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Nick Todd, Allison Payne,

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Nick Todd, Allison Payne, Douglas A. Christensen, Henrik Odeen, Dennis L. Parker Utah Center for Advanced Imaging Research, University of Utah

2 Background Utah Projects in MRI guided HIFU
Large animal MRgHIFU system (Siemens/IGT) Small animal MRgHIFU system (IGT) Breast MRgHIFU system (UofU/IGT/Siemens) See poster 4.8 by Allison Payne We appreciate the opportunity to present at this NCIGT workshop. During the past 10 years our involvement in MRI guided HIFU has increased and we currently have three functioning HIFU systems: Our original large animal

3 Background: Utah Projects
MR guided HIFU Breast: Develop the Utah Breast MRgHIFU system Brain Develop 3D MRI Temperature measurements for MRI guided Brain HIFU Temperature measurement requirements glandular tissues AND fat Near-field protection Brain: cover entire skull volume high temporal and spatial resolution MRI scanner Tumor

4 MR Temperature Basics - = Proton Resonance Frequency Shift (PRF).
MR signal frequency depends on local chemical environment of water Hydrogen. Temperature changes affect this environment. Current Time Frame Reference Difference Temperature Map Frequency changes measured as image phase changes. - =

5 Breast: Temperature Measurements
3-point Dixon Images Requirements: Control treatment in glandular tissue Avoid fat necrosis Coverage, speed, and resolution Temperature in water and fat? Hybrid PRF/T1 method 2D GRE 2D/3D Segmented EPI Fat Water PRF temperature map T1 signal change map fat agar

6 MRI Thermometry - Breast
Hybrid PRF/T1 Signal from Spoiled GRE sequence: Image sequence: 2 alternating flip angles PRF from phase of each image T1 from two images Deoni, Rutt, Peters. Magn Reson Med :

7 Breast: Temperature Measurements
A) 3-Pt Dixon Water Image B) 3-Pt Dixon Fat Image C) PRF/T1 Magnitude Image D) PRF Temperature Map Pork Muscle Breast Fat Targeted Area Transducer

8 Breast: Temperature Measurements
T1 Percent Change in Breast Fat PRF Temperatures in Pork B) C) PRF/T1 Magnitude Image D) PRF Temperature Map Pork Muscle Breast Fat Targeted Area Transducer

9 Transcranial MRI guided HIFU
Funding: Focused Ultrasound Surgery Foundation NIH R01 EB013433

10 Transcranial MRI guided HIFU
Cover all heated regions: Skull + within Resolution Speed Coverage (FOV) 1mm isotropic 1s Full head/breast 205x160x100 TR=35, ETL=7: 80s 205x160x33 (1x1x3mm), TR=35, ETL=7: 27s Image Volume

11 Required Values Brain Spatial Resolution: 1 x 1 x 3 mm
Temporal Resolution: Volume Coverage: Signal - to - Noise: 1 x 1 x 3 mm 2 seconds per image 256 x 162 x 72 mm Brain Image Volume: 256 x 162 x 72 mm Image Volume

12 Transcranial MRI guided HIFU
How to go faster: 2D Spatially selective RF excitation Prefer full FOV Parallel imaging + UNFOLD1 Temporally Constrained Reconstruction (TCR)2 Model Predictive Filtering (MPF)3 1: Chang-Sheng Mei, et al. Magnetic Resonance in Medicine 66:112–122 (2011) 2: N. Todd et al. Magn Reson Med 62(2): (2009). 3: N. Todd, A. Payne, D. L. Parker, Magn Reson Med 63:1269–1279 (2010)

13 Data Acquisition & Reconstruction
F = Fourier Transform m = Image Estimate d’ = Undersampled Data ~ Data Space (k-space) Image Space Inverse Fourier Transform 256 x 162 x 24 pixels 256 x 162 x 24 pixels

14 Constrained Reconstruction
F = Fourier Transform m = Image Estimate d’ = Undersampled Data = Gradient in time ~ is iteratively updated subject to constraints: Image must match acquired data Image must change smoothly in time iteration = 5 iteration = 25 iteration = 50 iteration = 100

15 TCR: Constrained Reconstruction
Sequence Parameters 1.5 x 2 x 3 mm 288 x 216 x 108 mm 192 x 108 x 36 matrix EPI Factor: 7 lines per excitation TR/TE = 35 / 9 ms Data Undersampling ky For a typical HIFU heating, kz Constrained Reconstruction Scan Time: 1.8 s / time frame 25 s / full data set Not real time

16 Constrained Reconstruction Results
Validation Tests: “Truth”: Full Data used 1.5 x 1.5 x 3.0 mm 2.8 seconds per image 288 x 162 x 24 mm Test Cases: 288 x 162 x 48 mm 288 x 162 x 90 mm 288 x 162 x 144 mm Full Data 2.8 s “Truth” 5.4 s 10.1 s 16.2 s Constrained Reconstruction 6X data reduction 2.8 s “Truth” 0.9 s 1.7 s 2.7 s

17 Model Predictive Filtering
Thermal Model Artifact-free Temperature maps Undersampled k-space Goal: real time N. Todd, A. Payne, D. L. Parker, MRM 63:1269–1279 (2010)

18 Model-Predictive Filtering
Segment tissues Determine tissue-specific thermal and acoustic properties TCR + Modeling Use tissue-specific properties in dynamic MPF temperature measurements Realtime, 3D, large FOV From highly undersampled 3D segmented EPI PRF

