Chiara Maffei¹, Fuyixue Wang¹, Suzanne Haber^2,3, and Anastasia Yendiki^1
1Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States, ²Department of Pharmacology and Physiology, University of Rochester School of Medicine, Rochester, NY, United States, ³McLean Hospital, Belmont, MA, United States

The relatively low resolution of conventional in-vivo human dMRI prevents the reconstruction of small axonal bundles (diameter < 3mm). Here we show that diencephalic connections, which are inaccessible at macroscopic resolution (1.5mm), can be reconstructed with unprecedented accuracy at mesoscopic resolution (760μm). We investigate the minimum number of directions needed to achieve this with a state-of-the-art, multi-slab dMRI sequence. We find that a long, multi-session acquisition is necessary. We aim to develop a protocol for manual annotation of these bundles in a small number of high-quality datasets, and thus produce a training set for automated reconstruction in lower-quality dMRI data. 

Andrea Dell'Orco^1,2,3,4, Layla Tabea Riemann², Semiha Aydin², Michael Scheel¹, and Ariane Fillmer^2
1Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Department of Neuroradiology, Berlin, Germany, ²Physikalisch-Technische Bundesanstalt (PTB), Braunschweig und Berlin, Germany, ³Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology, Berlin, Germany, ⁴Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, NeuroCure Clinical Research, Berlin, Germany

The accurate quantification of ultra-short echo-time ¹H-MRS spectra is challenging due to broad macromolecular (MM) signal components. Here, we propose the parameterized-ratio MM (PRaMM) method to model the MM and its use in linear-combination model fitting of ¹H-MRS spectra. The PRaMM method uses ratios of MM signal amplitudes as soft-constraints to limit the degrees of freedom of the fitting model, while allowing for more flexibility than commonly used approaches. The suggested model is demonstrated to improve the quantification of metabolites compared to two common methods for MM treatment.

Robert Jones¹, Chiara Maffei¹, Qiyuan Tian¹, Susie Huang¹, Vaanathi Sundaresan¹, and Anastasia Yendiki^1
1Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital & Harvard Medical School, Charlestown, MA, United States

We investigate the use of generalized anisotropy profiles (GAPs) for delineating boundaries between subcortical gray and white matter in vivo. Replicating results from a previous ex vivo study, we show that GAPs, which are computed from the diffusion propagator, provide more informative contrasts than T1- and T2-weighted images or conventional diffusion metrics. An undersampled Cartesian-grid sampling of q-space can be used to obtain these profiles with a reasonable scan time. Thus, GAPs show promise as a multi-channel contrast that could be used in the future to improve structural segmentation.

Muriel Bruchhage^1,2,3, Yaqing Chen⁴, Alvaro Gajardo Cataldo⁴, Hans-Georg Müller⁴, Elizabeth Weisee³, Sian Wilson^5,6, Maximilian Pietsch^5,7, Viren D'Sa^2,3, Andre Marquand⁸, Sylvia Madhow^9,10, Kristofer Bouchard^9,10, James H. Cole^11,12, Francesca Biondo^11,13, Jed Elison¹⁴, Jonathan OMuirchheartaigh^5,6,7, and Sean C. L. Deoni^2,3,15
1Psychology, Stavanger University, Stavanger, Norway, ²Pediatrics, Warren Alpert Medical School at Brown University, Providence, RI, United States, ³Diagnostic Imaging, Rhode Island Hospital, Providence, RI, United States, ⁴Statistics, UC Davis, Davis, CA, United States, ⁵Centre for the Developing Brain, Department of Perinatal Imaging and Health, King's College London, London, United Kingdom, ⁶Department of Forensic & Neurodevelopmental Sciences, King's College London, London, United Kingdom, ⁷MRC Centre for Neurodevelopmental Disorders, King's College London, London, United Kingdom, ⁸Radboud University, Maastricht, Netherlands, ⁹Scientific Data Division & Biological Systems and Engineering Division, LBNL, Berkeley, CA, United States, ¹⁰Helen Wills Neuroscience Institute & Redwood Center for Theoretical Neuroscience, UC Berkeley, Berkeley, CA, United States, ¹¹Centre for Medical Image Computing, Computer Science, UCL, London, United Kingdom, ¹²Dementia Research Centre, Queen Square Institute of Neurology, UCL, London, United Kingdom, ¹³Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom, ¹⁴Institute of Child Development, University of Minnesota, Minneapolis, MN, United States, ¹⁵Brown's Advanced Baby Imaging Lab, Rhode Island Hospital, Providence, RI, United States

