# Systems Seminar

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Accelerated Medical Imaging: Exploiting Temporal, Spatial, or Parametric Correlation

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Prof. Walter Block

Dept. of Biomedical Engineering

UW-Madison

#### Abstract

Computed tomography (CT), X-ray, and ultrasound imaging have achieved
dramatic accelerations in imaging performance over the last year though
the addition of greater and greater numbers of detectors. Magnetic
resonance imaging (MRI), however, has traditionally been a slow diagnostic
imaging modality because spatially encoding the MR signal is not done by
a geometric means but is instead acquired in an alternative, Fourier domain.
Only recently has MRI developed methods to utilize multiple detectors to
offload some of the burden of spatial encoding in the Fourier domain to
effectively sample more than one data point at a time. Algorithms used to
exploit the redundant information in multiple detectors (coils), termed
parallel imaging, become dramatically more complex when used with non-Cartesian
acquisition patterns of the MR Fourier space. The first part of the talk
describes numerical methods we have developed to utilize and optimize
parallel imaging, particularly in non-Cartesian sampling.
In the second part of the talk, the common assumption in medical imaging that
all pixels in a temporal or parametric image series are independent and then
must be resampled completely for each temporal or parametric index is examined.
The University of Wisconsin MR and CT groups has developed a broad class of
approximate reconstruction methods, termed HighlY Constrained Back PRojection
(HYPR), to utilize the significant redundancy within medical imaging. These
methods have superior SNR relative to previous acceleration techniques and
often permit transferring the SNR of an entire scan to individual time frames.
Often these concepts can be teamed with accelerated non-Cartesian acquisition
methods to achieve larger gains. For example, combining an accelerated MR
acquisition with a HYPR reconstruction has achieved undersampling factors
up to 1000 in selected applications. When applications require a tighter
error constraint, we have developed iterative constrained reconstruction
methods where the approximate method serves as an initial estimate to speed
convergence. Though methods have begun to be developed that merge the HYPR
concept with parallel imaging, significant opportunities exist to advance
this field though utilizing advanced estimation theory and compressed sensing.

**Time and Place:** Wed., Nov. 28, at 3:30 pm in 4610.

**SYSTEMS SEMINAR WEB PAGE:**
http://homepages.cae.wisc.edu/~gubner/seminar/schedule.html

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Web Page Contact: John (dot) Gubner (at) wisc (dot) edu