19 Tissue Segmentation Breast tissue segmentation
Hierarchal Support Vector Machine algorithm Non-FS T1 FS T2-w FS PD-w 3pt Dixon H2O only 3pt Dixon Fat only Another aspect to the grant is to improve the planning, monitoring and assessment of HIFU treatments. In this example Yi Wang has implemented a h-SVM algorithm to segment the breast into multiple tissue types. This segmentation aids both the thermometry and US modeling efforts needed to accurately plan a treatment. The algorithm requires five multi-parametric images as inputs to the algorithm. The results show that using the h-SVM algorithm after interpolating the data give a more accurate segmentation than c-SVM, FCM or h-SVM with no interpolation. This accuracy was confirmed both visually and statistically by a breast radiologist. h-SVM w/ Zero-Filled-Interpolation

20 Tissue property estimation: Acoustic parameters
Segment treatment volume into a small number of tissue types 4-8 low power pulses cover targeted volume TCR – reconstruct temperature images In-vivo estimates of the change in the attenuation coefficient with log10 of thermal dose using the iterative parameter estimation technique . Urvi Vyas et al. ISTU 2011 MR temps to get SAR patterns Use ultrasound model (HAS) to determine absorption and speed of sound to match measured pattern Tissue acoustic values for Model Predictive Filtering.

21 Tissue property estimation: Thermal parameters
Segment treatment volume into a small number of tissue types 4-8 low power pulses cover targeted volume TCR – reconstruct temperature images MRI temps during cooling Determine thermal diffusivity using cooling temperature curves Cheng et al., JMRI 16(5), 2002

22 Hybrid Angular Spectrum (HAS): Pressure Modeling

23 HAS SAR prediction

24 HAS: Head Model Courtesy: Guido Gerig, University of Utah



27 Model Predictive Filtering
Multi-step, recursive algorithm 3 2 Phase (n+1) 1 3 Temp (n) Temp (n+1, model) K-space (n+1) 5 Magnitude (n) Step 1: Use model to predict temperature at time n+1. Step 2: Convert temperature map to phase map for time n+1. Step 3: Use this phase and the magnitude from time n to create k-space for time n+1. Step 4: Insert any actually acquired k-space lines. Step 5: Recalculate the temperature for time n+1 using the data updated k-space. Temp (n+1, model and data)

28 Model Predictive Filtering
Use the Pennes Bioheat Equation, tissue properties, and a pre-treatment heating to determine the thermal model. Full Data Model Only T = temperature r = density C = tissue and blood heat capacity k = thermal conductivity Wb = blood perfusion Q = heat applied

29 2-D MPF Results Fully sampled k-space data sets: 288x288x20mm FOV, 2.3x2.3x4mm res, 8.3 sec/scan. 25% of k-space used in reconstruction. Power = 36W (Model Id data set) Mean and STD of error over an ROI MPF Power = 42W Mean and STD of error over an ROI MPF Power = 48W Mean and STD of error over an ROI MPF

30 3D (R=12) vs 2D (R=1) MPF Temperatures
Common: Ultrasound pulse = 36 W/58.1 sec 3-D GRE: FOV = 256x256x32 mm3, Matrix = 128x128x16 Resolution = 2.0x2.0x2.0 mm3 TR/TE = 25/8 ms Tacq = 76.8 s/image volume (R=1) = 6.4 s/image volume (R=12.1) 2D GRE: FOV = 256x256x20 mm (sl = 3mm) Matrix = 128x128 Resolution = 2.0x2.0x3.0 mm3 TR/TE = 65/8 ms; 8.3 sec per scan (R=1) Scans repeated 8x for variability N. Todd, A. Payne, D. L. Parker, MRM 63:1269–1279 (2010)

31 Model Predictive Filtering Results
Phantom Heating 2.0 x 2.0 x 2.0 mm 0.5 seconds per image 256 x 162 x 48 mm σT < 1°C Transverse: Sagital: Coronal:

32 Summary: Work in Progress
Brain requires: Large FOV: Cover insonified volume High speed: 1s/volume High resolution: < 1 x 1 x 3 mm3 Our solutions: PRF: Highly undersampled (>8) 3D segmented EPI TCR: Does not require tissue thermal and acoustic properties Achieves high spatial and temporal resolution, large FOV, LOW NOISE! Cannot (yet) be performed in real time Model-predictive Filtering (MPF) Requires tissue segmentation estimate of tissue acoustic and thermal properties Property estimates: SAR: Hybrid Angular Spectrum (HAS) Diffusivity/Perfusion: MRI during cooling Also achieves high spatial and temporal resolution, large FOV, LOW NOISE! Potential real time application Parallel imaging Can be used to supplement TCR or MPF Difficult with currently used HIFU coils Just to briefly summarize:

33 Acknowledgments Thank You People: Funding: Yi Wang Dennis Parker
Urvi Vyas Bob Roemer Emilee Minalga Joshua de Bever Doug Christensen Leigh Neumayer Chris Dillon Allison Payne Joshua Coon Justin Tidwell Nick Todd Rock Hadley Lexi Farrer Nelly Volland Robb Merrill Mahamadou Diakite Henrik Odeen Funding: Focused Ultrasound Surgery Foundation Siemens Medical Solutions NIH grants F31 EB A1, R01 EB013433, and R01 CA

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