NAMIC Wiki - User contributions [en]

OpenIGTLink/ProtocolV2/Type/Image

2010-07-06T21:14:42Z

Bmschwar: Clarify definition of the vector type, approved by Dr. Tokuda

[[OpenIGTLink/ProtocolV2/Draft | << Version 2 Draft Page]]

=Summary=
The IMAGE format supports 2D or 3D images with metric information including image matrix size, voxel size, coordinate system type, position, and orientation. The body section of the IMAGE data consists of two parts: image header to transfer the metric information and image body to transfer the array of pixel or voxel values. The data type of pixel or voxel can be either scalar or vector, and numerical values can be 8-, 16-, 32-bit integer, or 32- or 64-bit floating point. The pixel values can be either big-endian or little-endian, since the sender software can specify the byte order in the image header. The format also supports “partial image transfer”, in which a region of the image is transferred instead of the whole image. This mechanism is suitable for real-time applications, in which images are updated region-by-region. The sub-volume must be box-shaped and defined by 6 parameters consisting of the indices for the corner voxel of the sub-volume and matrix size of the sub-volume.

=Message Types=
==IMAGE==

{| border="1" cellpadding="5" cellspacing="0" align="center"
|-
| align="left style="background:#e0e0e0;" | Data
| align="left style="background:#e0e0e0;" | Type
| align="left style="background:#e0e0e0;" | Description
|-
| align="left" | V
| align="left" | unsigned short
| align="left" | version number
|-
| align="left" | T
| align="left" | 8bit unsigned int
| align="left" | Number of Image Components (1:Scalar, >1:Vector). (NOTE: Vector data is stored fully interleaved.)
|-
| align="left" | S
| align="left" | 8bit unsigned int
| align="left" | Scalar type (2:int8 3:uint8 4:int16 5:uint16 6:int32 7:uint32 10:float32 11:float64)
|-
| align="left" | E
| align="left" | 8bit unsigned int
| align="left" | Endian for image data (1:BIG 2:LITTLE) (NOTE: values in image header is fixed to BIG endian)
|-
| align="left" | O
| align="left" | 8bit unsigned int
| align="left" | image coordinate (1:RAS 2:LPS)
|-
| align="left" | RI, RJ, RK
| align="left" | 16 bit unsigned int
| align="left" | Number of pixels in each direction
|-
| align="left" | PX, PY, PZ
| align="left" | 32 bit float
| align="left" | center position of the image
|-
| align="left" | TX, TY, TZ
| align="left" | 32 bit float
| align="left" | Transverse vector (direction for 'i' index) / The length represents pixel size in 'i' direction
|-
| align="left" | SX, SY, SZ
| align="left" | 32 bit float
| align="left" | Transverse vector (direction for 'j' index) / The length represents pixel size in 'j' direction
|-
| align="left" | NX, NY, NZ
| align="left" | 32 bit float
| align="left" | Normal vector of image plane(direction for 'k' index) / The length represents pixel size in 'z' direction or slice thickness
|-
| align="left" | DI, DJ, DK
| align="left" | 16 bit unsigned int
| align="left" | Starting index of subvolume
|-
| align="left" | DRI, DRJ, DRK
| align="left" | 16 bit unsigned int
| align="left" | number of pixels of subvolume
|-
| align="left" | IMAGE_DATA
| align="left" | Binary image data ()
| align="left" | Image data (endian is determined by "E" field)
|-
|}

==GET_IMAGE==

==STT_IMAGE==

==STP_IMAGE==

=Implementations=
IMAGE type is implemented in the following files:
*[http://svn.na-mic.org/NAMICSandBox/trunk/OpenIGTLink/Source/igtlImageMessage.h igtlImageMessage.h]
*[http://svn.na-mic.org/NAMICSandBox/trunk/OpenIGTLink/Source/igtlImageMessage.cxx igtlImageMessage.cxx]

=Contributors=
This message type was originally proposed in version 1 protocol.

