Fuyixue Wang^1,2, Zijing Dong^1,3, Qiyuan Tian¹, Congyu Liao¹, Qiuyun Fan¹, W. Scott Hoge⁴, Chanon Ngamsombat¹, Boris Keil⁵, Jonathan R. Polimeni^1,2, Lawrence L. Wald^1,2, Susie Y. Huang^1,2, and Kawin Setsompop^1,2
1A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, ²Harvard-MIT Health Sciences and Technology, MIT, Cambridge, MA, United States, ³3Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, United States, ⁴Department of Radiology, Brigham and Women's Hospital, Boston, MA, United States, ⁵Department of Life Science Engineering, Institute of Medical Physics and Radiation Protection, Giessen, Germany

We present a whole brain in vivo diffusion MRI dataset acquired at 760um isotropic resolution and sampled at 1260 q-space points  across 9 two-hour sessions. The creation of this benchmark in vivo diffusion MRI dataset is possible through use of the Connectom scanner, custom-built 64-channel phased-array coil, gSlider acquisition, dual-polarity GRAPPA reconstruction, reverse phase encoding for distortion mitigation, and personalized motion-robust stabilizer. The data will enhance our understanding of gray and white matter structure with fine detail revealed at sub-mm resolution and serve as a reference dataset for new modeling and processing algorithms.

Peter Börnert^1,2, Holger Eggers¹, Kay Nehrke¹, Peter Mazurkewitz¹, Jürgen Rahmer¹, Johan van den Brink ³, and Silke Hey^3
1Philips Research Hamburg, Hamburg, Germany, ²Radiology, LUMC, Leiden, Netherlands, ³Philips Healtcare Best, Best, Netherlands

Single-shot diffusion-weighted imaging is predominantly performed with echo planar imaging today. Spiral imaging allows shorter echo times and thus promises higher SNR, but is more sensitive to various system imperfections. Previous work showed the feasibility of single-shot diffusion-weighted spiral imaging in the brain. This work explores the applicability to abdominal imaging. It shows that good image quality is achievable in volunteers, using the system demand trajectory for reconstruction, parallel imaging for acceleration, and static main field inhomogeneity mapping for corresponding deblurring.

Bertram Jakob Wilm¹, Manuela Roesler¹, Franciszek Hennel¹, Markus Weiger¹, and Klaas Paul Pruessmann^1
1ETH and University of Zürich, Zürich, Switzerland

To achieve high-resolution diffusion imaging with short echo times, single-shot spiral DWI using a recently developed gradient insert (strength=200 mT/m, slew=600 T/m/s) was implemented. The high gradient strength in combination with the spiral readout allowed for an echo time as short as 19 ms at a b-factor of 1000 s/mm². The high slew rate enabled shortening of the spiral readout duration which reduces sensitivity against static off-resonance and T₂* blurring artifacts, and allowed imaging with an in-plane image resolution of only 0.69 mm. First in-vivo results are presented.

Zhiqiang Li¹, Melvyn B Ooi^1,2, and John P Karis^1
1Neuroradiology, Barrow Neurological Institute, Phoenix, AZ, United States, ²Philips Healthcare, Gainesville, FL, United States

Diffusion weighted MRI is a useful technique for the diagnosis of neurological disorders. DW EPI is time efficient but suffer from geometric distortions. DW PROPELLER and its variants, including turboPROP and turboPROP+, have been proposed to generate distortion free images. This project improves the turboPROP+ technique by incorporating LRX RF phase modulation approach to improve SNR and signal stability, and by revising the phase correction algorithm to minimize residual artifacts. Volunteer results demonstrate reduced artifacts compared to the original phase correction algorithm, and increased SNR/image quality compared to original XY2 phase modulation.

Hu Cheng¹, Jian Wang^1,2, Shreyas Sanjeev Fadnavis³, Eleftherios Garyfallidis³, and Sharlene Newman^1
1Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States, ²School of Information Science and Engineering, Shandong Normal University, Jinan, China, ³Indiana University, Bloomington, IN, United States

We developed a simple deep learning method for DWI data denoising and tested it on correcting sum of square (SoS) noise. By acquiring two sets of diffusion images reconstructed with SoS and SENSE1 coil combination schemes on one subject as training data, the learned model can effectively denoise any SoS data acquired with the same DWI protocol. The denoised data produces similar results in diffusion tensor analysis and NODDI analysis as the SENSE1 data. This method also shed light on denoising techniques for diffusion imaging if a low-noise DWI dataset is available.

