Comparison of Single Shot Methods for R2* Comparison Presentation for Kana Lab, Lab Meeting Rishi Deshpande Thesis Committee: Dr. Donald Twieg, Chair Dr. N. Shastry Akella Dr. Georg Deutsch University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd,

Outline  Introduction Basics of MRI, fMRI Significance of reliable R 2 * estimation Single-shot methods: MEPI and SS-PARSE  Experiment and Analytical Methods Trajectory generation Data acquisition Reconstruction and comparison of accuracy and temporal variability  Discussion  Conclusion  Future scope 2 University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd, 2009

1 H nuclei within tissues 1 H nuclei under external magnetic field RF pulse (sinc/Gaussian/square) 1 H get dislodged from steady state. They release energy while returning to steady state. Energy is collected as a function of 2D-Inverse Fourier Transform Sources: Applying a 2D-FFT to the signal data generates 2D-images in the imaging plane. Basics of MRI Image Acquisition 3 University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3rd, 2009

Control/Stimulation acquisition Estimation of Neuronal activity ↓ BOLD effect ↓ R2* fMRI 4 University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd, 2009

BOLD Response Model: Significance of reliable R 2 * estimation 5 *BOLD = Blood Oxygenation Level Dependent * R 2 * = 1/T 2 * University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd, 2009

R 2 * Measurement: Multiple Shot Method Gradient Echo Multiple Shot (GEMS)  Echoes can be closely stacked, thus enabling accurate R 2 * calculation  Serves as a gold standard in the comparison study 6 University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd 2009

Single Shot Methods Multiple Gradient Echo – Echo Planar Imaging (MEPI) 7 University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd 2009

SS-PARSE Conventional model Estimates map: M(x) M(x)  (x) R 2 * (x) SS-PARSE model Include local phase evolution & local signal decay Estimate maps (images) of M(x), R 2 * (x), ω(x) by solving an inverse problem. It uses Progressive Length Conjugate Gradient (PLCG) algorithm which requires optimal parameters to minimize least squared residuals to generate parameter maps. Single-Shot Parameter Assessment by Retrieval from Signal Encoding 8 Censored for gratuitous math University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd 2009

Comparing Conventional MRI & SS-PARSE Methods 9 University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd 2009

10 Source: University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd 2009

Project Goals - experimental  Create gradient waveforms and generate trajectories for 7 different gradient strengths (1.9 G/cm to 3.8 G/cm):  Implement the sequence on Varian 4.7 T vertical scanner using phantoms  Compare performance of SS-PARSE with MEPI based on: 1. Accuracy of R 2 * estimates (compare with Gradient-Echo results) 2. Temporal variability of R 2 * (over time-series of 50 acquisitions) 3. Find correlation between R 2 * and TSD values 4. Find correlation between maximum gradient strength and accuracy 11 University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd 2009

Project goals – Theoretical Inferences  Factors contributing towards performance of SS-PARSE: 1. G max values – Find relationship between G max and R 2 * estimates (compared with gradient-echo values) 2. Shimming – Find effects of field inhomogeneity in SS-PARSE and MEPI studies. 3. Performance over R 2 * range - Observe the changes in temporal behavior over R 2 * values typically found in human brain tissues (20 to 40 sec -1 in 4.7 T MRI systems) 12 University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd 2009

65 ms 1.9 G/cm 2.29 G/cm 2.5 G/cm2.9 G/cm 3.2 G/cm 3.5 G/cm 3.8 G/cm k-trajectory Generation and Calibration Calibration data acquired at: ±2, ±4, ±6, ±8, ±10, ±12 mm displacements in x & y planes For G max : 1.9, 2.29, 2.5, 2.9, 3.2, 3.5 and 3.8 G/cm. 13 University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3rd, 2009 Censored for gratuitous math

Phantom For Data Acquisition R2* Range: 15 sec -1 to 45 sec University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3rd, 2009

Data Acquisition: GEMS, MEPI and SS-PARSE 1. SS-PARSE acquisitions Per study =(7x G max ) x (50x repetitions) Per study =(7x G max ) x (50x repetitions) Repetition time=5 second Repetition time=5 second Slice Thickness=3 mm Slice Thickness=3 mm 2. MEPI acquisitions Per study=50x repetitions at 4 echo times Per study=50x repetitions at 4 echo times Resolution=64 x 64 Resolution=64 x 64 Repetition time=5 second Repetition time=5 second Echo Times=22.3, 66.8, 96.4 and millisecond Echo Times=22.3, 66.8, 96.4 and millisecond Slice Thickness=3 mm Slice Thickness=3 mm 3. GEMS acquisitions Per study=16 x echo times Per study=16 x echo times Resolution= 128 x 128 Resolution= 128 x 128 Echo Times=5, 8, 10, 12, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65 and 70 millisecond Echo Times=5, 8, 10, 12, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65 and 70 millisecond Slice Thickness=3 mm Slice Thickness=3 mm Performed total 18 experiments to obtain the R2* values in the desired range (15 to 45 sec -1 ) Hardware: 4.7 T 60 cm-vertical-bore Varian primate MRI system (Varian Inc., Palo Alto, CA) 15 University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd 2009

Source: 16 University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd, 2009

