1. Model-based Automatic AC/PC Detection on Three-dimensional MRI Scans Babak A. Ardekani, Ph.D., Alvin H. Bachman, Ph.D., Ali Tabesh, Ph.D. The Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY

2. The anterior and posterior commissures are bundles of transverse white matter fibers that connect the two cerebral hemispheres

3. The AC and PC landmarks are intersection points of these fibers with the mid-sagittal plane AC PC

4. Aim To develop an algorithm for automatic detection of the AC/PC on 3D structural MRI scans

5. Applications Orientation of the human brain for computerized image analysis Definition of coordinate systems in brain atlases (Talairach-Tournoux; Schaltenbrand-Wahren) Placement and orientation of the imaging FOV in MRI acquisition Image registration

6. Example application FOV placement for MRI acquisition

7. Method 3D template matching Normalized cross-correlation (NCC) similarity measure

8. Template definition AC/PC and MPJ landmarks are manually placed on example images by an expert

9. Detect the mid-sagittal plane (MSP) Detect midbrain-pons junction (MPJ) Detect AC/PC Update MSP MRI volume Save AC/PD Save MSP MPJ template AC/PC templates Algorithm

10. MSP detection Ax + By + Cz = 1 Ardekani et al., IEEE Trans. Medical Imaging, 1997.

11. Detect the mid-sagittal plane (MSP) Detect midbrain-pons junction (MPJ) Detect AC/PC Update MSP MRI volume Save AC/PD Save MSP MPJ template AC/PC templates Algorithm

12. MPJ detection Candidate MPJ points are detection on a circular search region by template matching using NCC

13. Detect the mid-sagittal plane (MSP) Detect midbrain-pons junction (MPJ) Detect AC/PC Update MSP MRI volume Save AC/PD Save MSP MPJ template AC/PC templates Algorithm

14. AC/PC detection The AC/PC are detected based on each possible MPJ location

15. AC/PC detection The final decision is made by adding the NCC’s of all three landmarks

16. Detect the mid-sagittal plane (MSP) Detect midbrain-pons junction (MPJ) Detect AC/PC Update MSP MRI volume Save AC/PD Save MSP MPJ template AC/PC templates Algorithm

17. MRI data 36 healthy volunteers, 17 patients with chronic schizophrenia (total: 53 scans) Siemens Vision 1.5T 3D T 1 -weighted MPRAGE structural MRI scans TR=11.6 ms, TE=4.9 ms,  =8 , Matrix=256×256×190, 1 mm 3 voxels

18. Template definition 3 patient (top row) and 3 control (bottom row) scans were used to construct templates for AC/PC and MPJ

19. Results Qualitatively correct AC/PC locations were detected on 52 of the 53 cases.

20. Results The 1 scan on which the algorithm failed

21. Results 5 of 53 scans had severe artifacts

22. Results Processing time: 6 s on Pentium 4, 3.2 GHz (4.5 s MSP detection + 1.5 s AC/PC detection); 2 s on Quad Core Intel Xeon E5430, 2.66 GHz

23. Results: Manual vs. automatic detection ACPC Average error0.86 mm0.85 mm Maximum error1.60 mm1.74 mm Error < 1.0 mm34/4233/42 3D Euclidean distance (error) between manually and automatically detected AC/PC in 42 scans (53-6-5=42)

24. Summary Fast, accurate, robust, and fully automatic AC/PC detection Algorithm can be trained for contrasts other than T 1 (e.g., T 2 -weighted FSE) Algorithm shows robustness with respect to field strength, pulse sequence parameters, subject population Available on www.nitrc.org