New developments in Magnetic Resonance Spectrocopy and Diffusion MRI. Els Fieremans Steven Delputte Mahir Ozdemir

Transcription

1 New developments in Magnetic Resonance Spectrocopy and Diffusion MRI Els Fieremans Steven Delputte Mahir Ozdemir

2 Overview Magnetic Resonance Spectroscopy (MRS) Basic physics of MRS Quantitative MRS Pitfalls MRS of the prostate Diffusion MRI Basic physics of diffusion MRI Sequence development Validation (hardware phantom) Diffusion Tensor Tractography Validation (software phantom) More MR research at UGent

3 General Introduction: Nuclear spin A nucleus with an odd atomic number or an odd mass number has a nuclear spin. The spinning charged nucleus generates a magnetic field.

4 General Introduction: Net magnetization M The magnetic fields of the spinning nuclei will align either parallel with the external field, or antiparallel to the field. M

5 General Introduction: Larmor frequency ν = γ B 0 0 ν 0 is the Larmor precession frequency γ is the gyromagnetic ratio (42.58 MHz/T for hydrogen) B 0 is the main magnetic field (typically 1T to 3T) ν 0

6 General Introduction: RF excitation & FID M is tilted from its original longitudinal z-axis orientation by B 1 matching the larmor frequency of M. The oscillation of M xy produces a fluctuating magnetic field that generates a current in the receiver coil: FID. I Spectrum: peak area is proportional to proton concentration

7 Introduction Absolute quantification Pitfalls Application Introduction MRS: real spectrum If all the proton nuclei in a mixture of molecules had the same Larmor frequency, spectra would be limited to a single peak! Mahir

8 Introduction Absolute quantification Pitfalls Application Introduction MRS: Magnetic shielding B local B 0 B i a bare nucleus (H + ) feels the full effect of the external field (B 0 ) electrons generate an induced field (B i ) which opposes B 0 electron density partially shields the nucleus from B 0 so it feels B local ν = γ local B local Mahir

9 Introduction Absolute quantification Pitfalls Application Introduction MRS: chemical shift The difference between the resonance frequency and a standard reference frequency in Hz (chemical shift) is characteristic for each metabolite and is dependent on the magnetic field strength. This difference, divided by that standard frequency is independent of the field strength: δ (ppm)= shift (Hz) / frequency of excitation pulse (MHz) Mahir

10 Introduction Absolute quantification Pitfalls Application Introduction MRS: CSI versus SVS Chemical Shift Imaging Single Voxel Spectroscopy Courtesy: Siemens Mahir

11 Introduction Absolute quantification Pitfalls Application Introduction MRS: Ratio based results Can generate maps of certain metabolites Maps of metabolite ratios such as NAA/Cre, or Cho/Cre Courtesy: GE The ratio based results can be used for the classification of tissues (eg. for discrimination between malignant versus benign tissues) Ambiguity: is numerator or denominator changing? Mahir

12 Introduction Absolute quantification Pitfalls Application Absolute quantification Resolves ambiguities caused by ratio based results. S [ M ] = [ reference] x S Correction for metabolite dependent values of T1, T2 and # of protons per molecule needed!! M ref Choice of reference substance is of key importance: internal (creatine, water, ) versus external reference Since last couple of years: internal water signal most popular as reference.» Pathology related changes are relatively small compared to Cre» Concentration is very well known Mahir

13 Introduction Absolute quantification Pitfalls Application Absolute quantification Concentration of water: 1000g 1liter 1mole 18g = 55.5M 2moles moleh H O M = [ protons] Concentration of metabolite is typically only in the order of 10mM! Severe dynamic range problem (factor of 10000)! But it is too time consuming to record both water unsuppressed and water suppressed data sets. Mahir

14 Introduction Absolute quantification Pitfalls Application Absolute quantification: Singular Value Decomposition Unsuppressed water spectrum SVD Metabolite spectrum Mahir

15 Introduction Absolute quantification Pitfalls Application Pitfall 1: Signal loss due to SVD P_true Forward Problem SVD P_true>P_res 18% signal loss P_res P_res n (number of steps) Inverse problem Iterative Algorithm Remaining signal loss (%) Mahir P_true Iterative Algorithm

16 Introduction Absolute quantification Pitfalls Application Pitfall 2: Sideband artifacts Sitebands = gradient induced frequency modulations of the unsuppressed water signal Residual water signal NAA Mahir

