Accuracy in Warping ECT LV Surfaces to CT Angiography Coronary Vessels

Transcription

1 Accurcy in Wrping ECT LV Surfces to CT Angiogrphy Coronry Vessels BC Lee, JN Kritzmn, JR Corbett, EP Ficro * University of Michign Helth System, Ann Arbor, MI Disclosure: * Receive softwre roylties from the 4D-MSPECT softwre ppliction.

2 Objectives Fuse myocrdil perfusion emission computed tomogrphy (ECT) imges with coronry computed tomogrphy (CT) ngiogrphy (CTA) imges. ECT CTA While integrted ECT-CT systems provide nerly sptilly co-registered hert volumes, difference in temporl smpling require wrping registrtion for ccurte fusion of ECT nd CTA dt.

3 Objectives Should the ECT imges be ligned to the CTA imges or vice-vers? vers? ECT? CTA ECT imge resolution is > 8.0mm. CTA imge resolution is < 0.5mm. Investigte ccurcy of lgorithm to wrp 3D ECT LV surfces to CTA coronries to preserve the high resolution informtion of the CTA coronries.

4 Bckground Estimtion of Left Ventriculr Surfces Gted myocrdil perfusion SPECT/PET studies re processed to provide the 3D LV endo nd epicrdil surfces t end-distole distole (ED). Estimtion of Coronry Distribution Coronry vessels re extrcted from CTA studies to provide tree with n irregulr set of vessel centers with surfce contour nd brnch descriptions. ECT CTA

5 Fusion Algorithm Methods: Overview 1. Find the closest point pirs. 2. Initilly register LV surfce with ffine-trnsform. 3. Anchor the unrestricted LV surfce. 4. Refined-wrp LV surfce with curved-trnsform.

6 Methods: Finding Closest Points 1. Finding Closest Points on LV Surfce to Coronry Tree Minimize distnce using Newton s s root finding method: r x = r x [ t ] 1 ( f )( x ) f ( x ) n+ 1 n n n Assign closest LV surfce nd coronry points s control points. r r

7 Methods: Initil Registrtion 2. Initil Registrtion 1 st -order polynomil ffine trnsformtion scles, trnsltes, rottes, nd shers: Trnsform Coefficients xi y i = z i 1 i = 1K N Solve for { jk } where N 4 tht best mps source to destintion control points in the lest-squres squres sense Source Points Destintion Points xˆ yˆ zˆ i i i,

8 Methods: Initil Registrtion Affine-trnsform ll LV surfce points, then regrid.

9 Methods: Anchoring Surfce 3. Anchoring the Unrestricted LV Surfce Higher-order trnsformtions re less predictble in regions unrestricted by control points. Anchor control points, where source nd destintion re the sme, keep unrestricted regions in plce.

10 Methods: Anchoring Surfce Plce course grid of nchor control points on LV surfce not lredy occupied by coronry points. Anchors nd updted closest pirs re control points.

11 Methods: Finl Registrtion 4. Finl Registrtion 3 rd -order polynomil curved trnsformtion is non-liner: Wrping Coefficients 1,1 2,1 3,1 1,2 2,2 3,2 L L L 1,20 2,20 3,20 Source Points 3 xi 3 yi 3 z i M 1 Destintion Points xˆ i = yˆ i zˆ i i = 1K N Solve for { j,k } where N 20 tht best mps source to destintion control points in the lest-squres squres sense.,

12 Methods: Finl Registrtion Wrp ll LV surfce points nd regrid.

13 Methods: Finl Registrtion Comprison between originl nd wrped LV surfces.

14 Methods: Vlidtion 5. Vlidting the Algorithm LV Surfce Dt 11 gted myocrdil perfusion SPECT studies (EF:32->75%) LV endo nd epicrdil surfces t ED, ES nd MID(ED,ES) re produced by the 4D-MSPECT softwre. Coronry Tree Model Mthemticlly model coronry rtery tree on ED epicrdil surfces.

15 Sttisticl Anlysis Methods: Vlidtion ED, MID, nd ES epicrdil surfces re wrped to the simulted ED coronry tree using the new lgorithm. Men bsolute error (MAE) is computed between the true ED surfce nd its wrped estimte ~ ~ (, ~. S ( h, v) S h v ) MAE = 1 NM N M i= 1 j= 1 S( h, v i j ) ~ ~ S ( h, v~ i j ) Runtime Anlysis Tests were run on n AMD AthlonXP 1.5GHz processor with 512MB of RAM compiled in C++. The runtime in seconds is n verge of 5 runs.

16 Results: Full Coronry Tree Errors of MID LV Surfce Wrped to ED Coronry Tree Before nd After Wrping (1 study with EF=51%)

17 Results: Full Coronry Tree Errors Using the Full Coronry Artery Tree Segment Apex Lterl Inferior Septl Anterior All Error (mm) 0.7 ± ± ± ± ± ±0.7

18 Results: Mid LCx Blockge Errors from Removing the Mid LCx nd OM2 Segment Apex Lterl Inferior Septl Anterior All Error (mm) 0.8 ± ± ± ± ± ±0.7

19 Results: Mid LAD Blockge Errors from Removing the Mid LAD nd D2 Segment Apex Lterl Inferior Septl Anterior All Error (mm) 1.6 ± ± ± ± ± ±1.0

20 Results: RCA Blockge Errors from Removing the PDA Segment Apex Lterl Inferior Septl Anterior All Error (mm) 1.1 ± ± ± ± ± ±1.5

21 Results: Sttisticl Anlysis Tble of Men Wrping Error (mm) of 11 Studies Blockge Frme None Mid LAD Mid LCx RCA ED -> > ED 0.03 ± ± ± ±0.01 MID -> > ED 0.61 ± ± ± ±0.32 ES -> > ED 0.92 ± ± ± ±0.48

22 Results: Runtime Anlysis Tble of Runtimes for Significnt Subroutines Subroutine Runtime Avg. (sec) Subroutine Runtime Avg. (sec) Find Closest Pirs Find Anchor nd Closest Pirs Compute Affine Coefficients Compute Curved Coefficients Affine Trnsform Surfce Curved Trnsform Surfce Regrid Registered Surfce Regrid Wrped Surfce Totl Runtime Averge 1.41 sec

23 Conclusions Wrping lgorithm demonstrted excellent ccurcy in fusing ECT surfces with full nd prtil CTA coronry trees (< 2mm vg. error). Wrping lgorithm is fully utomted, fst (<2sec), nd cliniclly pplicble for ECT studies. Correltion of coronry blockge with perfusion bnormlity cn be chieved without degrdtion to the high resolution coronry dt.