Scott Edmunds

**Sheer Heart Attack**  
![](http://gigasciencejournal.com/blog/wp-content/uploads/2014/11/robotheart-1-275x300.jpg){.alignleft
.wp-image-2214 loading="lazy" decoding="async" width="238" height="260"
srcset="http://gigasciencejournal.com/blog/wp-content/uploads/2014/11/robotheart-1-275x300.jpg 275w, http://gigasciencejournal.com/blog/wp-content/uploads/2014/11/robotheart-1.jpg 338w"
sizes="(max-width: 238px) 100vw, 238px"}Diagnosis is key to beginning
treatment for preventing [coronary heart
disease](http://en.wikipedia.org/wiki/Coronary_artery_disease "CAD on wikipedia"){target="_blank"
rel="noopener noreferrer"}, the most common cause of heart attacks. One
useful tool in the fight against this leading killer is magnetic
resonance imaging, which allows the direct examination of blood flow to
the myocardium of the heart. However, for this perfusion analysis
technique to be the most effective requires compensation for the
breathing motion of the patient, which is done using complex image
processing methods. Thus, there is a need to improve these tools and
algorithms. The key to achieving things is the availability of large
publicly available
[MRI](http://en.wikipedia.org/wiki/Magnetic_resonance_imaging "Magnetic resonance imaging"){target="_blank"
rel="noopener noreferrer"} datasets to allow testing, optimization and
development of new methods.

In our [latest paper in
*GigaScience*](https://doi.org/10.1186/2047-217X-3-23 "Wollny MRI paper"){target="_blank"
rel="noopener noreferrer"}, researchers from Universidad Politécnica de
Madrid in Spain and the National Institutes of Health in the USA provide
a fantastic example of open data sharing to help build these exact
tools: a wealth of patient imaging data. Even better: to enable
reproducible comparisons between new tools, the researchers and journal
have taken the unusual step of publishing and packaging the data
alongside tools, scripts and the software required to run the
experiments. This is [available to
download](http://dx.doi.org/10.5524/100106 "MRI data DOI"){target="_blank"
rel="noopener noreferrer"} from our
[GigaDB](http://gigadb.org/ "GigaDB homepage"){target="_blank"
rel="noopener noreferrer"} database as a "virtual hard disk" that will
specifically allow researchers to directly run the experiments
themselves and to add their own annotations to the data set.

**Black Box (Ride on Time)?**  
With our goal to publish different shaped research objects to the
traditional static paper, on top of [publishing Galaxy
workflows](http://blogs.biomedcentral.com/gigablog/2014/02/06/rewarding-reproducibility-first-papers-in-our-galaxy-series-utilizing-our-gigagalaxy-platform/ "Galaxy series blog"){target="_blank"
rel="noopener noreferrer"} and [packages to recreate papers in
Knitr](http://blogs.biomedcentral.com/gigablog/2014/03/26/carmen-reproducible-research-and-push-button-papers/ "Knitr/dynamic doc blog"){target="_blank"
rel="noopener noreferrer"}, this new paper provides another great
example of a more reproducible and interactive way of disseminating
scientific research. While this approach makes it much easier for
reviewers and future users to immediate run applications. Allowing
reviewers and future users to reproduce the experiments described in a
paper through launching a virtual machine means there would be no need
to install complex, version-sensitive and inter-dependent prerequisite
software components. While this ability to quickly and easily recreate
the results of a study would seem to address many issues of
reproducibility, there are [arguments from some (including Titus
Brown)](http://ivory.idyll.org/blog/vms-considered-harmful.html "Titus black box blog"){target="_blank"
rel="noopener noreferrer"} that this reproducibility is achieved in a
not very useful way, and virtual machines are essentially black boxes
that aren't really reusable for remixing or mashing up the code. Helping
to address these arguments somewhat, the data (MRI scans from a series
of ten patients considered clinically to have a stress perfusion defect)
and the scripts and the software required to run the experiments are
provided separately to enable interested parties to directly run the
experiments themselves. The scripts to run the motion compensation
algorithms [are also available in
Sourceforge](http://sourceforge.net/p/tabseg/myoperfdata/ci/master/tree/ "Wollny sourceforge page"){target="_blank"
rel="noopener noreferrer"}, and as with all of our papers everything is
available under open source licenses.

