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IMPROVED MRI TEMPERATURE IMAGING USING A SUBJECT-SPECIFIC BIOPHYSICAL MODEL Nick Todd, Allison Payne, Douglas A. Christensen, Henrik Odeen, Dennis L. Parker.

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Presentation on theme: "IMPROVED MRI TEMPERATURE IMAGING USING A SUBJECT-SPECIFIC BIOPHYSICAL MODEL Nick Todd, Allison Payne, Douglas A. Christensen, Henrik Odeen, Dennis L. Parker."— Presentation transcript:

1 IMPROVED MRI TEMPERATURE IMAGING USING A SUBJECT-SPECIFIC BIOPHYSICAL MODEL Nick Todd, Allison Payne, Douglas A. Christensen, Henrik Odeen, Dennis L. Parker Utah Center for Advanced Imaging Research, University of Utah

2 UCAIR Utah Center For Advanced Imaging ResearchBackground Utah Projects in MRI guided HIFU Utah Projects in MRI guided HIFU Large animal MRgHIFU system (Siemens/IGT) Large animal MRgHIFU system (Siemens/IGT) Small animal MRgHIFU system (IGT) Small animal MRgHIFU system (IGT) Breast MRgHIFU system Breast MRgHIFU system (UofU/IGT/Siemens) (UofU/IGT/Siemens) See poster 4.8 by See poster 4.8 by Allison Payne

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

4 UCAIR Utah Center For Advanced Imaging Research 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 FrameReferenceDifferenceTemperature Map Frequency changes measured as image phase changes.

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

6 UCAIR Utah Center For Advanced Imaging Research 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 UCAIR Utah Center For Advanced Imaging Research Breast: Temperature Measurements A) 3-Pt Dixon Water Image B) 3-Pt Dixon Fat Image C) PRF/T1 Magnitude Image D) PRF Temperature Map Transducer Pork Muscle Breast Fat Targeted Area

8 UCAIR Utah Center For Advanced Imaging Research Breast: Temperature Measurements PRF Temperatures in PorkT1 Percent Change in Breast Fat B) C) PRF/T1 Magnitude Image D) PRF Temperature Map Transducer Pork Muscle Breast Fat Targeted Area

9 UCAIR Utah Center For Advanced Imaging Research Transcranial MRI guided HIFU Funding: Focused Ultrasound Surgery Foundation NIH R01 EB013433

10 UCAIR Utah Center For Advanced Imaging Research Transcranial MRI guided HIFU Cover all heated regions: Skull + within Cover all heated regions: Skull + within ResolutionSpeedCoverage (FOV) ResolutionSpeedCoverage (FOV) 1mm isotropic 1s Full head/breast 1mm isotropic 1s Full head/breast 205x160x100 TR=35, ETL=7: 80s 205x160x100 TR=35, ETL=7: 80s 205x160x33 (1x1x3mm), TR=35, ETL=7: 27s 205x160x33 (1x1x3mm), TR=35, ETL=7: 27s Image Volume

11 UCAIR Utah Center For Advanced Imaging Research Required Values Spatial Resolution: Temporal Resolution: Volume Coverage: Signal - to - Noise: 1 x 1 x 3 mm 2 seconds per image 256 x 162 x 72 mm Image Volume Image Volume: 256 x 162 x 72 mm Brain

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

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

14 UCAIR Utah Center For Advanced Imaging Research 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 = 25iteration = 50iteration = 100

15 UCAIR Utah Center For Advanced Imaging Research 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 ky Data Undersampling kz Constrained Reconstruction Scan Time: 1.8 s / time frame 25 s / full data set Not real time

16 UCAIR Utah Center For Advanced Imaging Research Constrained Reconstruction Results Full Data Constrained Reconstruction 6X data reduction 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 “Truth” 5.4 s 10.1 s 16.2 s 0.9 s 1.7 s 2.7 s 2.8 s

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

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

19 UCAIR Utah Center For Advanced Imaging Research Tissue Segmentation Non-FS T1FS T2-w FS PD-w 3pt Dixon H 2 O only 3pt Dixon Fat only Breast tissue segmentation Hierarchal Support Vector Machine algorithm h-SVM w/ Zero-Filled-Interpolation

20 UCAIR Utah Center For Advanced Imaging Research Tissue property estimation: Acoustic parameters Tissue acoustic values for Model Predictive Filtering. Segment treatment volume into a small number of tissue types 4-8 low power pulses cover targeted volume MR temps to get SAR patterns Use ultrasound model (HAS) to determine absorption and speed of sound to match measured pattern TCR – reconstruct temperature images In-vivo estimates of the change in the attenuation coefficient with log 10 of thermal dose using the iterative parameter estimation technique. Urvi Vyas et al. ISTU 2011

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

22 UCAIR Utah Center For Advanced Imaging Research Hybrid Angular Spectrum (HAS): Pressure Modeling

23 UCAIR Utah Center For Advanced Imaging Research HAS SAR prediction

24 UCAIR Utah Center For Advanced Imaging Research HAS: Head Model Courtesy: Guido Gerig, University of Utah

25 UCAIR Utah Center For Advanced Imaging Research

26 UCAIR Utah Center For Advanced Imaging Research

27 UCAIR Utah Center For Advanced Imaging Research Model Predictive Filtering Temp (n) Temp (n+1, model) Multi-step, recursive algorithm Temp (n+1, model and data) 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 Phase (n+1) Magnitude (n) K-space (n+1)

28 UCAIR Utah Center For Advanced Imaging Research Model Predictive Filtering Use the Pennes Bioheat Equation, tissue properties, and a pre-treatment heating to determine the thermal model. T = temperature  = density C = tissue and blood heat capacity k = thermal conductivity W b = blood perfusion Q = heat applied Full DataModel Only

29 UCAIR Utah Center For Advanced Imaging Research 2-D MPF Results Mean and STD of error over an ROI 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) Power = 42W Power = 48W MPF

30 UCAIR Utah Center For Advanced Imaging Research 3D (R=12) vs 2D (R=1) MPF Temperatures N. Todd, A. Payne, D. L. Parker, MRM 63:1269–1279 (2010) Common: Ultrasound pulse = 36 W/58.1 sec 3-D GRE: FOV = 256x256x32 mm 3, Matrix = 128x128x16 Resolution = 2.0x2.0x2.0 mm 3 TR/TE = 25/8 ms T acq = 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 mm 3 TR/TE = 65/8 ms; 8.3 sec per scan (R=1) Scans repeated 8x for variability

31 UCAIR Utah Center For Advanced Imaging Research 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 σ T < 1°C Transverse: Sagital: Coronal:

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

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


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