1. J OURNAL C LUB : M. Pei et al., Shanghai Key Lab of MRI, East China Normal University and Weill Cornell Medical College “Algorithm for Fast Monoexponential Fitting Based on Auto-Regression on Linear Operations (ARLO) of Data.” Aug 18, 2014 Jason Su

2. Motivation Traditional fitting methods for exponentials have pros and cons – Nonlinear LS (Levenberg-Marquardt) – slow, may converge to local minimum – Log-Linear – fast but sensitive to noise Can we improve upon them? – Surprisingly, yes!

3. Background: Numerical Integration Approximating the value of a definite integral Trapezoidal Rule: the area under a 2-pt linear interpolation of the interval Simpson’s Rule: the area under a 3-pt. quadratic interpolation of the interval Newton-Cotes formulas:

4. Theory

6. Methods Rician noise compensation – Data truncation, only keep points with high SNR Values > μ + 2σ noise in background – Apply a bias correction based on a Bayesian model table look-up depending on the number of coils

7. Methods Simulation to assess bias and variance – Fitting method vs T2* range, # channels, SNR – 10,000 trials with Rician noise In vivo – 1.5T, 8ch, 15 patients, 2D GRE, TR=27.4, α=20deg, TE = 1.3- 23.3ms (16 linearly sampled), liver – 3T, 8ch?, 2 volunteers, 3D GRE, α=20deg, 7/12 echoes with 6.5/4.1ms spacing, brain – 1.5T, 2D GRE, TR=19ms, α=35deg, TE=2.8-16.8ms (8 echoes), heart with iron overload – Manual segmentation of liver and brain structures Statistical – Linear regression, Bland-Altman, and t-tests

8. Results: Simulation LM and ARLO are effectively equivalent ARLO is generally equivalent to LM except at T2*=1.5ms Log-linear is sensitive to T2*, SNR, and channels

9. Results: In Vivo, Liver ROI Computation time per voxel – 8.81 ± 1.00ms for LM – 0.57 ± 0.04ms for LL – 0.07 ± 0.02ms for ARLO

10. Results: In Vivo, Whole Liver

12. Results: In Vivo, Brain

14. Results: In Vivo, Heart

15. Discussion ARLO is more robust than LL to noise with accuracy as good as LM at 10x the speed of LL – Noise is amplified by log-transform – ARLO is a single-variable linear regression, O(N) – LL is a two-variable linear regression, O(6N) – LM is nonlinear LS, O(N 3 ) ARLO provides an effective linearization of the nonlinear estimation problem – Does not require an initial guess, immune to convergence issues like in LM

16. Discussion Simpson’s rule much better approximation than Trapezoidal – Higher order gave little improvement Could also use differentiation but not as good as integration in low SNR and need finer sampling Other applications: – Other exponential decay models like diffusion, T2, off- resonance and T2* – T1 recovery “from data measured at various timing parameters such as TR or TI” Can also be adapted to multi-exponential fitting

17. Discussion Limitations – Requires at least 3 data points vs 2 for LM and LL – Linear sampling of echo times – Results in minimum T2* of 1.5ms by ARLO Probably due to poor protocol

18. Thoughts Nonlinear sampling – Generally linear sampling is not ideal for experimental design, are there approximations that don’t require this? – “Gaussian quadrature and Clenshaw–Curtis quadrature with unequally spaced points (clustered at the endpoints of the integration interval) are stable and much more accurate” For protocols varying multiple parameters, we would integrate over multiple dimensions? – Higher-dimensional integral approximations? – Simpson’s in each dimension would be a lot of sample points

19. Thoughts