Y. Ogura, I. Narabayashi, K. Utsunomiya, T. Komori, I. Adachi, A. Stundžia*, P. Dufort*, M. Ichise**.
Osaka Medical College, Takatsuki, Japan;
*Advanced Biologic Corp., Toronto, Canada;
**National Institute of Mental Health, Bethesda, USA
Objectives: We developed a rapid non-linear method to register and fuse 3D lung CT images with SPECT 99mTc MAA perfusion, 81mKr ventilation and ventilation–perfusion normalized (V/Q) images called the LungGuide Image Fusion Method (LG-IFM).
Methods:
CT,
SPECT
99mTc-MAA perfusion,
and 81mKr
ventilation lung
imaging was performed for 12 patients with suspected lung defects
including embolism, emphysema, and tumour nodules. For each patient,
the LG-IFM technique was
applied in four stages. First, a plane of approximate
left-right symmetry, labeled as the midsagittal (MS) plane,
separating the left lung from the right lung, was found. This MS
plane identification was performed independently for CT and SPECT
image data. Second, the optimal linear match
between the original lung SPECT and CT images was found by aligning
the MS planes and then rigidly translating (shifting) and rotating
the SPECT image in 3D such that the maximum overlap between the two
images was achieved, as measured by a statistical correlation
function. Third, the most probable SPECT outer lung surface was
estimated and corresponding anatomic reference points on the CT and
SPECT lung surfaces were respectively identified. A one-to-one
correspondence between all points on the CT lung surface and all
points on the SPECT lung surface was generated giving a continuous
3D vector displacement field
from one surface to the other. Finally, a scalar deformation energy
function
was constructed for a volume displacement field
defined on the interior points of the lungs, with the surface mapping
as a boundary constraint. Minimization of the energy function yielded
a non-linear mapping of the SPECT volume onto the CT volume, or
equivalently 3D nonlinear image fusion of CT with SPECT.
Results: The processing of same patient 3D LG-IFM registration and fusion of lung CT images (16 million voxels 32 MB) with SPECT ventilation, SPECT perfusion, and V/Q images (1 million voxels 2 MB) took 300 seconds on a PC workstation (Windows 2000 on a 2 GHz Pentium 4 with 512MB of RAM). The accuracy of image registration and fusion were confirmed visually for all twelve cases by 3 independent readers, using the lung defects and independent markers as reference. Conclusion: The LungGuide Image Fusion Method (LG-IFM) appears to be a rapid and accurate method of generating fused 3D images of anatomic CT and functional SPECT lung studies within the same subject, and may be a clinically useful tool.