motion-moderated transfer function for volume rendering 4d cmr data
TRANSCRIPT
Motion-Moderated Transfer Function for
Volume Rendering 4D CMR Data
(alt. title: what did rainbow colourmaps ever do to you?)
Simon Walton1 and Min Chen1 and Cameron Holloway2
1 Oxford University 2 St. Vincent's Hospital, Sydney
Overview
• Brief introduction to CMR
• Motivation for motion-moderated TFs
• What are motion-moderated TFs
• How we implemented them
• More Results
Cardiovascular MRI (CMR)
Cardiovascular MRI (CMR)• Essentially ECG-timed MRI
https://www.med-ed.virginia.edu/courses/rad/cardiacmr/Techniques/
Gating.html
beepbeepbeep
CINE Imaging
LV
RV
apex
mid
base
Slices often targeted
Colourmapping
low density high density
Problems with Colourmaps• The colour bands are mapped
to raw intensity
• Intensity values represent density and not 'tissue type'
• Myocardium density changes through systole and diastole
• Therefore, intensity-mapped colour alone cannot visually track tissue
t distant distractor
t+1
nearby distractor
target region
distant distractor
nearby distractor
target region
We wanted a solution which...
• ...could help alleviate density incoherence
• ...didn't require a new capture technique
• ...maintained the primary method of viewing
• ...could benefit 4D volume rendering generally
New Transfer Function?• Transfer function f(x) = t
• A couple of examples of transfer function input types in the literature:
• Size, visibility (Correa et al. 2008; 2009)
• Histogram (Caban et. al. 2008)
• Shape (Sato et al. 2000)
• How about motion?
Motion-Moderated Transfer Functions
A motion-moderated TF
• We can use motion information as input
• Can bring the attention to areas under motion
• Can aid with occlusion caused by static objects in the foreground
• Can aid identification of high-velocity areas
t1
t10
GradientIntensity Motion Mag
How to obtain motion metadata
• Phase-contrast velocity mapping
• Tagged CMR techniques (see right)
• However:
• We cannot rely on this
• We wish for generality
Optical Flow estimation• For generality we use
optical flow estimation
• Output is a 3D grid of vectors for each time step
Workflow
1 bu
ild compute motionintensity
t
intensityt-1
motion field
Intensity Field: original volume dataset Motion Field: generated by optical flow
Raycasting to 2D buffers
Intensity Field and
Motion Fieldcolour buffer
c: colourmap(intensity)α: motion mag
Motion Field
motion buffer
( )
Workflow
1 bu
ild compute motionintensity
t
intensityt-1
motion field
2 ra
ycas
t
accumulate colour
accumulate motion
Problem: noisy motion• The motion vectors from optical flow techniques
are usually noisy
• E.G. if we follow a region from left to right:
t0 t1 t2 t3 t4
• This can produce artefacts in the imagery
• Motion from frames t-1, t-2, etc can be used to smooth out the motion in t
++
t
t� 1
t� 2
t� 3+
Solution for noisy motion
Workflow
1 bu
ild compute motionintensity
t
intensityt-1
motion field
2 ra
ycas
t
accumulate colour
accumulate motion
3 co
mpo
se
temporal coherence
framebuffer
Results
intensity intensity gradient motion mag x
intensity
motion
motion x
intensity
motion x
intensity
motion2 x
intensity
Alpha from intensity values
Alpha from motion magnitude
Conclusion• We have introduced a transfer function utilising
motion information
• Such a technique can be generally applied
• Relies on an optical flow technique
• Clinical data often of reduced quality
• Research patients can be scanned for longer
Thanksfrom the heart (groan)