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Research project: Reading between the lines: Signal Processing for faster fMRI acquisition

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The human brain is the most complex organ in our bodies and our current understanding of its intricate operation remains limited. We don’t even fully understand yet how our brains are connected, how these connections differ between individuals and exactly what happens when connections fail. Modern brain imaging methods, such as functional Magnetic Resonance Imaging (fMRI), now allow us for the first time to study these questions in detail in the living human brain. fMRI can measure brain activity and is now increasingly used to measure brain connections. From data acquired when a person is "at rest" that is, when they do not perform a specific task, we can estimate brain regions that exchange information and from this, we can deduce that a connection between these regions must exist. However, current fMRI technology is limited and slow. In this project, we work towards methods that significantly speed up fMRI acquisition, which in turn will allow us to collect data of higher quality. This new information provides much more detail and will enable us to learn much more about the way in which the human brain is connected, knowledge that becomes increasingly valuable in our battle against many neurological disorders.

Imaging the human brain
Imaging the human brain
Matrix completion and functional MRI

fMRI data contains information on how brain activity changes with time. Activity is measured in hundreds of thousands of brain areas so that the data can be thought of as a large data matrix that contains a time-series for each spatial location. The acquisition of a single time point at hundreds of thousands of spatial locations is time consuming and standard fMRI techniques are only able to sample brain activity with a temporal resolution of about 2 to 3 seconds and a spatial resolution of about 2 to 3 mm3. To get finer temporal resolution, it is possible to only measure some of the spatial information. In effect, this means that the fMRI data matrix has missing entries.

In this project, we are developing advanced algorithms that are able to recover these missing entries. This is done based on the assumption that measured brain activity is dominated by relatively few main network structures. This assumption leads to the constraint that the data matrix is low rank, so that the latest techniques developed in low-rank matrix completion can be used to recover the full data-set.

Data from traditional fMRI acquisition
Data from traditional fMRI
Data reconstructed from 4 x accelerated fMRI acquisition
Data reconstructed

Advanced MRI techniques have enormous potential as diagnostic tools for the early detection of neurological disorders such as Alzheimer's, autism, schizophrenia and epilepsy. Early detection of these disorders would in turn have ramifications for early treatment. Any improvements in diagnosis and treatment of neurological diseases can lead to substantial financial savings in healthcare provision, especially with our aging population, where mental conditions such as dementia are becoming a significant problem.

Missing matrix entries are recovered through a low-rank factorisation
Missing matrix entries
Project Partners

Much of this work is done in collaboration with the Oxford Centre for Functional MRI of the brain (FMRIB)


This work is funded by EPSRC grants:

  • EP/J005444/1 Advanced FMRI acquisition, reconstruction and signal processing for dynamic brain network imaging
  • EP/K037102/1 Constrained low rank matrix recovery: from efficient algorithms to brain network imaging
  • Chiew M, Smith SM, Koopmans P, Blumensath T, Miller K, "Acceleration of Resting State FMRI Data Acquisition using Matrix Completion," OHBM, Seattle, 2013
  • Chiew M, Miller KL, Koopmans PJ, Tunniclie EM, Smith SM, Blumensath T, "Iterative hard thresholding and matrix shrinkage (IHT+MS) for low-rank recovery of k-t undersampled MRI data," 21st International Society of Magnetic Resonance in Medicine, Salt Lake City, 2013.
  • Chiew M, Smith SM, Koopmans PJ, Blumensath T, Miller KL (2013). "k-t FASTER: A new method for the acceleration of resting state FMRI data acquisition," 21st International Society of Magnetic Resonance in Medicine, Salt Lake City, 2013.
  • Mark Chiew, Karla Miller, Stephen Smith, Thomas Blumensath, "Acceleration of low-rank MRI data acquisition," US Patent (pending).
k-t space sampling pattern used for accelerated acquisition
k-t space sampling pattern

Related research groups

Signal Processing, Audio and Hearing Group
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