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Multi-atlas cardiac PET segmentation.

Sally Ji Who Kim1, Seongho Seo2, Hyeon Sik Kim3

  • 1Department of Nuclear Medicine, Seoul National University, Seoul, Republic of Korea; Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.

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PubMed
Summary
This summary is machine-generated.

A new multi-atlas segmentation method improves cardiac PET and SPECT image analysis by accurately segmenting myocardial structures. This approach offers better correlation with manual segmentation than single-atlas methods, without needing prior tracer distribution information.

Keywords:
AtlasMyocardial PETQuantificationSegmentation

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Area of Science:

  • Nuclear Medicine
  • Medical Imaging
  • Cardiovascular Research

Background:

  • Cardiac PET and SPECT imaging are crucial for assessing myocardial function and disease.
  • High variability in tracer uptake characteristics poses challenges for accurate image segmentation.
  • Existing single-atlas segmentation methods can lead to underestimation of perfusion values.

Purpose of the Study:

  • To propose and evaluate a multi-atlas based segmentation method for cardiac PET and SPECT images.
  • To address the variability in tracer uptake in the myocardium.
  • To compare the performance of the multi-atlas method against manual segmentation and single-atlas approaches using dynamic myocardial PET.

Main Methods:

  • Developed a multi-atlas based segmentation technique utilizing spatial normalization and pre-defined regions-of-interest (ROIs).
  • Generated multiple left ventricular (LV) atlases from manually segmented images of SD rats undergoing PET/CT scans.
  • Compared segmentation performance using leave-one-out cross-validation and estimated polar-maps of kinetic parameters.

Main Results:

  • The highest template yielded the lowest root-mean-square error (RMSE) between source images and best-matching templates (0.06-0.11).
  • Multi-atlas segmentation demonstrated a high correlation (r² = 0.92) with manual segmentation, outperforming single-atlas methods (r² = 0.88).
  • Single-atlas based segmentation significantly underestimated high perfusion values.

Conclusions:

  • The proposed multi-atlas method accurately segments cardiac PET/SPECT images without requiring prior tracer distribution information.
  • This approach is versatile and applicable to various cardiac PET or SPECT imaging agents.
  • The method enhances the reliability of myocardial perfusion assessment in nuclear cardiology.