, University of British Columbia, Vancouver, BC, Canada

Reliably quantifying longitudinal relaxation in heterogenous systems like white matter is challenging because measured parameters are sensitive to details of the measurement technique. In particular, cross-relaxation/magnetization exchange between aqueous and non-aqueous tissue components may lead to multi-exponential relaxation during inversion recovery, depending on the difference in the pools’ initial magnetizations. We use a generalization of the Bloch equations to fractional order to show that the additional component stemming from this exchange is better described by a stretched Mittag-Leffler function than a standard exponential in heterogenous systems. This approach may provide additional information about the material structure.

Sophie Malaquin¹ and Julien Valette^1
1Université Paris-Saclay, Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA), Centre National de la Recherche Scientifique (CNRS), Molecular Imaging Research Center (MIRCen), Laboratoire des Maladies Neurodégénératives, Fontenay aux Roses, France

Brain lactate compartmentation in neurons, astrocytes and the extracellular space is presumably of critical importance, but remains impossible to assess non-invasively. DW-MRS of lactate might capture lactate fractions in each compartment, provided these compartments induce different diffusion properties. Here we propose a framework where diffusion of cell-specific metabolites (Ins and NAA) is modeled to predict lactate diffusion in astrocytes and neurons. We use some “realistic” (spheres+cylinders) and simplified (cylinders) models of cell microstructure to show that lactate fractions in each compartment can be accurately estimated, despite inaccurate microstructure modeling. Models are finally tested on in vivo mouse brain data. 

Scott Peltier^1,2, Michelle Karker¹, Jon-Fredrik Nielsen^1,2, Navid Seraji-Bozorgzad³, Henry Paulson³, Bruno Giordani^3,4, and Benjamin M. Hampstead^4,5
1Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States, ²Functional MRI Laboratory, University of Michigan, Ann Arbor, MI, United States, ³Neurology, University of Michigan, Ann Arbor, MI, United States, ⁴Psychiatry, University of Michigan, Ann Arbor, MI, United States, ⁵Mental Health Service, VA Ann Arbor Healthcare System, Ann Arbor, MI, United States

This work examined the relationship between white matter microstructure parameters calculated by NODDI and a composite measure of memory function. Connectome predictive modeling was done on fractional anisotropy, orientation dispersion, and neurite density maps.  Significant relationships were found for all parameters, with orientation dispersion having the highest correlation between predicted and observed memory scores.

Guanxiong Luo¹, Moritz Blumenthal¹, Xiaoqing Wang^1,2, and Martin Uecker^1,2,3,4
1University Medical Center Göttingen, Göttingen, Germany, ²German Centre for Cardiovascular Research (DZHK), Partner Site Göttingen, Göttingen, Germany, ³Institute of Medical Imaging, Graz University of Technology, Graz, Austria, ⁴Cluster of Excellence "Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells'' (MBExC), University of Göttingen, Göttingen, Germany

Most deep-learning-based reconstructions methods need predefined sampling patterns and precomputed coil sensitivities for supervised training, limiting their later use in applications under different conditions. Furthermore, only the magnitude images are always stored in DICOM format in the Picture Archiving and Communication System (PACS) of a typical radiology department. That means that raw k-space data is usually not available. This work focuses on how to extract prior knowledge from magnitude images (DICOM) and how to apply the extracted prior to reconstruct images from k-space multi-channel data sampled with different schemes.