[visionlist] Special issue on “Whole Body MRI: Restoration and Analysis with Signal/Image Processing Principles.”

[Stathis Hadjidemetriou]

Dear Colleagues,

This is for a special issue
<https://www.mdpi.com/journal/signals/special_issues/L6K2572P65> on:
“Whole Body MRI: Restoration and Analysis with Signal/Image Processing
Principles.”

It invites submissions on the development of pre-processing, restoration
and analysis methodologies for the effective and efficient interpretation
of whole-body MRI data for the benefit of patient examinations. There is a
summary of the scope of the special issue below.

Best regards,
Dr. Stathis Hadjidemetriou
Dr. Ismini Papageorgiou
Guest Editors


Summary:
Whole-Body MRI (WB-MRI) has a large Field-of-View (FoV) that covers the
entire body. It is a competitive image expedition tool to both cover
conventional clinical needs as well as enable novel observations.  It
currently includes indications that span over a spectrum of physiology and
pathology. An example for physiology is for monitoring fat and muscle in
sports medicine. Furthermore, evidence collected so far argument towards a
future first-line indication of WB-MRI in cancer staging and follow up.
Implementations for bone metastatic disease detection reveal equal or
higher sensitivity and specificity of the WB-MRI compared to classical
radiation-base methods such as the bone scans with Tc99m-based
radiopharmaceuticals and Positron Emission Tomography CT (PET-CT). Thus,
T1w and T2w MRI as well as with other contrasts offers a screening solution
with high anatomical resolution, free of ionizing radiation and,
eventually, free of Contrast Enhancing (CE) agents.
WB-MRI is a technological and clinical hotspot of research to improve the
state-of-the-art on various fronts. Some of the technologies are large
FoVs, parallel imaging with multiple coils having non-uniform
sensitivities, and large scanning times prone to motion artifacts. Quality
improvements of the imaging data require image restoration with effective
and efficient pre-processing steps. A basic step is image denoising while
at the same time preserving essential structures. These data often also
suffer from motion artifacts due to involuntary or voluntary patient motion
and require motion correction. WB intra-patient registration is also an
essential to compensate for the displacements between scans of the same
patient that are part of an imaging protocol.
WB-MRI images are large FoV data from multicoil acquisitions, inevitably
suffering from extensive intensity nonuniformity artifacts. This special
issue calls for corrections for smooth intensity uniformity for individual
coils, as well as for compensatory solutions for signal intensity and
chemical shift jumps at coil junctions. That is, these data require
appropriate combined intensity uniformity restorations.
This special issue intends to provide a platform on innovative WB-MRI
pre-processing steps to improve the quality of the data as well as to
improve the conspicuity of the findings. This can perhaps obviate the need
for contrast enhancement (CE) agents during imaging. Additional information
that can also obviate the need for contrast enhancement can originate from
diffusion-weighted imaging (DWI). Hence, we are looking forward to
endorsing manuscripts on WB-DWI MRI processing.
The objective is to use the corrected imaging data for further analysis,
i.e., for providing a clinical report or for the final diagnosis of a
patient. The size and the amount of information in the data is not only
cumbersome to analyze manually, but limiting as well. It is preferable and
sometimes even necessary to perform computer analysis of the imaging data
to extract semantic information. One type of automated analysis can be for
detection, segmentation, and compartmentalization into tissue types
throughout the body. For example, into muscle, fat, and other tissues with
DIXON, to assess the effect of exercise on an athlete. Another example is
for pathology to achieve detection, volumetry and statistical quantitative
evaluation of organ lesions, especially in disseminated expressions,
impossible to achieve without computative support.
The time requirements both for image acquisition as well as for analysis
are high for Whole Body imaging. There is a need to expedite both. The
whole-body imaging has been expedited with parallel imaging, partial (half)
Fourier imaging, and other signal processing techniques such as compressed
sensing. These imaging and reconstruction technologies must be further
improved without at the same time ignoring the requirement to maintain a
high data quality.
The large size of the acquired datasets, approximately one gigabyte per
patient, sets challenging space and time requirements in data processing.
Rigid registration and other pre-processing and analysis steps are also
inherently time-demanding. This time is not necessarily available in the
clinical routine. Hence, there is a need for both efficient methodologies
as well as implementations. It is necessary to emphasize on the critical
steps in the processing and summarize the remaining. The methods must be
implemented efficiently; whenever possible parallelize them and implement
them with GP-GPUs.
Beyond the work of individual laboratories, the progress in this field can
also benefit from the collaboration between laboratories. To this end, this
issue is also inviting manuscripts for WB-MRI public databases openly
available to the community for analysis and evaluation. The databases can
consist from the typical imaging modalities that are T1w, T2w, IR, DIXON,
and diffusion as well as from other modalities. Furthermore, these
databases can enable cross-sectional studies for population analysis.
The objective of this issue is to emphasize the significance of WB-MRI and
improve the state-of-the-art on WB MRI data processing. To this end it
intends to consolidate the problem of WB-MRI reconstruction, restoration,
and analysis for clinical interpretation. Another future objective is to
have a “one-stop-shop” role for WB-MRI, with implementations that go beyond
bone staging and cover individual organs such as liver, pancreas, spleen or
even the gastrointestinal tract (GIT) and the lungs. The methodologies to
be investigated can be based on analytical signal and image processing as
well as on machine learning and neural networks. This special issue is
expected to develop novel methodologies in various aspects of the WB
processing problem that will benefit directly the extensive and efficient
patient examination.
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