Simin Liu¹, Erpeng Dai^1,2, and Hua Guo^1
1Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China, ²Department of Radiology, Stanford University, Stanford, CA, United States

3D simultaneous multi-slab (SMSlab) is a technique to increase SNR efficiency in high-resolution diffusion imaging. However, it still suffers from the intrinsic low SNR of diffusion MRI, especially when using multi-band RF pulses, which increases the pulse duration and thus lengthens the echo time. In this study, root-flipped RF pulses were used in SMSlab to acquired 1 mm isotropic 3D diffusion images. With a multi-band factor of 2, the root-flipped pulses brought about 12 ms reduction of TE (from 91 to 79 ms), and 16% SNR gain, compared to traditional SINC pulses.

Qiyuan Tian^1,2, Berkin Bilgic^1,2, Qiuyun Fan^1,2, Congyu Liao^1,2, Chanon Ngamsombat¹, Yuxin Hu³, Thomas Witzel¹, Kawin Setsompop^1,2, Jonathan R. Polimeni^1,2, and Susie Y. Huang^1,2
1Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, ²Department of Radiology, Harvard Medical School, Boston, MA, United States, ³Department of Electrical Engineering, Stanford University, Stanford, CA, United States

Diffusion tensor imaging (DTI) is widely used clinically but typically requires acquiring diffusion-weighted images (DWIs) along many diffusion-encoding directions for robust model fitting, resulting in lengthy acquisitions. Here, we propose a joint denoising and q-space angular super-resolution method called “DeepDTI” achieved using data-driven supervised deep learning that minimizes the data requirement for DTI to the theoretical minimum of one b=0 image and six DWIs. Metrics derived from DeepDTI’s results are equivalent to those obtained from three b=0 and 19 to 26 DWI volumes for different scalar and orientational DTI metrics, and superior to those derived from state-of-the-art denoising methods.

Kristofor Pas^1,2, Michal Komlosh^2,3, Daniel Perl⁴, Peter Basser², and Dan Benjamini^2,3
1University of Texas at Arlington, Arlington, TX, United States, ²National Institutes of Health, Bethesda, MD, United States, ³Uniformed Service University of the Health Sciences, Bethesda, MD, United States, ⁴Uniformed Services University of the Health Sciences, Bethesda, MD, United States

Multidimensional correlation MRI is an emerging imaging modality that is capable of disentangling highly heterogeneous systems, according to chemical and physical interactions of water within them. Using this approach, the conventional three dimensional MR scalar images are replaced with spatially resolved multidimensional spectra. The ensuing abundance in microstructural and chemical information is a blessing that incorporates a real challenge: how does one distill and refine it into images? Here we introduce a method that robustly identifies the multidimensional spectral components in the image domain, defines the spectral regions of interest, and uses them to reconstruct images of sub-voxel components.

Konstantinos Zormpas-Petridis¹, Nina Tunariu¹, Andra Curcean¹, Christina Messiou¹, David Collins¹, Yann Jamin¹, Dow-Mu Koh¹, and Matthew D. Blackledge^1
1Radiotherapy and Imaging, Institute of Cancer Research, London, Sutton, United Kingdom

Diffusion-weighted MR-imaging (DWI) is an attractive non-invasive tool for staging and response evaluation of myeloma and metastatic bone disease.  However, scans can last up to 30 minutes in whole body studies, which can hinder the adoption of DWI in clinical practice, especially in patients who are unwell. Here, we use a deep learning approach to establish that sub-sampled, but rapidly acquired images, could be used to reconstruct ‘clinical-grade’ DWI images, potentially reducing acquisition times (from ~30 to ~5 minutes). Such time savings could reduce scanning costs and spare patient time/discomfort.