=Comments=

2010 Winter Project Week MRI Reconstruction by Registration

2010-01-08T16:43:57Z

Bmschwar:

__NOTOC__
<gallery>
Image:Ultrasound_lookup_prototype.png
Image:MRI_sorted_recon.png
</gallery>

==Key Investigators==
* BWH: Ben Schwartz
* BWH: Sandy Wells

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
I am investigating the use of feedback between MRI reconstruction and image registration to deduce both motion parameters and anatomy from a single dataset. My objective for the week is to demonstrate a connected chain from initial reconstruction -> image registration -> new k-space positions -> new reconstruction.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>
Start with initial image sequence (~40 Fiesta images of a moving tomato, tagged with a position parameter). Perform rigid registration between all images in the sequence, using some existing toolbox. Assign registration parameters to the center-of-kspace moment. Fit a curve to translate the position parameter into registration parameters. Evaluate this curve to determine the registration parameters for each line of k-space. Use these deformations to evaluate the true k-space locations, and perform non-uniformly sample reconstruction using some existing toolbox. Repeat as necessary.

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>
Day 0: I have a dataset, on which initial reconstruction has not yet been performed. This dataset represents images of a tomato moving in a curved path in-plane, taken with Fiesta. Every MRI echo is tagged with a scalar position parameter that is roughly monotone and smooth with position, but not precisely proportional.

Day 1: I performed reconstruction of MRI data according to the position ordering from ultrasound. I learned, from Sandy Wells, the appropriate tools for registering this dataset (ITK, possibly congealing using first-order B-spline as affine).

Day 2-4: Attempt to install ITK. Learn about the structure of the ITK python bindings and WrapITK from Luis Ibañez. Join the ITK mailing list to ask build questions.

Day 5: First working prototype of ultrasound signal database search.

</div>

</div>

<div style="width: 97%; float: left;">

==References==
</div>

File:MRI sorted recon.png

2010-01-08T16:43:26Z

Bmschwar: MRI images of a moving tomato, reconstructed at 40 points in space according to an ultrasound-derived position phase parameter by fusing data from many cycles of motion.

MRI images of a moving tomato, reconstructed at 40 points in space according to an ultrasound-derived position phase parameter by fusing data from many cycles of motion.

File:Ultrasound lookup prototype.png

2010-01-08T16:37:17Z

Bmschwar: First plot from a simple prototype lookup of 500 random points, comparing against 50 anchors.

First plot from a simple prototype lookup of 500 random points, comparing against 50 anchors.

2010 Winter Project Week MRI Reconstruction by Registration

2010-01-08T16:13:54Z

Bmschwar:

__NOTOC__
<gallery>
Image:PW-SLC2010.png|[[2010_Winter_Project_Week#Projects|Projects List]]
</gallery>

==Key Investigators==
* BWH: Ben Schwartz
* BWH: Sandy Wells

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
I am investigating the use of feedback between MRI reconstruction and image registration to deduce both motion parameters and anatomy from a single dataset. My objective for the week is to demonstrate a connected chain from initial reconstruction -> image registration -> new k-space positions -> new reconstruction.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>
Start with initial image sequence (~40 Fiesta images of a moving tomato, tagged with a position parameter). Perform rigid registration between all images in the sequence, using some existing toolbox. Assign registration parameters to the center-of-kspace moment. Fit a curve to translate the position parameter into registration parameters. Evaluate this curve to determine the registration parameters for each line of k-space. Use these deformations to evaluate the true k-space locations, and perform non-uniformly sample reconstruction using some existing toolbox. Repeat as necessary.

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>
Day 0: I have a dataset, on which initial reconstruction has not yet been performed. This dataset represents images of a tomato moving in a curved path in-plane, taken with Fiesta. Every MRI echo is tagged with a scalar position parameter that is roughly monotone and smooth with position, but not precisely proportional.

Day 1: I performed reconstruction of MRI data according to the position ordering from ultrasound. I learned, from Sandy Wells, the appropriate tools for registering this dataset (ITK, possibly congealing using first-order B-spline as affine).

Day 2-4: Attempt to install ITK. Learn about the structure of the ITK python bindings and WrapITK from Luis Ibañez. Join the ITK mailing list to ask build questions.

Day 5: First working prototype of ultrasound signal database search.