The first 1000 days are essential for a child’s development, consisting of critical time windows for brain and cognitive development but also vulnerability. In this large multi-cohort study, we investigated typical development of 478 children with ≤6 timepoints across two different scanners with different acquisition protocols. PACE brain-for-age growth percentiles and regression slope functions with 95% pointwise confidence intervals identified critical windows of neurocognitive development. Our results could allow for more appropriate neurodevelopmental burden estimates across multi-scanner cohorts, and help identify primary risk factors and age-specific intervention impact.

Sana Vaziri¹, Adam Autry¹, Marisa Lafontaine¹, Janine M Lupo¹, Jeremy W Gordon¹, Jasmine Hu¹, Hsin-Yu Chen¹, Yaewon Kim¹, Javier Villanueva-Meyer¹, Susan M Chang², Jennifer Clarke^2,3, Nancy Ann Oberheim Bush^2,3, Duan Xu¹, Peder EZ Larson¹, Daniel B Vigneron^1,4, and Yan Li^1
1Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States, ²Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States, ³Department of Neurology, University of California, San Francisco, San Francisco, CA, United States, ⁴Department of Bioengineering and Therapeutic Science, University of California, San Francisco, San Francisco, CA, United States

Dynamic hyperpolarized (HP) ¹³C metabolic imaging allows for non-invasive measurements of real-time enzymatic conversion of injected [1-¹³C]pyruvate to [1-¹³C]lactate. The HP signal depends on several factors, including perfusion and monocarboxylate transporter activity. Previously, a global increase of pyruvate-to-lactate apparent conversion rates values was found in patients with glioma after receiving anti-angiogenic therapies. To better understand the effects of tissue vasculature on HP signals, we investigated the relationship between HP[1-¹³C]pyruvate metabolism and ¹H perfusion parameters in normal-appearing white matter patients with glioma.

Jose Pedro Manzano Patron^1,2, Steen Moeller³, Essa Yacoub³, and Stamatios Sotiropoulos^1
1Sir Peter Mansfield Imaging Centre, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom, ²Precision Imaging Beacon, University of Nottingham, Nottingham, United Kingdom, ³Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States

Denoising diffusion MRI data has gained significant interest over the last years, due to the inherently low-SNR in the dMRI signal. Despite the existence of a number of denoising algorithms, open questions exist on how, and even whether, to denoise data. A consistent set of evaluations that comprehensively characterise newly-developed approaches and their impact on downstream applications is lacking for providing insight to these questions. Here we propose EDDEN, a framework for Evaluating DMRI DENoising approaches, consisting of a set of unique data and assessments. We demonstrate its use using 3 exemplar denoising methods (NLM, MPPCA and NORDIC).

Icaro A.F. Oliveira^1,2, Yuxuan Cai^1,2, Shir Hofstetter¹, Jeroen C.W. Siero^1,3, Wietske van der Zwaag¹, and Serge O. Dumoulin^1,2,4
1Spinoza Centre for Neuroimaging, Amsterdam, Netherlands, ²Experimental and Applied Psychology, VU University, Amsterdam, Netherlands, ³Radiology, University Medical Centre Utrecht, Utrecht, Netherlands, ⁴Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, Netherlands

We extended the use of VASO-CBV to pRF mapping modeling. We show that VASO-CBV data can be used reliably to map polar angle and eccentricity, similar to BOLD-based data. In addition, the pRF size increased systematically from V1 to V3 similarly for BOLD and VASO-CBV estimates. The higher microvascular specificity of VASO-CBV did not result in smaller pRF size estimates. This result suggests that the vascular contribution to the pRF size is not dominant in either VASO-CBV or BOLD-based pRF mapping.