17 Introduction Absolute quantification Pitfalls Application Pitfall 2: Corrected sideband artifacts Mahir

18 Introduction Absolute quantification Pitfalls Application Application: MRS of the prostate Second cause of cancer related death in men (*) Prostrate: 2x4x3 cm, 20 g Normal tissue Reduced signal ratio between citrate & choline *Imperial cancer research Fund, American Cancer Society Tumor Mahir

19 Application: MRS of the prostate, validation with a pelvis phantom The phantom Introduction Absolute quantification Pitfalls Application 90 mm Citrate Solution ER coil m = mm, std = 17.3 mm Mahir

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21 Overview Magnetic Resonance Spectroscopy (MRS) Basic physics of MRS Quantitative MRS Pitfalls MRS of the prostate Diffusion MRI Basic physics of diffusion MRI Sequence development Validation (hardware phantom) Diffusion Tensor Tractography Validation (software phantom) More MR research at UGent

22 Introduction General introduction to Diffusion MRI DTI can disclose the 3D organization of fibrous tissue DTT enables us to reconstruct non-invasively the white matter axonal pathways

23 Introduction Basics of diffusion MRI The random movement of protons. Mean step = 2Dt Einstein equation D = diffusion coefficient in free medium t = observation time» Typically: 8μm in 35ms (D=1.0x10-3 mm 2 s -1 )

24 Introduction Origin of diffusion signal in brain white matter Dendrites Cell body Nucleus Axon Myelin Sheath Axon terminals 10 μm

25 Introduction Diffusion signal: Extra cellular FAST diffusion Intra cellular SLOW diffusion Exchange IC / EC

26 Introduction B x x x Δ B x x t

27 Introduction Basics of diffusion MRI By applying diffusion gradients, the random movement of protons in the extra cellular space along a chosen direction is measured (DWI). Molecular mobility is not the same in all directions due to barriers (myelin and axon membranes) anisotropy DTI: probing the three-dimensional architecture of brain white matter Diffusion Tensor Tractography (DTT): non-invasive tool for reconstructing the white matter axonal pathways of the human brain in vivo.

28 Spiral acquisition Validation: head phantom Diffusion Tensor Imaging Sequences DTI in brain white matter:» Intra-voxel heterogeneity a voxel may contain multiple fiber directions (eg. crossing fibers).» Low SNR Solution: Increase the number of acquisitions and angular resolution by applying diffusion gradients in many directions: for DTI and for High Angular Resolution Diffusion Imaging (HARDI, does not suppose any model for the diffusion). Speed of the sequence becomes crucial! Els

29 Spiral acquisition Validation: head phantom MRI Imaging Basic principle of magnetic resonance imaging: k-space formalism I r (r ) = I = S (k r ) Image space Frequence space Fourier space k-space Els

30 Spiral acquisition Validation: head phantom K-space sampling strategies Need for fast MR imaging sequences for fmri, DTI, HARDI, 2 strategies for sampling the k-space rapidly: Echo planar imaging (EPI) Spiral imaging Spiral sequences show some advantages/differences in comparison with cartesian EPI:» Smoother trajectory less demands on hardware performance» Less sensitive to motion artifacts.» Spirals (radial symmetric PSF) blurring.» Cartesian EPI (anisotropic PSF) distortion artifacts Els

31 Spiral acquisition Validation: head phantom K-space sampling strategies Comparison between Cartesian EPI and Spiral Imaging Els

32 Spiral acquisition Validation: head phantom DTI optimization strategies Elimination of the artifacts: Spiral image Spiral image SHIMMING Spiral image Correction for eddy currents imperfection of the magnetic gradients Els

33 Spiral acquisition Validation: head phantom Validation of DTI sequences In vivo single shot spiral scan images with diffusion encoding along the x-, y-, z-direction and corresponding isotropic diffusion-weighted imaging (from left to right). Bammer R, Basic principles of diffusionweighted imaging, European journal of radiology, 45: , 2003 Hardware diffusion phantom Els

34 Spiral acquisition Validation: head phantom Validation: head diffusion phantom Synthetic fibers to imitate the neural fascicle bundles. Anthropomorphic phantom of the major neural fiber tracts. MRI-compatibility: T1 and T2-relaxation times similar with brain white matter. DTI-compatibility: similar diffusion behavior as brain white matter (Monte Carlo diffusion simulations and quantitative measurements of D App (t)-curves for different fiber materials). Els