</div>

</div>

<div style="width: 97%; float: left;">

==References==
</div>

2010 Winter Project Week MRI Reconstruction by Registration

2010-01-08T16:11:49Z

Bmschwar:

__NOTOC__
<gallery>
Image:PW-SLC2010.png|[[2010_Winter_Project_Week#Projects|Projects List]]
</gallery>

==Key Investigators==
* BWH: Ben Schwartz
* BWH: Sandy Wells

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
I am investigating the use of feedback between MRI reconstruction and image registration to deduce both motion parameters and anatomy from a single dataset. My objective for the week is to demonstrate a connected chain from initial reconstruction -> image registration -> new k-space positions -> new reconstruction.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>
Start with initial image sequence (~40 Fiesta images of a moving tomato, tagged with a position parameter). Perform rigid registration between all images in the sequence, using some existing toolbox. Assign registration parameters to the center-of-kspace moment. Fit a curve to translate the position parameter into registration parameters. Evaluate this curve to determine the registration parameters for each line of k-space. Use these deformations to evaluate the true k-space locations, and perform non-uniformly sample reconstruction using some existing toolbox. Repeat as necessary.

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>
Day 0: I have a dataset, on which initial reconstruction has not yet been performed. This dataset represents images of a tomato moving in a curved path in-plane, taken with Fiesta. Every MRI echo is tagged with a scalar position parameter that is roughly monotone and smooth with position, but not precisely proportional.
Day 1: I performed reconstruction of MRI data according to the position ordering from ultrasound. I learned, from Sandy Wells, the appropriate tools for registering this dataset (ITK, possibly coherence using first-order B-spline as affine).
Day 2-4: Attempt to install ITK. Learn about the structure of the ITK python bindings and WrapITK from Luis Ibañez. Join the ITK mailing list to ask build questions.
Day 5: Prototype ultrasound signal database search.

</div>

</div>

<div style="width: 97%; float: left;">

==References==
</div>

2010 Winter Project Week

2009-12-30T03:16:02Z

Bmschwar: /* Registration */

Back to [[Project Events]], [[AHM_2010]], [[Events]]

__NOTOC__
[[Image:PW-SLC2010.png|300px]]

==Background==

From January 4-8, 2010, the tenth project week for hands-on research and development activity in Image-Guided Therapy and Neuroscience applications will be hosted in Salt Lake City, Utah. Participant engange in open source programming using the [[NA-MIC-Kit|NA-MIC Kit]], algorithms, medical imaging sequence development, tracking experiments, and clinical applications. The main goal of this event is to further the translational research deliverables of the sponsoring centers ([http://www.na-mic.org NA-MIC], [http://www.ncigt.org NCIGT], [http://nac.spl.harvard.edu NAC], [http://catalyst.harvard.edu/home.html Harvard Catalyst], and [http://www.cimit.org CIMIT]) and their collaborators by identifying and solving programming problems during planned and ad hoc break-out sessions.

Active preparation for this conference begins with a kick-off teleconference. Invitations to this call are sent to members of the sponsoring communities, their collaborators, past attendees of the event, as well as any parties expressing an interest in working with these centers. The main goal of the initial teleconference is to gather information about which groups/projects would be active at the upcoming event to ensure that there were sufficient resources available to meet everyone's needs. Focused discussions about individual projects are conducted during several subsequent teleconferences and permits the hosts to finalize the project teams, consolidate any common components, and identify topics that should be discussed in break-out sessions. In the final days leading up to the meeting, all project teams are asked to complete a template page on the wiki describing the objectives and research plan for each project.

On the first day of the conference, each project team leader delivers a short presentation to introduce their topic and individual members of their team. These brief presentations serve to both familiarize other teams doing similar work about common problems or practical solutions, and to identify potential subsets of individuals who might benefit from collaborative work. For the remainder of the conference, about 50% time is devoted to break-out discussions on topics of common interest to particular subsets and 50% to hands-on project work. For hands-on project work, attendees are organized into 30-50 small teams comprised of 2-4 individuals with a mix of multi-disciplinary expertise. To facilitate this work, a large room is setup with ample work tables, internet connection, and power access. This enables each computer software development-based team to gather on a table with their individual laptops, connect to the internet, download their software and data, and work on specific projects. On the final day of the event, each project team summarizes their accomplishments in a closing presentation.

A summary of all past NA-MIC Project Events is available [[Project_Events#Past|here]].

