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S Dr. S. J. Doran Department of Physics, University of Surrey, Guildford, GU2 5XH, UK Pelvic MR scans for radiotherapy planning: Correction of system- and patient-induced distortions Simon J Doran1, Liz Moore2, Martin O Leach2 1Department 2CRC of Physics, University of Surrey Clinical Magnetic Resonance Research Group, Institute of Cancer Research, Sutton Acknowledgements • David Finnigan • Steve Tanner • Odysseas Benekos • David Dearnaley • Steve Breen • Young Lee • Geoff Charles-Edwards Summary of Talk • The problem of distortion • Strategy for solving the problem Chang and Fitzpatrick algorithm (B0-induced distortion) Linear test object (gradient distortion) • Current limitations of the method • Patient trials and validations of system in progress The Problem • For many applications, MR provides better diagnostic information than other imaging modalities. • However, MR images are not geometrically accurate they cannot be used as a basis for planning procedures • Can we correct all the sources of distortion in an MR image? Radiotherapy Potential Applications Thermotherapy Stereotactic surgery Correlation of MR with other modalities (image fusion) Mathematical statement of the problem I(r) = Itrue (r-Dr) where Dr = Dr(r) Dr is a 3-D vector, whose magnitude and direction both depend on position. Sources of distortion: (1) B0-induced • Source of the problem is incorrect precession frequency in the absence of gradients due to poor shim or susceptibility variations in sample chemical shift variations in sample Data source: C.D. Gregory, BMRL Sources of distortion: (2) gradient-induced • Source of the problem is incorrect change in precession frequency when gradients are applied. Data courtesy R Bowtell, University of Nottingham % error in Bz DBz / arb. units +15 0 -10 250 250 Isocentre 0 Isocentre 250 250 0 Strategy for solving problem • FLASH 3-D sequence – susceptibility and CS lead to distortion only in read direction (unlike EPI) • Acquire data twice – forward and reverse read gradients. • Correct for B0-induced distortions with Chang and Fitzpatrick algorithm. IEEE Trans. Med. Imag. 11(3), 319-329 (1992). • Use linearity test phantom to establish gradient distortions. • Remove gradient distortions using interpolation to correct position and Jacobian to correct intensity. Chang and Fitzpatrick algorithm • We have two data sets, F and R, which we treat row by row. • For a given row, F(xF) dxF = R(xR) dxR . • Calculate points xR corresponding to xF. corr - fwd Then xtrue = (xF + xR) / 2 . fwd corr corr - rev rev The “linearity test phantom” (1) • Why do we need it? Can’t we get theoretical results? Manufacturers very protective of this sort of data Need to guarantee “chain of evidence” for e.g., radiotherapy Is the gradient system subtly malfunctioning? • robust, light, fixed geometry • mechanical interlocks give reproducible position in magnet • 3 orthogonal arrays of water-filled tubes • square lattice of spots in each orthogonal imaging plane. The “linearity test phantom” (2) Coronal Transverse Sagittal X-ray CT vs. MRI of linearity test phantom Slice offset 0 mm Slice offset -185 mm System distortion mapping algorithm: Step 1 • Acquire 3-D datasets with forward and reverse read gradients. • Match spots between the CT and MRI datasets for transverse plane and correct for distortion in read direction to give single MRI dataset. • Calculate displacement of each point Dx, Dy • Reformat the data to give sagittal and coronal projections. (A different matrix of spots appears in each plane.) • Repeat the matching process: Coronal Dx, Dz Sagittal Dy, Dz System distortion mapping algorithm: Step 2 • Interpolate and smooth data to provide complete 3-D Example: x-distortion on transverse plane at slice offset 117.5 mm reconstructed from transverse images x-distortion / mm matrices of gradient distortion values. 10 -10 100 200 y / mm -100 -200 x / mm System distortion mapping algorithm: Step 3 • Taking the known distortion data, correct the images: Sample the 3-D data Idist at appropriately interpolated points. Correct for intensity distortions using the Jacobian. ^ I(x, y, z) = Idist(x-Dx, y-Dy, z-Dz) . J(x, y, z) B0 corrected B0&Grad B0&Grad - B0 corrected Problems remaining with the technique • We currently have incomplete mapping data from the current phantom. Modifications to design of linearity test phantom • Problem of slice warp: Further data processing using full 3-D dataset Patient study and validation • Protocol is being tested on patients diagnosed with prostate cancer and undergoing CT planning for conformal, external beam radiotherapy. • 4 patients have undergone both CT and MRI to date. • Protocol (total time ~20 mins.) 3-D FLASH, TR / TE 18.8 ms / 5 ms FOV 480 x 360 x 420 mm3 (256 x 192 x 84 pixels) 5mm “slices” FOV 480 x 360 x 160 mm3 (256 x 192 x 80 pixels) 2mm “slices” Each sequence repeated twice (forward and reverse read gradient) • Image registration and comparison with CT now underway. Once we have the corrected MR images ... CT • Validation via 3-D image MRI registration of MRI with CT using champfer-matching • Assess impact of MRbased radiotherapy plans • Ultimate goal: to give us the ability to use MRI alone for radiotherapy planning MRI dataset fed into treatment planning software MR vs. CT Dose-volume histogram for planning treatment volume - patient data • Data for 4 patients analysed so far excellent agreement between treatments calculated with X-ray CT and those calculated on the basis of MR images. 80 % volume • Early indications show 100 60 full C T numbers 40 segmented bone bone density variations 20 water 0 95 100 % d o se 105 System distortion mapping algorithm: Step 3 • Problem: The slices are not themselves flat — slice warp! The slice the scanner tells us we are selecting E.g., for a transverse plane, we have Dx and Dy, but we don’t know exactly which zposition they correspond to The slice we actually get ! System distortion mapping algorithm: Step 3 • Solution: Use the complete set of data acquired • Consider the x-distortion We have two estimates of Dx, acquired from matching spots on transverse and coronal reformats of the original dataset. For Dxtra(x, y, z), z is not known correctly because of slice warp. For Dxcor(x, y, z), y is not known correctly. • But we can estimate unknowns from the data we have ... Dz can be estimated from the coronal or transverse reformats and so used to correct Dxtra and similarly Dy can be estimated to correct Dxcor.