IMPROVED MRI TEMPERATURE IMAGING USING A SUBJECTSPECIFIC BIOPHYSICAL

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

UCAIR Background n Utah Center For Advanced Imaging Research Utah Projects in MRI guided HIFU n n n Large animal MRg. HIFU system (Siemens/IGT) Small animal MRg. HIFU system (IGT) Breast MRg. HIFU system (Uof. U/IGT/Siemens) n See poster 4. 8 by n Allison Payne

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

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

Breast: Temperature Measurements n Requirements: n n n Utah Center For Advanced Imaging Research 3 -point Dixon Images Control treatment in glandular tissue Avoid fat necrosis Coverage, speed, and resolution Temperature in water and fat? n UCAIR Fat Water Hybrid PRF/T 1 method 2 D GRE 2 D/3 D Segmented EPI fat agar PRF temperature map T 1 signal change map

MRI Thermometry - Breast Hybrid PRF/T 1 Signal from Spoiled GRE sequence: Image sequence: 2 alternating flip angles PRF from phase of each image T 1 from two images Deoni, Rutt, Peters. Magn Reson Med 2003 49: 515 -526. UCAIR Utah Center For Advanced Imaging Research

Breast: Temperature Measurements A) 3 -Pt Dixon Water Image C) PRF/T 1 Magnitude Image Pork Muscle Breast Fat Targeted Area Transducer B) 3 -Pt Dixon Fat Image D) PRF Temperature Map UCAIR Utah Center For Advanced Imaging Research

Breast: Temperature Measurements T 1 Percent Change in Breast Fat PRF Temperatures in Pork B) C) PRF/T 1 Magnitude Image Pork Muscle Breast Fat Targeted Area Transducer D) PRF Temperature Map UCAIR Utah Center For Advanced Imaging Research

UCAIR Utah Center For Advanced Imaging Research Transcranial MRI guided HIFU Funding: Focused Ultrasound Surgery Foundation NIH R 01 EB 013433

Transcranial MRI guided HIFU n n UCAIR Utah Center For Advanced Imaging Research Cover all heated regions: Skull + within Resolution Speed Coverage (FOV) n n n 1 mm isotropic 1 s Full head/breast 205 x 160 x 100 TR=35, ETL=7: 80 s 205 x 160 x 33 (1 x 1 x 3 mm), TR=35, ETL=7: 27 s Image Volume

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

Transcranial MRI guided HIFU n How to go faster: n 2 D Spatially selective RF excitation n n Prefer full FOV n Parallel imaging + UNFOLD 1 n Temporally Constrained Reconstruction (TCR)2 n 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): 406 -419 (2009). 3: N. Todd, A. Payne, D. L. Parker, Magn Reson Med 63: 1269– 1279 (2010) UCAIR Utah Center For Advanced Imaging Research

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

Constrained Reconstruction UCAIR Utah Center For Advanced Imaging Research F = Fourier Transform ~ = Image Estimate m 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

TCR: Constrained Reconstruction Sequence Parameters • • • UCAIR Utah Center For Advanced Imaging Research Data Undersampling 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 kz Scan Time: 1. 8 s / time frame Constrained Reconstruction 25 s / full data set Not real time

Constrained Reconstruction Results Validation Tests: Utah Center For Advanced Imaging Research Full Data “Truth” 2. 8 s 5. 4 s 10. 1 s 16. 2 s Constrained Reconstruction 6 X data reduction “Truth” 2. 8 s 0. 9 s 1. 7 s 2. 7 s “Truth”: Full Data used 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 UCAIR

Model Predictive Filtering The rma l. M de Un Utah Center For Advanced Imaging Research ode l d ple m rsa UCAIR ace p s k- Artifact-free Temperature maps Goal: real time N. Todd, A. Payne, D. L. Parker, MRM 63: 1269– 1279 (2010)

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

Tissue Segmentation UCAIR Utah Center For Advanced Imaging Research Breast tissue segmentation • Hierarchal Support Vector Machine algorithm Non-FS T 1 FS T 2 -w FS PD-w 3 pt Dixon H 2 O only h-SVM w/ Zero-Filled-Interpolation 3 pt Dixon Fat only

Tissue property estimation: Acoustic parameters UCAIR Utah Center For Advanced Imaging Research 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 log 10 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.

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 UCAIR Utah Center For Advanced Imaging Research

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

HAS SAR prediction UCAIR Utah Center For Advanced Imaging Research

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

UCAIR Utah Center For Advanced Imaging Research

UCAIR Utah Center For Advanced Imaging Research

Model Predictive Filtering UCAIR Utah Center For Advanced Imaging Research Multi-step, recursive algorithm 3 2 Phase (n+1) 1 Temp (n) 3 Temp (n+1, model) Step 1: Use model to predict temperature at time n+1. K-space (n+1) Magnitude (n) 5 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 kspace. Temp (n+1, model and data)

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

2 -D MPF Results UCAIR Utah Center For Advanced Imaging Research Fully sampled k-space data sets: 288 x 20 mm FOV, 2. 3 x 4 mm res, 8. 3 sec/scan. 25% of k-space used in reconstruction. Power = 36 W (Model Id data set) Mean and STD of error over an ROI MPF Power = 42 W Mean and STD of error over an ROI MPF Power = 48 W Mean and STD of error over an ROI MPF

3 D (R=12) vs 2 D (R=1) MPF Temperatures UCAIR Utah Center For Advanced Imaging Research Common: Ultrasound pulse = 36 W/58. 1 sec 3 -D GRE: FOV = 256 x 32 mm 3, Matrix = 128 x 16 Resolution = 2. 0 x 2. 0 mm 3 TR/TE = 25/8 ms Tacq = 76. 8 s/image volume (R=1) = 6. 4 s/image volume (R=12. 1) 2 D GRE: FOV = 256 x 20 mm (sl = 3 mm) Matrix = 128 x 128 Resolution = 2. 0 x 3. 0 mm 3 TR/TE = 65/8 ms; 8. 3 sec per scan (R=1) Scans repeated 8 x for variability N. Todd, A. Payne, D. L. Parker, MRM 63: 1269– 1279 (2010)

Model Predictive Filtering Results Phantom Heating 2. 0 x 2. 0 mm 0. 5 seconds per image 256 x 162 x 48 mm σT < 1°C Transverse: Sagital: Coronal: UCAIR Utah Center For Advanced Imaging Research

Summary: Work in Progress n Utah Center For Advanced Imaging Research Brain requires: n n UCAIR Large FOV: Cover insonified volume High speed: 1 s/volume High resolution: < 1 x 3 mm 3 Our solutions: n n PRF: Highly undersampled (>8) 3 D segmented EPI TCR: n n 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) n Requires n n n Property estimates: n n n tissue segmentation estimate of tissue acoustic and thermal properties 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 n n Can be used to supplement TCR or MPF Difficult with currently used HIFU coils

Acknowledgments Thank You People: Yi Wang Urvi Vyas Dennis Parker Emilee Minalga Bob Roemer Joshua de Bever Doug Christensen Chris Dillon Leigh Neumayer Joshua Coon Allison Payne Justin Tidwell Nick Todd Lexi Farrer Rock Hadley Robb Merrill Nelly Volland Mahamadou Diakite Henrik Odeen Funding: Focused Ultrasound Surgery Foundation Siemens Medical Solutions NIH grants F 31 EB 007892 -01 A 1, R 01 EB 013433, and R 01 CA 134599. UCAIR Utah Center For Advanced Imaging Research