== Dates.Venue.Registration ==

Please [[AHM_2010#Dates_Venue_Registration| click here for Dates, Venue, and Registration]] for this event.

== Agenda==

Please [[AHM_2010#Agenda|click here for the agenda for AHM 2010 and Project Week]].

== Modules and extensions==

* [[Media:3DSlicer-Modules%2BExtensions-2009-11-27.ppt|Overview]]
* [http://wiki.slicer.org/slicerWiki/index.php/Documentation-3.5#Requirements_for_Modules Requirements for modules]
* [http://wiki.slicer.org/slicerWiki/index.php/Documentation-3.5#Introduction User-side explanations]
* [http://wiki.slicer.org/slicerWiki/index.php/Slicer3:Extensions Developer-side explanations]

==Projects==

=== Segmentation ===
#[[2010_Winter_Project_Week_Spine_Segmentation_Module_in_Slicer3|Spine Segmentation Module in Slicer3]] (Martin Loepprich, Sylvain Jaume, Polina Golland, Ron Kikinis, Steve Pieper)
#[[2010_Winter_Project_Week_The_Vascular_Modeling_Toolkit_in_3D_Slicer|The Vascular Modeling Toolkit in 3D Slicer]] (Daniel Haehn, Luca Antiga, Steve Pieper)
#[[2010_Winter_Project_Week_RobustStatisticsDrivenActiveContourSegmentation|Active contour segmentation using robust statistics]] (Yi Gao, Allen Tannenbaum, GT; Andriy Fedorov, Katie Hayes Ron Kikinis, BWH)
#[[2010_Winter_Project_Week_SegmentationWizard|High Level Wizard for Segmentation of Images]] (Mark Scully, Jeremy Bockholt, Steve Pieper)
#[[2010_Winter_Project_Week_LongitudinalLupusAnalyses|Longitudinal Analyses of Lesions in Lupus]] (Mark Scully, Jeremy Bockholt, Steve Pieper)
#[[2010_Winter_Project_Week_MultiscaleLupusAnalyses|Multiscale Analyses of Lupus Patients]] (Mark Scully, Jeremy Bockholt, Steve Pieper)
#[[2010_Winter_Project_Week_ProstateSeg|Prostate segmentation using shape-based method]] (Andras Lasso, Gabor Fichtinger, Yi Gao, Allen Tannenbaum, Andriy Fedorov)
#[[2010_Winter_Project_Week_TubularTreeSeg|Tubular Tree Segmentation for brain and cardiac imagery]] (Vandana Mohan, Allen Tannenbaum, GT; Marek Kubicki, BWH)
#[[2010_Winter_Project_Week_SegmentationEpicardialWall|Epicardial Wall Segmentation]] (Behnood Gholami, Yi Gao, Allen Tannenbaum, GT; Rob MacLeod, Josh Blauer, University of Utah)
#[[2010_Winter_Project_Week_SegmentationMeshEmbeddedContours|Segmentation on Mesh Surfaces Using Geometric Information]] (Peter Karasev, Matias Perez, Allen Tannenbaum, GT; Ron Kikinis, BWH)
#[[2010_Winter_Project_Week_TBISegmentation|Segmentation of TBI (Traumatic Brain Injury) Subjects from Multimodal MRI]] (Marcel Prastawa, Guido Gerig, Ron Kikinis)
#[[2010_Winter_Project_Week_Cardiac_Ablation_Scar_Segmentation|Cadiac Ablation Scar Segmentation]] (Michal Depa, Polina Golland, Ehud Schmidt, Ron Kikinis)
#[[2010_Winter_Project_Week_Musco_Skeletal_Segmentation | Knee Segmentation]] (Harish Doddi, Saikat Pal, Luis Ibanez, Scott Delp)

=== Registration ===
#[[2010_Winter_Project_Week_RegistrationCaseLibrary|The 3DSlicer Registration Case Library Project]] (Dominik Meier, Casey Goodlett, Ron Kikinis)
#[[2010_Winter_Project_Week_RegistrationInfrastructure|Improvements to the infrastructure for Registration in Slicer]] (Casey Goodlett, Dominik Meier, Ron Kikinis)
#[[2010_Winter_Project_Week_Deformation_Field_Visualization|Deformation Field and Tensor Visualization]] (Garrett Larson, Martin Styner)
#[[2010_Winter_Project_Week_ThalamicNucleiAtlas | Fusion of Anatomy,MRI and Electrophysiology in Parkinson's]] (Andrzej Przybyszewski, Dominik Meier, Ron Kikinis)
#[[2010_Winter_Project_Week_MRI_Reconstruction_by_Registration | MRI Reconstruction by Registration]] (Ben Schwartz)

