SPECT images power probabilistic atlas for middle cerebral artery

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Probabilistic maps, or atlases, are powerful tools in medical imaging that can help clinicians quantify the extent of disease and prognosticate treatment options. These atlases rely heavily on imaging data compiled from a clinical population. Until recently, maps of cerebral artery territories were limited to injection studies in cadavers.

A team from the department of nuclear medicine and Medical Research Institute at Pusan National University Hospital in Busan, Republic of Korea, has developed a population-based probabilistic anatomic map of the cerebral blood flow distribution of the middle cerebral artery (MCA), which might have a crucial role in the assessment of cerebrovascular diseases.

“The absence of a clear definition of the MCA territory has encouraged an alternate strategy for the development of probabilistic maps of this territory based on perfusion images,” the scientists wrote in a recent issue of the Journal of Nuclear Medicine.

The team used a dual-head SPECT system (Vertex, Philips Medical Systems) to image 29 patients who received stent placement previously for MCA stenosis (15 left MCA stenosis and 14 right MCA stenosis).

“Because cerebral angiography is an extremely invasive procedure, a population of normal healthy individuals could not be recruited,” the authors noted. “We excluded patients who had major complications, such as a major cerebral infarction or hemorrhage of the nonaffected cerebral hemisphere, chronic complete obstruction of the MCA, stenosis distal to the M1 bifurcation of the nonaffected artery, previous head trauma, psychiatric illness, alcoholism and drug abuse, epilepsy, and kidney, liver, or lung failure.”

The team conducted basal and MCA SPECT studies of the patient cohort. The basal studies were performed 15 minutes after injection of 750MBq of 99mTc-Ethylcysteinate Dimer (99mTc-ECD) and an MCA brain SPECT exam was performed, using the same radiotracer, during a follow-up angiography for the evaluation of in-stent restenosis.

The researchers utilized 18 of the MCA SPECT studies to generate their probabilistic map, because 11 of the patients demonstrated uneven uptake distribution of the 99mTc-ECD in their brains. The scientists then generated perfusion and extent probabilistic maps using mirrored images of the injected cerebral hemispheres to increase the reliability of the probabilistic maps by increasing the sample size.

Extent probabilistic map for vascular territory of MCA. MCA reached to deep structures of brain, including internal capsule, caudate nucleus, putamen, globus pallidus, insular cortex, and thalamus with a high-extent probability. Image and caption by permission of SNM from “Probabilistic Anatomic Mapping of Cerebral Blood Flow Distribution of the Middle Cerebral Artery” by Seong-Jang Kim, MD, et. al.  

The data obtained by the group allowed them to generate two probabilistic maps; a probabilistic MCA atlas of the individual binary contours of its territory and a map of the vascular contribution from the MCA.

The scientists foresee multiple clinical utilizations for the probabilistic MCA atlas:

  • For quantification of MCA infarction using a volume-of-interest method and follow-up after revascularization treatment using surgery or intraarterial stent placement. In addition, it could have a potential role for the prognostication of MCA stenosis or infarction.
  • Because the MCA probabilistic map is 3D, it could be used to overlay results of group comparisons, giving probability-based information.
  • The stereotaxically normalized MCA probabilistic map contains the probabilistic information for any given voxel of the MCA-flow territory.
  • Lastly, the map could be used for individual analysis of the MCA territory.

“This map could be another potential tool for analysis of the major cerebral artery distribution, especially the MCA,” the authors wrote. “Furthermore, the probabilistic MCA atlas could be used to define the object delineation of the MCA territory, to quantify ischemic disease affecting the MCA, and to predict prognosis and to stratify the risk of cerebrovascular diseases, especially affecting the MCA.”