[![Images_162](http://gigasciencejournal.com/blog/wp-content/uploads/2014/11/Images_162.jpg){.alignright
.size-full .wp-image-1370 loading="lazy" decoding="async" width="256"
height="192"}](http://gigasciencejournal.com/blog/wp-content/uploads/2014/11/Images_162.jpg)As
one potential user of these resources, [Professor Alistair
Young](https://unidirectory.auckland.ac.nz/profile/a-young "Alistair Young homepage"){target="_blank"
rel="noopener noreferrer"}, Technical Director of the Auckland Magnetic
Resonance Research Group (and *GigaScience* Editorial Board Member)
commented: "Very large amounts of medical imaging data are now becoming
available through registries and large population studies. Well
validated, automated methods are required to derive maximum benefit from
such resources. [The paper by Wollny and
Kellman](https://doi.org/10.1186/2047-217X-3-23 "Wollny MRI paper"){target="_blank"
rel="noopener noreferrer"} exemplifies how data and algorithm sharing
can advance the field by providing a platform by which existing methods
can be tested and new methods validated against existing benchmarks.
Such benchmarking datasets are essential to advance the field through
objective metrics and standards."

Having everything wrapped up in a virtual machine also made things
simpler during the scientific peer-review and publication process, as
the settings, packages and file locations were already set up in a
working configuration. One of the people carrying out this testing
process was *GigaScience* Data Scientist Rob Davidson, who stated
"Actually testing the code during review is sadly almost a novel concept
and one that needs to roll out as a standard. But even more: if it's
easy for the reviewers, it's easy for the community to use too."

As well as being important for improving the diagnosis for the [number
one cause of death world
wide](http://www.who.int/mediacentre/factsheets/fs310/en/ "WHO death stats"){target="_blank"
rel="noopener noreferrer"}, the continuing rise in retractions of
published scientific articles, makes the addition of direct means to
improve article reproducibility is essential, both for the ability to be
able to trust current findings ---on which future studies are built---
and to prevent the public losing confidence in the research community
they fund. Publishing a virtual machine, an interactive and executable
publication provides an example to the scientific community and test
case demonstrating a potential new type of scholarly output.

While we this is the first first myocardial MRI data we've published, we
have [recently
published](https://doi.org/10.1186/2047-217X-3-21 "Urchin MRI Data Note"){target="_blank"
rel="noopener noreferrer"} a large series of MRI scans of 98 sea urchin
species, as well as a [functional MRI dataset for motor, language and
spatial attention
function](https://doi.org/10.1186/2047-217X-2-6 "fMRI Data Note"){target="_blank"
rel="noopener noreferrer"}. We are currently collecting papers for a
[fMRI thematic series edited by our board member Russell
Polldrack](http://blogs.biomedcentral.com/gigablog/2014/10/19/guest-blog-challenges-and-opportunities-with-sharing-neuroimaging-data/ "Russ Polldrack series blog"){target="_blank"
rel="noopener noreferrer"}, so please contact us if you have data or
papers you are interested in publishing.

### **References**

[1.](https://doi.org/10.1186/2047-217X-3-23 "Wollny MRI paper"){target="_blank"
rel="noopener noreferrer"} Wollny, G; Kellman, P: Free breathing
myocardial perfusion data sets for performance analysis of motion
compensation algorithms. *GigaScience* 2014 **3**:23
<https://doi.org/10.1186/2047-217X-3-23>

[2.](http://dx.doi.org/10.5524/100106 "MRI data DOI"){target="_blank"
rel="noopener noreferrer"} Wollny, G; Kellman, P (2014): Supporting
material for: "Free breathingly acquired myocardial perfusion data sets
for performance analysis of motion compensation algorithms". GigaScience
Database.
[http://dx.doi.org/10.5524/100106](http://dx.doi.org/10.5524/100106 "MRI data DOI"){target="_blank"
rel="noopener noreferrer"}

The post [Publishing our first virtual box of delights to aid the fight
against heart
disease](http://gigasciencejournal.com/blog/publishing-our-first-virtual-box-of-delights-to-aid-the-fight-against-heart-disease/){rel="nofollow"}
appeared first on
[GigaBlog](http://gigasciencejournal.com/blog){rel="nofollow"}.


<strong>
 Sheer Heart Attack
</strong>
Diagnosis is key to beginning treatment for preventing coronary heart disease, the most common cause of heart attacks. One useful tool in the fight against this leading killer is magnetic resonance imaging, which allows the direct examination of blood flow to the myocardium of the heart.

Messaggi di Rogue Scholar

Publishing our first virtual box of delights to aid the fight against heart disease