Isabelle Heukensfeldt Jansen¹, Luca Marinelli¹, Ek Tsoon Tan², Robert Y Shih^3,4, J Kevin DeMarco^3,4, J Kent Werner^3,4, Vincent B Ho^3,4, and Thomas Foo^1
1GE Global Research Center, Niskayuna, NY, United States, ²Hospital for Special Surgery, New York, NY, United States, ³Uniformed Services University of the Health Sciences, Bethesda, MD, United States, ⁴Walter Reed National Military Medical Center, Bethesda, MD, United States

We demonstrate a method for simultaneous imaging of diffusion and slow motion in vivo. We use both the magnitude and phase information from image data to reconstruct coherent and incoherent motion (flow and diffusion). We modified a PGSE diffusion imaging sequence so that b-value and encoded velocity can be set independently. We imaged healthy volunteers with a 2-shell sequence with b_max=2000 sec/mm² and v_enc=0.24 mm/s at multiple phases during the cardiac cycle using peripheral gating. Results show a distinct periodic motion around the ventricles with RMS speed 0.065 mm/s, moving laterally during systole and medially during diastole

Ye Wu¹, Yoonmi Hong¹, and Pew-Thian Yap^1
1Department of Radiology and BRIC, University of North Carolina, Chapel Hill, Chapel Hill, NC, United States

We propose a tractography method, called active cortex tractography (ACT), to overcome gyral bias by enabling fiber streamlines to curve naturally into the cortex. We show that the cortex can play an active role in cortical tractography by affording anatomical knowledge to overcome orientation ambiguities as the streamlines enter the superficial white matter in gyral blades and approach the cortex.

Jaume Coll-Font^1,2, Onur Afacan^1,2, Scott Hoge^2,3, Bahram Marami⁴, Ali Gholipour^1,2, Jeanne Chow^1,2, Simon Warfield^1,2, and Sila Kurugol^1,2
1Radiology, Boston Children's Hospital, Boston, MA, United States, ²Harvard Medical School, Boston, MA, United States, ³Radiology, Brigham and Women's Hospital, Boston, MA, United States, ⁴Icahn School of Medicine at Mount Sinai, New York, NY, United States

Diffusion-weighted MRI (DW-MRI) has been increasingly used in abdominal applications. However, unavoidable respiratory motion, as well as B0 field inhomogeneities reduce the accuracy of the quantitative parameters and hinders clinical applicability. In this work, we present a dual echo EPI DW-MRI and slice-to-volume registration method to jointly correct for geometric distortion and motion of the kidneys.  The results show that our method effectively reduced geometric distortions, improved alignment of the DW-MR volumes and increased the precision of the estimated quantitative parameters.

Hassna Irzan^1,2, Michael Hütel², Sebastien Ourselin², Neil Marlow³, and Andrew Melbourne^1,2
1Medical Physics and Biomedical Engineering, University College London, London, United Kingdom, ²Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, ³Institute for Women's Health, London, United Kingdom

Preterm birth has been linked to white matter abnormalities in infants, however the functional implications of these abnormalities are poorly understood. Thus, the long-term effect of such alterations needs further investigation. By combining graph theory and statistical analysis methods, we identify and investigate the hub structure of the preterm brain. The results suggest that while the hub structure is preserved, the connectivity strength and capacity of information flow is reduced and that is linked to reduced brain volume as well as preterm birth.

Nian Wang¹, Leonard E. White², Gary Cofer¹, Yi Qi¹, and G. Allan Johnson^1
1Department of Radiology, Duke University, Durham, NC, United States, ²Department of Neurology, Duke University, Durham, NC, United States

Diffusion MRI (dMRI) encompasses a broad range of scales, physical mechanisms and models and applications from clinical to the basic sciences. The recent development of compressed sensing allowed us to extend the spatial and contrast resolution to define more subtle brain architecture beyond the meso scale, solidly in the microscopic domain. We report here dMRI at spatial resolution down to 25 μm i.e. voxels that are more 500,000 times smaller than that of the routine clinical scans.

SangJIn Im¹ and Hyeon-Man Baek^2
1Gachon Advanced Institute for Health Sciences & Technology, Gachon university, Incheon, Korea, Republic of, ²Gachon university, Incheon, Korea, Republic of

Although tractography research was focused primarily on the human brain, tractography was integrated into animal models to benefit from various preclinical experiments. Accurate segmentation is required for proper connectome of animal models. The Allen mouse brain atlas can provide accurate coordinates and segmentation information to the mouse brain, but it is difficult to use because it is not MRI data. In this study, we use the ABA to accurately segment the mouse brain and examine tractography. In addition, various NEX are used to determine the changes in tractography caused by an increase in the SNR of the DTI.