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Diffusion is the passive movement of substances down their concentration gradients—requiring no expenditure of cellular energy. Substances, such as molecules or ions, diffuse from an area of high concentration to an area of low concentration in the cytosol or across membranes. Eventually, the concentration will even out, with the substance moving randomly but causing no net change in concentration. Such a state is called dynamic equilibrium, which is essential for maintaining overall...
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Diffusion is a type of passive transport. In passive transport, a substance tends to move from an area of high concentration to an area of low concentration until the concentration is equal across the space. For example, take the diffusion of substances through the air. When someone opens a perfume bottle in a room filled with people, the perfume is at its highest concentration in the bottle and is at its lowest at the edges of the room. The perfume vapor will diffuse, or spread away, from the...
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While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
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Underflow gates are vital for controlling water flow in irrigation canals. The three main types of underflow gates — vertical, radial, and drum gates — serve different purposes while ensuring effective flow management. Vertical gates move up and down, generating a free-flowing water jet; radial gates pivot to regulate the flow; and drum gates rotate for precise adjustments. The flow through these gates is influenced by downstream conditions, resulting in free or drowned outflow.Free and...
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Mass and weight are often used interchangeably in everyday conversation. For example,  medical records often show our weight in kilograms, but never in the correct units of newtons. In physics, however, there is an important distinction. Weight is the pull of the Earth on an object. It depends on the distance from the center of the Earth. Weight dramatically varies if we leave the Earth's surface, unlike mass, which does not vary with location. On the Moon, for example, the...
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Related Experiment Video

Updated: Jan 23, 2026

Author Spotlight: Optimized Lung MRI Protocol with Computationally Efficient Reconstruction Methods
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High-Resolution Diffusion-Weighted Imaging With Self-Gated Self-Supervised Unrolled Reconstruction.

Zhengguo Tan1, Patrick A Liebig2, Annika Hofmann3

  • 1Michigan Institute for Imaging Technology and Translation (MIITT), Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA.

Magnetic Resonance in Medicine
|January 22, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient self-supervised deep learning method for submillimeter diffusion-weighted imaging (DWI). The novel approach enhances image quality and motion robustness, making high-resolution DWI clinically feasible.

Keywords:
algorithm unrollingdiffusion weighted imagingimage reconstructionmachine learningself‐supervised learningsubmillimeter resolution

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Area of Science:

  • Magnetic Resonance Imaging
  • Medical Imaging Technology
  • Computational Neuroscience

Background:

  • High-resolution diffusion-weighted imaging (DWI) is crucial for neuroimaging but faces clinical acquisition challenges.
  • Developing efficient and robust DWI techniques is essential for detailed brain structure and function analysis.

Purpose of the Study:

  • To develop an efficient self-supervised algorithm unrolling technique for submillimeter-resolution DWI.
  • To improve the clinical feasibility of high-resolution DWI acquisition.

Main Methods:

  • Submillimeter DWI acquisition using multi-band multi-shot EPI with diffusion shift encoding.
  • Unrolling the alternating direction method of multipliers (ADMM) for scan-specific, self-gated, self-supervised DeepDWI learning.
  • Implementation on a clinical 7 Tesla scanner.

Main Results:

  • ADMM unrolling demonstrated generalizability across slices.
  • Outperformed MUSE and compressed sensing with LLR regularization in image sharpness, tissue continuity, and motion robustness.
  • Achieved clinically feasible inference times.

Conclusions:

  • The proposed ADMM unrolling enables whole-brain DWI at 0.7 mm isotropic resolution in 10 minutes.
  • Results show higher SNR, clearer tissue delineation, and improved motion robustness.
  • The technique is plausible for clinical translation, advancing neuroimaging capabilities.