=== IGT ===
#[[Tissue_Dependent_Registration|Registration with Varying Elastic Parameters for Tumor Resection]] (Petter Risholm, Sandy Wells)
# [[2010_Winter_Project_Week_Fast_Imaging_Library_%2B_Siemens_EPI]] (Scott Hoge, Nick Todd, Dennis Parker, Katie Hayes)
# [[2010_Winter_Project_Week_MRI_Guided_Robotic_Prostate_Intervention| MRI-guided Robotic Prostate Intervention]] (Andras Lasso and Junichi Tokuda)
#[[ 2010_Winter_Project_Week_WM_ATLAS|Atlas-Based White Matter Segmentation for Neurosurgical Planning]] (Lauren O'Donnell, C-F Westin, Alexandra J. Golby)
# [[ 2010_Winter_Project_Week_testbed|Testbed for Evaluation, Comparison, and Parameter Exploration for 3D Registration]] (James Fishbaugh, Guido Gerig)

=== Radiotherapy ===
# [[2010_Winter_Project_Week_DicomRT_Plugin|DicomRT plugin for Slicer]] (Greg Sharp, others)
# [[Adaptive Radiotherapy for Head, Neck, and Thorax]] (Ivan Kolesov, Vandana Mohan, Greg Sharp, Allen Tannenbaum )

=== Analysis ===
#[[2010_Winter_Project_Week_VervetMRILongitudinalAnalysis|Vervet MRI Longitudinal Analysis]] (Andriy Fedorov, Ron Rikinis, Ginger Li, Chris Wyatt)
#[[2010_WinterProject_Week_MRSIModule|MRSI Module]] (Bjoern Menze, Polina Golland)
#[[2010_WinterProject_Week_CorticalThicknessAnalysis|Cortical thickness analysis]] (Clement Vachet, Heather Cody Hazlett, Martin Styner)
#[[2010_WinterProject_Week_XNATUseforPopulationAnalysis|XNAT Use for Population Analysis]] (Corentin Hamel, Martin Styner, Clement Vachet)

=== Informatics ===
#[[2010_Winter_Project_Week_XND|XNAT Desktop User Interface]] (Dan M, Wendy P, Ron K)
#[[2010_Winter_Project_Week_Slicer_XNAT|Slicer 3 XNAT Performance Tuning]] (Wendy P, Dan M, Tim Olson, Nicole Aucoin)
#[[2010_Winter_Project_Week_catalyst|Harvard CTSC XNAT]] (Yong Gao, Dan M, Tim Olson, John Paulett)
#[[2010_Winter_Project_Week_xnatfs|xnatfs Integration into XNAT core]] (Dan Blezek, John Paulett, Tim Olsen)
#[[2010_Winter_Project_Week_OAWMB|Open Access Whole body CT/MR data set]] (Dan Marcus, Steve Pieper)
#[[2010_Winter_Project_Week_mComment | Annotation of Medical Images]] (Kilian Pohl, Yong Zhang, Nicole Aucion, Wendy Plesniak, Steve Pieper)

=== Diffusion ===
#[[ 2010_Winter_Project_Week_HARDI_RSH|Integration of Real Spherical Harmonic basis for HARDI models]] (Luke Bloy, C-F Westin)
#[[ 2010_Winter_Project_Week_Tractography|Filtered tractography]] (James Malcolm, Peter Savadjiev, Yogesh Rathi, C-F Westin, Casey Goodlett)
#[[ 2010_Winter_Project_Week_HARDI_CONNECTIVITY|Connectivity Study of Neonatal Brain Data using HARDI Techniques]] ( Yundi(Wendy) Shi, Deepika Mahalingam, Martin Styner )
#[[2010_Winter_Project_Week_TractographyPickingEditing|Tractography Picking and Bundle Editing]] (Jim Miller)
#[[ 2010_Winter_Project_Week_DTI_Fiber_Tract_Statistics|DTI Fiber-Tract Statistics]] (Anuja Sharma, Guido Gerig)

