Paper Presentation 03/04/2010

Transcription

1 Paper Presentation 03/04/2010 Tracked Regularized Ultrasound Elastography for Targeting Breast Radiotherapy Hassan Rivaz, Pezhman Foroughi, Ioana Fleming, Richard Zellars, Emad Boctor, and Gregory Hager Project : GPU Elastography Nishikant Deshmukh [External: Hyun Jae Kang, Philipp Stolka] Mentors: Emad Boctor, Mohamad Alaf Aim: Improve speed of NCC based Elastography. Implement AM + DP based Elastography for better quality. (AM = Analytic Minimization + DP = Dynamic Programming) Integrate with Da Vinci for minimally invasive Prostatectomy. System Block Diagram: Figure 1: Figure 1 shows overall system block diagram of our system for real-time GPU Elastography algorithm. An RF Data Generator reads RF signals from Ultrasound probe and then passes this data to a data distributor which in turn distributes it across to NCC Elastography Module and DP/AM Elastography module. This data then is passed to a GUI which visualizes data and gives feedback to each module. The feedback mechanism is kept for parameter passing and in case DP/AM is slower than NCC then we want to pass area of interest to this DP/AM Elastography module, so that only specific area of the RF data is covered by this DP/AM Method. This area will be much smaller compared to what NCC does.

2 Advantages/Significance of using AM/DP: NCC sensitive and accurate for small displacement. NCC is expensive for larger displacement. NCC is highly sensitive to motion and even a shiver of hand can make and break the images. AM/DP gives sub pixel displacement. Earlier the DP algorithm gave only integer level displacement. Finer sub pixel displacement using techniques like generating strain images from a pair of strain images is quiet expensive. Variable regularization applied. Regularization value changes per RF line depending on the input parameters. Tackles motion of sub resolution scatters, out of plane motion, high compression and complex fluid motions. Works at depth where SNR decreases. Low correlation due to complex motion near arteries and inside of vessels due to blood motion can be handled. Handles low correlation in lesions with liquid inside it. Out-of-plane motion of movable structures within the image is handled. Freehand palpation is more robust. Minimizes displacement underestimation caused by smoothness constraints. Uncorrelated ultrasound data can be treated as outliers and corrected. AM/DP combination is more parallelizable than simple DP algorithm. Because AM introduces sub pixel displacement which compensates for lack of feedback from cost function from adjacent RF line in DP.

3 DP/AM Elastography: Figure 2 Figure 3

4 The incoming RF Data is passed onto the Dynamic programming Elastography, which is calculated using following formula. Cost function is minimized at i = m and the d i values that minimized the cost function are tracked back to i = 1, giving d i for all samples in axial direction. This displacement is then feed to Analytical Minimization method, which is calculated using formula. We solve the above equation for d,so that we can get d + d. D is matrix with shifting right with each row. Now,

5 Figure 4 Figure 4 shows the effect of applying biased regularization to the output. The dotted line represents underestimation of the curve and solid line is the correction. The comparison should be done between the same colors. Now sometimes few out of range or uncorrelated RF data can act as noise. To correct them we do Iterated reweighted estimation. 2 t. p. ( wi' 2 ad li ˆ) d wi' 2 e ( ad li ˆ) d ld b, w ( diag ( w( r1 ).. w( rm )) i Small value of T may discard many good sampling points. 1( i) I 2( i di ) I' 2 ( i di di r I ) The above equation gives us the least square estimation which we also call as strain images.

6 Results Figure 5 Figure 5 shows the comparison of output from the strain image we generated. The arrow in 5.a shows a CT scan of cavity of the tumor that has been removed. 5.b shows B mode image with no visible image and 5.c shows output using AM/DP method clearly showing cavity despite of low correlation inside the cavity. Figure 6: Finite element strain Figure 7: Finite element Mesh The author has simulated the phantom data by using Finite Element mesh having homogenous and isotropic material. The compression is applied using ABAQUS finite element package. This is done with the help of 10 5 scatterers in the simulated phantom image.

7 Figure 8: SNR vs regularization The author has shown decrease in SNR when IRLS is not applied, which shows that using IRLS helps in removing outliers. Application for the Project: By applying AM/DP to individual RF lines in axial direction we can get the required strain images. This can be achieved by ignoring the components in AM/DP formula where we depend on previous cost function or RF lines. Better quality image than NCC because of lower noise and less sensitive to motion. Summary of paper

8 Positive: Shortcomings Tracking information used to choose appropriate frames and discard out of plane motion frames. Very nice result section with simulation of phantom results found matching with exact location of tumor in the simulator. Thorough explanation of AM method. Some nice images. The flow of ideas is nice. Useful for freehand palpation. Difficult to understand. Need to explain method diagrammatically. Not so thorough explanation of 2D DP method. In biased regularization epsilon value can be calculated from DP method. Did not address noise due to echoing problem from bottom surface. Most of the advantages are restated in confusing manner. Conclusion: The author has done good research on identifying the problem with free hand palpation and how tracking information is useful to get rid of out of plane motion probe problems. The use case and results section is quiet thorough. For our project we can use AM/DP method applied to each individual RF lines since AM provides the sub pixel displacement and will correct the errors in DP. The AM/DP properties will be there but only in axial direction. Reading List: Tracked Regularized Ultrasound Elastography for Targeting Breast Radiotherapy; Hassan Rivaz, Pezhman Foroughi, Ioana Fleming, Richard Zellars, Emad Boctor, and Gregory Hager. GPU-Based Elasticity Imaging Algorithms; Nishikant Deshmukh, Hassan Rivaz, Emad Boctor Ultrasound Elastography: A Dynamic Programming Approach; Hassan Rivaz*, Emad Boctor, Pezhman Foroughi, Richard Zellars, Gabor Fichtinger, and Gregory Hager