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Medical Physics / Volume 37 / Issue 6 / NUCLEAR MEDICINE PHYSICS

Med. Phys. 37, 2414 (2010); http://dx.doi.org/10.1118/1.3395554 (11 pages)

Registration of myocardial PET and SPECT for viability assessment using mutual information

Martina Marinelli

Nuklearmedinische Klinik der TU München, Ismaningerstraße 22, 81675 München, Germany and Scuola Superiore Sant’Anna, Piazza Martiri della Libertà, 33, 56127 Pisa, Italy

Axel Martinez-Möller and Brian Jensen

Nuklearmedinische Klinik der TU München, Ismaningerstraße 22, 81675 München, Germany and Computer Aided Medical Procedures and Augmented Reality, Fakultät für Informatik/I16, TU München, Boltzmannstraße 3, 85748 Garching bei München, Germany

Vincenzo Positano

Fondazione G. Monasterio CNR–Regione Toscana, Via Moruzzi 1, 56124 Pisa, Italy

Susanne Weismüller, Markus Schwaiger, and Stephan G. Nekolla

Nuklearmedinische Klinik der TU München, Ismaningerstraße 22, 81675 München, Germany

Nassir Navab

Computer Aided Medical Procedures and Augmented Reality, Fakultät für Informatik/I16, TU München, Boltzmannstraße 3, 85748 Garching bei München, Germany

Luigi Landini

Department of Information Engineering, University of Pisa, Via Diotisalvi 2, 56126 Pisa, Italy

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(Received 25 November 2009; accepted 17 March 2010; revised 16 March 2010; published online 5 May 2010)

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Purpose: The combination of sequentially acquired cardiac PET and SPECT data integrating metabolic and perfusion information allows the assessment of myocardial viability, a relevant clinical parameter for the management of patients who have suffered myocardial infarction and are now candidates for complex and cost intensive therapies such as bypass surgery. However, registration of cardiac functional datasets acquired on different imaging systems is limited by the difficulty to define anatomical landmarks and by the relatively poor inherent spatial resolution. In this article, the authors sought to evaluate whether it is possible to automatically register FDG-PET and sestamibi-SPECT cardiac data.
Methods: Automatic rigid registration was implemented with the ITK framework using Mattes mutual information as the similarity measure and a quaternion to represent the rotational component. The goodness of the alignment was evaluated by computing the mean target registration error (mTRE) at the myocardial wall. The registration parameters were optimized for robustness and speed using the data from 11 cardiac patients undergoing both PET and SPECT examinations (training datasets). The optimized algorithm was applied on the PET and SPECT data from 11 further patients (evaluation datasets). Quantitative (mTRE calculation) and visual (scoring method) comparisons were performed between automatic and manual registrations. Moreover, the automatic registration was also compared to the registration implicitly defined in the standard clinical analysis.
Results: The registration parameters were successfully optimized and resulted in a mean mTRE of 1.13 mm and 1.2 s average runtime on standard computer hardware for the training datasets. Automatic registration in the 11 validation datasets resulted in an average mTRE of 2.3 mm, with 7.5 mm mTRE in the worst case and an average runtime of 1.6 s. Automatic registration outperformed manual registrations both for the mTRE and for the visual assessment. Automatic registration also resulted in higher accuracy and better visual assessment as compared to the registration implicitly performed in the standard clinical analysis.
Conclusions: The results demonstrate the possibility to successfully perform mutual information based registration of PET and SPECT cardiac data, allowing an improved workflow for the sequentially acquired cardiac datasets, in general, and specifically for the assessment of myocardial viability.

© 2010 American Association of Physicists in Medicine

ACKNOWLEDGMENT

This research was partly supported by Siemens Healthcare, Erlangen, Germany.

Article Outline

  1. INTRODUCTION
  2. MATERIALS AND METHODS
    1. Patient population
    2. Data acquisition and reconstruction
    3. Registration algorithm
    4. Definition of the gold standard
    5. Method to quantitatively evaluate the goodness of a registration result
    6. Optimization of the registration parameters
    7. Evaluation of the automatic registration algorithm
    8. Comparison with manual registration
    9. Comparison of the registration algorithm to the standard clinical workflow
  3. RESULTS
    1. Optimization of the registration parameters
    2. Evaluation of the automatic registration algorithm
    3. Comparison with manual registration
    4. Comparison of the registration algorithm to the standard clinical workflow
  4. DISCUSSION
  5. CONCLUSION
Digital Object Identifier
http://dx.doi.org/10.1118/1.3395554

KEYWORDS and PACS

Keywords

cardiovascular system, haemorheology, image registration, medical image processing, optimisation, positron emission tomography, single photon emission computed tomography, registration, mutual information, functional images, myocardial viability

PACS

PUBLICATION DATA

ISSN

0094-2405 (print)  

Publisher

$abstract.society.value is a Member of CrossRef American Association of Physicists in Medicine

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