=== NA-MIC Kit Internals ===
#Testing for Extensions (Steve, Andre, Jim, Julien Jomier, Katie Hayes, Stuart Wallace)
#[[2010_Winter_Project_Week_SPECTRE_3DSlicer_Integration|Integration of SPECTRE Java module into 3D Slicer]] (Nicole Aucoin, Aaron Carass, Min Chen)
#[[2010_Winter_Project_Week_VTK_3D_Widgets_in_Slicer3|VTK 3D Widgets in Slicer3]] (Nicole Aucoin, Karthik, Will)
#[[2010_Winter_Project_Week_Slicer3_Colors_Module|Updates to Slicer3 Colors Module]] (Nicole Aucoin)
#CMAKE Build process (Dave Partyka, Katie Hayes)
#Integration of XNAT Packaging for Slicer Internals (Dan, Tim Olsen, Dave Partyka, Wendy, Randy)
#[[2010_Winter_Project_Week_Orthogonal_Planes_Issues|Orthogonal planes in reformat widget issues in Slicer3.5]] (Michal Depa, Steve Pieper)

(Other possibilities: Plotting, Layouts)

=== NA-MIC Outreach ===
#[[AHM 2010 Tutorial Polishing | Tutorial Polishing]] (Stuart Wallace, Randy Gollub, Sonia Pujol, all contributing tutorial contest developers)

=== Execution Model ===
# [[2010_Winter_Project_Week_Qt-ing the Command Line Module | Qt-ing the Command Line Module]] (Jim Miller)
# [[2010_Winter_Project_Week_Command Line Module Simple Return Types | Simple Return Types]] (Jim Miller)

=== Preparation ===

# Please make sure that you are on the [http://public.kitware.com/cgi-bin/mailman/listinfo/na-mic-project-week na-mic-project-week mailing list]
# Starting Thursday, October 15th, part of the weekly Thursday 3pm NA-MIC Engineering TCON will be used to prepare for this meeting. The schedule for these preparatory calls is as follows:
#*October 15: Engineering Infrastructure Projects
#*October 22: Funded External Collaboration Projects
#*October 29: Funded External Collaboration Projects
#*November 5: DPB Projects
#*November 19: DPB Projects
#*December 3: Other/new collaborations
#*December 10: Finalize Engineering Projects
#*December 17: Loose Ends
#By December 17, 2010: [[Project_Week/Template|Complete a templated wiki page for your project]]. Please do not edit the template page itself, but create a new page for your project and cut-and-paste the text from this template page. If you have questions, please send an email to tkapur at bwh.harvard.edu.
# By December 17, 2009: Create a directory for each project on the [[Engineering:SandBox|NAMIC Sandbox]] (Zack)
##[https://www.kitware.com/Admin/SendPassword.cgi Ask Zack for a Sandbox account]
## Commit on each sandbox directory the code examples/snippets that represent our first guesses of appropriate methods. (Luis and Steve will help with this, as needed)
## Gather test images in any of the Data sharing resources we have (e.g. the BIRN). These ones don't have to be many. At least three different cases, so we can get an idea of the modality-specific characteristics of these images. Put the IDs of these data sets on the wiki page. (the participants must do this.)
## Setup nightly tests on a separate Dashboard, where we will run the methods that we are experimenting with. The test should post result images and computation time. (Zack)
# Please note that by the time we get to the project event, we should be trying to close off a project milestone rather than starting to work on one...