35 Spiral acquisition Validation: head phantom Validation: a phantom bundle 400 parallel wires tightly held together by a shrinking tube Wire = woven strand of Ultrahigh-Molecular Weight Polyethylene fibers (UHMWPE) (Dyneema ) FA = 0.45 (± σ = 0.15) Els

36 Spiral acquisition Validation: head phantom Validation: head diffusion phantom Els

37 Spiral acquisition Validation: head phantom Validation: head diffusion phantom FA FA Fractional Anisotropy 3T, TE = 60ms, TR= 3s spin echo sequence with TRSE-diffusion preparation 12 directions, b-factors of 0 and 700 s/mm² Tracking result of the corticospinal tract. FA = (± σ = 0.15) Els

38 Overview Magnetic Resonance Spectroscopy (MRS) Basic physics of MRS Quantitative MRS Pitfalls MRS of the prostate Diffusion MRI Basic physics of diffusion MRI Sequence development Validation (hardware phantom) Diffusion Tensor Tractography Validation (software phantom) More MR research at UGent

39 DTT: introduction DRFT Validation: software phantom DTT: reconstruction of axonal connections line propagation Solve: d r ds ( s) = For each step, 2 decisions to make: New direction?» Principal diffusion direction» Tensor deflection» Tensor deflection with subpixel adaptive step size, Integration method? e» Euler (first order integration: i+1 i )» Runge-Kutta (fourth order integration)» FACT (1999, Mori et al.), r = r + c e Steven

40 DTT: introduction DRFT Validation: software phantom DTT algorithms & visualization Point to point rigid connections Diagnostically valuable Fast Cumulative error propagation (spurious tracts) Likelihood of connectivity maps eg. Fast marching More information slower Seedvoxel Steven

41 DTT: introduction DRFT Validation: software phantom Density Regularized Fiber Tracking (DRFT) Point to point connections + pointwise estimate of probability + environmental architectural information Based on the fact that the architectural environment plays a dominant role in the reproducibility of each tracking result Steven

42 DTT: introduction DRFT Validation: software phantom Density Regularized Fiber Tracking (DRFT) d i > d +1.7σ d Temporary track CM tract Stop temporary track Steven

43 DTT: introduction DRFT Validation: software phantom DRFT results: visualization Body of the corpus callosum Color encodes directional information (A/P: green, I/S: blue, L/R: red) Transparency encodes estimate of probability Width encodes σ d (dispersion) Steven

44 DTT: introduction DRFT Validation: software phantom DTT validation: framework A B In vivo DWI acquisition Anisotropic smoothing of DWIs RESTORE (robust tensor estimation) DRFT ground truth fibers Build the anatomically realistic phantom 1 (using environmental architectural information) C Add noise & try to reconstruct the ground truth fibers Compute similarity measures 1 extension of work by A. Leemans (MRM 2005, 53: )

45 (a) In vivo (a,c) (c) DTT: introduction DRFT Validation: software phantom (b) A good correspondence is found between the colour coded synthetic FA images and the original in vivo ones (d) Phantom (b,d) Steven

46 DTT: introduction DRFT Validation: software phantom Validation results: similarity measurements Steven

47 DTT: introduction DRFT Validation: software phantom DRFT and DTT validation DRFT results in diagnostically valuable 3D pathways AND at the same time gives an estimate of probability. By using in vivo DRFT results, we were able to build a noise-free and anatomically realistic dataset.» Noise and MRI acquisition artifacts can be incorporated in the synthetic phantom as well. With an anatomically realistic synthetic DT dataset we can:» quantitatively predict how a (new) DTT algorithm will perform on real in vivo data (and not just for ad hoc cases such as helices etc.).» optimize internal and operator dependant tractography parameters. Steven

48 More More MR research at Ugent (1.5T and 3T) GifMi:» fmri studies of language and memory of epileptic patients, fmri of stuttering» MRI techniques for measuring the biological malfunctioning of neurovascular units in migraine patients» Neuropsychology: Emotional disorders, mental rotation, cognitive dysfunctions structural brain damage in MS patients Radiotherapy:» Quantitative T2-mapping for 3D geldosimetry & tissue classification» Molecular imaging

49 New developments in Magnetic Resonance Spectrocopy and Diffusion MRI Els Fieremans Steven Delputte Mahir Ozdemir