Diffusion MRI (dMRI) is typically time consuming as it involves acquiring a series of 3D volumes, each associated with a wave-vector in q-space that determines the diffusion direction and strength. The acquisition time is further increased when "blip-up blip-down" scans are acquired with opposite phase encoding directions (PEDs) to facilitate distortion correction. In this work, we show that geometric distortions can be corrected without acquiring with opposite PEDs for each wave-vector, and hence the acquisition time can be halved. Our method uses complimentary rotation-invariant contrasts across shells of different diffusion weightings. Distortion-free structural T1-/T2-weighted MRI is used as reference for nonlinear registration in correcting the distortions. Signal dropout and pileup are corrected with the help of spherical harmonics. To demonstrate that our method is robust to changes in image appearance, we show that distortion correction with good structural alignment can be achieved within minutes for dMRI data of infants between 1 to 24 months of age.
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http://dx.doi.org/10.1007/978-3-030-59713-9_4 | DOI Listing |
Background: Synthetic biology involves combining different DNA fragments, each containing functional biological parts, to address specific problems. Fundamental gene-function research often requires cloning and propagating DNA fragments, such as those from the iGEM Parts Registry or Addgene, typically distributed as circular plasmids.Addgene's repository alone offers around 150,000 plasmids.
View Article and Find Full Text PDFIEEE Trans Neural Netw Learn Syst
February 2023
Contrastive learning (CL) methods achieve great success by learning the invariant representation from various transformations. However, rotation transformations are considered harmful to CL and are rarely used, which results in failure when the objects show unseen orientations. This article proposes a representation focus shift network (RefosNet), which adds the rotation transformations to CL methods to improve the robustness of representation.
View Article and Find Full Text PDFAnn Transl Med
December 2021
School of Medical Imaging, North Sichuan Medical College, Nanchong, China.
Background: Liver segmentation in computed tomography (CT) imaging has been widely investigated as a crucial step for analyzing liver characteristics and diagnosing liver diseases. However, obtaining satisfactory liver segmentation performance is highly challenging because of the poor contrast between the liver and its surrounding organs and tissues, the high levels of CT image noise, and the wide variability in liver shapes among patients.
Methods: To overcome these challenges, we propose a novel method for liver segmentation in CT image sequences.
Neuroimage Clin
January 2022
Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands; Image Sciences Institute, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands. Electronic address:
Objectives: Acquisition-related differences in diffusion magnetic resonance imaging (dMRI) hamper pooling of multicentre data to achieve large sample sizes. A promising solution is to harmonize the raw diffusion signal using rotation invariant spherical harmonic (RISH) features, but this has not been tested in elderly subjects. Here we aimed to establish if RISH harmonization effectively removes acquisition-related differences in multicentre dMRI of elderly subjects with cerebral small vessel disease (SVD), while preserving sensitivity to disease effects.
View Article and Find Full Text PDFJ Neural Eng
July 2021
Department of Computer Science, University of Verona, Verona, Italy.
The mechanisms driving multiple sclerosis (MS) are still largely unknown, calling for new methods allowing to detect and characterize tissue degeneration since the early stages of the disease. Our aim is to decrypt the microstructural signatures of the Primary Progressive versus the Relapsing-Remitting state of disease based on diffusion and structural magnetic resonance imaging data.A selection of microstructural descriptors, based on the 3D-Simple Harmonics Oscillator Based Reconstruction and Estimation and the set of new algebraically independent Rotation Invariant spherical harmonics Features, was considered and used to feed convolutional neural networks (CNNs) models.
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