2010 Winter Project Week MRI Reconstruction by Registration

2009-12-30T03:15:08Z

Bmschwar: Created page with '__NOTOC__ <gallery> Image:PW-SLC2010.png|Projects List </gallery> ==Key Investigators== * BWH: Ben Schwartz <div style="margin: 20px;"> <…'

__NOTOC__
<gallery>
Image:PW-SLC2010.png|[[2010_Winter_Project_Week#Projects|Projects List]]
</gallery>

==Key Investigators==
* BWH: Ben Schwartz

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
I am investigating the use of feedback between MRI reconstruction and image registration to deduce both motion parameters and anatomy from a single dataset. My objective for the week is to demonstrate a connected chain from initial reconstruction -> image registration -> new k-space positions -> new reconstruction.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>
Start with initial image sequence (~40 Fiesta images of a moving tomato, tagged with a position parameter). Perform rigid registration between all images in the sequence, using some existing toolbox. Assign registration parameters to the center-of-kspace moment. Fit a curve to translate the position parameter into registration parameters. Evaluate this curve to determine the registration parameters for each line of k-space. Use these deformations to evaluate the true k-space locations, and perform non-uniformly sample reconstruction using some existing toolbox. Repeat as necessary.

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>
Day 0: I have a dataset, on which initial reconstruction has not yet been performed. This dataset represents images of a tomato moving in a curved path in-plane, taken with Fiesta. Every MRI echo is tagged with a scalar position parameter that is roughly monotone and smooth with position, but not precisely proportional.

</div>

</div>

<div style="width: 97%; float: left;">

==References==
</div>

2009 Summer Project Week RTHawk MR Navigation

2009-06-26T04:09:47Z

Bmschwar: /* Key Investigators */

__NOTOC__
<gallery>
Image:PW2009-v3.png|[[2009_Summer_Project_Week#Projects|Projects List]]
</gallery>

==Key Investigators==
*BWH: Ben Schwartz, Scott Hoge, Renxin Xu, Jun-ichi Tokuda

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
I would like to use the RTHawk software library to implement MR navigation, using external position information in lieu of
navigator echoes.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>
Pester, nag.

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>
Over the course of this week, experts in GE's EPIC system have given me a tremendous amount of assistance, education, and advice. We have prepared a modified version of GE's 2DFAST sequence code that includes the scaffolding for real-time communication through the RTHawk system.

</div>
</div>

<div style="width: 97%; float: left;">

==References==

</div>

2009 Summer Project Week RTHawk MR Navigation

2009-06-22T17:19:00Z

Bmschwar: /* Key Investigators */

__NOTOC__
<gallery>
Image:PW2009-v3.png|[[2009_Summer_Project_Week#Projects|Projects List]]
</gallery>

==Key Investigators==
*BWH: Ben Schwartz, Scott Hoge, Renxin Xu

<div style="margin: 20px;">
<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Objective</h3>
I would like to use the RTHawk software library to implement MR navigation, using external position information in lieu of
navigator echoes.

</div>

<div style="width: 27%; float: left; padding-right: 3%;">

<h3>Approach, Plan</h3>
Pester, nag.

</div>

<div style="width: 40%; float: left;">

<h3>Progress</h3>
We have (1) a low-latency 1-D US position sensor that spits out OpenIGTLink transforms, (2) an OpenIGTLink->RTHawk proxy, and (3) a modified FGRE pulse sequence that accepts RTHawk position updates.

</div>
</div>

<div style="width: 97%; float: left;">

==References==

</div>

2008 IGT Project Week Dynamic control of the MRI acquisition

2008-12-12T01:29:08Z

Bmschwar: Add results in bullets

Back to [[2008_IGT_Project_Week#Projects]]

Team:Nathan McDannold, Ben Schwartz, Scott Hoge

Goals: Dynamic control of the MRI acquisition based on US-based signals. This would be to apply existing technologies developed for IGT such as optical-based tracking of a biopsy or ablation probe.

Needs:
# want to move FUS treatments to dynamic target (liver, kidney)

Approach:
# use ultrasound navigators
# how many sensors does one need?
# different ways to manipulate the MR scan

Results (to be completed and presented at the end of the week):

Over the course of this week, we
# discussed new algorithmic approaches to fast US registration, based on recent research in content-based image retrieval, dimensionality reduction, and sparse coding
# determined a way to connect the Realtime Data Server, MRMail, RTHawk, DARTS, 3D Slicer, OpenIGTLink, and other components into a system that is capable of altering image acquisition parameters with sufficiently low latency to allow navigation from US
# considered extensions of this concept to dynamic control of stereotactic radiotherapy and CT based on US navigation
# began packaging work on 3D Slicer to improve its integration with standard Linux operating systems, so that it may be included as a standard component.