Development of GUI For Analysis & File Handling File HandlingPLCG TweakersParameter Maps 17 University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd 2009

R 2 * Evaluation: GEMS and MEPI R 2 * is computed over a ROI Monoexponential fitting of signal to echo times. MEPI GEMS 18 University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd, 2009

Parameters Estimates in SS-PARSE  Reconstruction (SS-PARSE) Parameter maps were computed using the PLCG algorithm from all the SS-PARSE acquisitions. Maps were created for all G max values (1.9 G/cm to 3.8 G/cm). 19 University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd, 2009

Accuracy of R 2 * Estimation 1.R 2 * estimates from SS-PARSE and MEI plotted vs. R 2 * from GEMS 2.Ratio of R 2 * accuracy plotted vs. R 2 * estimates from GEMS SS-PARSE and MEPI estimates and accuracy plot at SS-PARSE G max = 2.9 G/cm 20 University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd, 2009

Accuracy Over Gradient Amplitudes Accuracy of R 2 * estimation computed by using the ratio: |R 2 * MEPI - R 2 * GEMS | |R 2 * SSPARSE - R 2 * GEMS | was > 1 for following percentage points over the G max range: G/cm:61.3% G/cm:64.2% G/cm:66.4% G/cm:68.3% G/cm:67.6% G/cm:65.6% G/cm:61.2% Accuracy of estimation (ratio) was maximum at G max = 2.9 G/cm 21 University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd, 2009

Temporal Variation of R2* Over 50 Repetitions TSD computed for: Each pixel over 50 repetitions Each ROI over 50 repetitions For MEPI and SS-PARSE For G max with best accuracy 22 University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd 2009

23 University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd, 2009 Mark with lower temporal variability, Thus lower TSD Good Mark with higher temporal variability, Thus higher TSD Not Good

Depiction of TSD in MRI Studies 24 University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd, 2009

TSD Plots The difference was > 0 for 79.3% to 97.3% for R2* values between 15 sec -1 and 45 sec -1 Dot indicates TSD at a single pixel Each blob of pixels represents a tube with a different R2* Scatter plot for the difference TSD (MEPI) – TSD (SS-PARSE) shows points around the difference = 0 line Dots above the difference=0 line show locations where the performance of SS-PARSE was better than of MEPI R2* (GEMS) vs. TSD (SS-PARSE) R2* (GEMS) vs. TSD (MEPI) R2* (GEMS) vs. [TSD (MEPI) and TSD (SS-PARSE)] 25 University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd, 2009

Performance Under Field Inhomogeneity MEPI SS-PARSE 26 University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd, 2009

Parameter Estimation Under Field Inhomogeneity  SS-PARSE parameter maps have an one-on-on correspondence with the ROI from GEMS image (obtained before intention deshimming)  MEPI image appears distorted in one direction and the ROI does not correspond with ROI from GEMS. Even though we have studied the behavior of MEPI, the same behavior is also observed in standard EPI scans, which is the common modality used in clinical fMRI sudies.  R 2 * computation in MEPI is impossible under field-inhomogeneity because of a noticeable geometric distortion. 27 University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd 2009

Source: 28 University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd, 2009

Conclusions  Gradient waveforms for seven G max values were developed for SS- PARSE and were used to acquire phantom data  Performance of SS-PARSE and MEPI was compared using GEMS as the gold standard (for accuracy and TSD) over range of G max values.  Performance of SS-PARSE improved with increasing gradient amplitude until 2.9 G/cm. Thereafter the performance deteriorates.  SS-PARSE has a lower TSD than MEPI. This means it can estimate the parameters much reliably over several repetitions when used in fMRI studies.  SS-PARSE is able to reconstruct reliable parameter maps even in the presence of field inhomogeneities. 29 University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd 2009

Future Scope  PLCG algorithm requires adjusting the algorithm tweakers heuristically. With better knowledge about the estimation process we should be able to set the parameters in a deterministic manner.  With better problem conditioning, and with MRI systems capable of delivering more than 6.5 G/cm (hardware limit of Varian 4.7 T system), we should be create trajectories with much higher sampling rates, thus giving accurate parameter estimation.  Parallel acquisition and multiple shot trajectories, increases the number of sample points, thus improving conditioning of the inverse problem and leading to more accurate estimates. 30 University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd 2009

Acknowledgement  Advisor: Dr. Donald Twieg  Committee Members Dr. N. Shastry Akella Dr. Georg Deutsch  Dr. Stan Reeves (Auburn)  CDFI & VSRC colleagues: Mark Bolding Rajiv Menon Ningzhi Li Matt Ward Debbie Whitten Jerry Millican  Parents and Sister  Friends Michelle Jon Chris  Grant Support: NIH # R21/R33 EB  City of Birmingham 31 University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd, 2009

Thank You 32 University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd, 2009

Questions Source: 33 University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd, 2009

Extras 34 University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd, 2009

Cramer-Rao Lower Bound for standard deviation of error for Multiple-Echo EPI (MEPI) and Rosette, SNR=200 Rosette (k,t)-trajectories acquire more information on R2* than multiple-echo EPI trajectory MEPI Rosette Idealized radial s.d. of R2* R2* (sec -1 ) 35 University of Alabama at Birmingham, Department of Psychology, Lab Meeting Presentation, September 3 rd 2009