6.2. Manual Registration

6.2.1. Examples

Use a mouse or gesture device to manually rotate/translate/scale a pre-operative CT dataset so that it aligns with the intra-operative scene, e.g. live video.

• To get a feel for this try manipulating this model, provided on-line by Kitware.

• To overlay a 3D liver CT model over a 2D video image, giving an augmented reality feel, follow the Python Setup, then try:

python mphy0026_manual_registration.py \
-b doc/registration/liver_background.png \
-m doc/registration/liver.vtp \
-c doc/registration/liver_camera.txt

in the root of the MPHY0026 (i.e. this course) git repo.

Or, see Manual Alignment done with SmartLiver:

Discussion

• how usable is it?

• Which is better or worse: Manual alignment, but reliable or Automatic alignment, but only semi-reliable?

6.2.2. Papers

No algorithm to speak of, so these are examples of how it’s used:

• [Pratt2012] : Manually align on iPad, using gestures, for image-guided partial nephrectomy

• [Thompson2013a] : Manually align, keyboard controls, for radical prostatectomy

6.2.3. Typical Performance

Pros:

• Robust (no algorithm to fail)

• Easy to implement for rigid/scaling

• Easy to validate, on a phantom, and get approved

Cons:

• Not suitable for non-rigid alignment

• Normally inaccurate

• Time consuming for the user

• Highly user dependent

• How to interact with the device? who? is the user sterile?

• Hard to re-register, due to the above mentioned time and user variability for instance

Accuracy:

• Depends on anatomy and user interface, and user

• e.g. 10-20mm is not uncommon with deformable anatomy, maybe < 1-3mm with neuro?