Improving Diagnostic Accuracy in Low-Dose SPECT Myocardial Perfusion Imaging With Convolutional Denoising Networks
| Autor: | Albert Juan Ramon, P. Hendrik Pretorius, Miles N. Wernick, Yongyi Yang, Karen L. Johnson, Michael A. King |
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| Rok vydání: | 2020 |
| Předmět: |
Noise reduction
Image processing Iterative reconstruction Single-photon emission computed tomography Article 030218 nuclear medicine & medical imaging 03 medical and health sciences Myocardial perfusion imaging 0302 clinical medicine Image Processing Computer-Assisted medicine Humans Electrical and Electronic Engineering Mathematics Tomography Emission-Computed Single-Photon Radiological and Ultrasound Technology medicine.diagnostic_test Phantoms Imaging business.industry Supervised learning Myocardial Perfusion Imaging Computer Science Applications Noise ROC Curve Tomography Nuclear medicine business Algorithms Software |
| Zdroj: | IEEE Trans Med Imaging |
| ISSN: | 1558-254X 0278-0062 |
| Popis: | Lowering the administered dose in SPECT myocardial perfusion imaging (MPI) has become an important clinical problem. In this study we investigate the potential benefit of applying a deep learning (DL) approach for suppressing the elevated imaging noise in low-dose SPECT-MPI studies. We adopt a supervised learning approach to train a neural network by using image pairs obtained from full-dose (target) and low-dose (input) acquisitions of the same patients. In the experiments, we made use of acquisitions from 1,052 subjects and demonstrated the approach for two commonly used reconstruction methods in clinical SPECT-MPI: 1) filtered backprojection (FBP), and 2) ordered-subsets expectation-maximization (OSEM) with corrections for attenuation, scatter and resolution. We evaluated the DL output for the clinical task of perfusion-defect detection at a number of successively reduced dose levels (1/2, 1/4, 1/8, 1/16 of full dose). The results indicate that the proposed DL approach can achieve substantial noise reduction and lead to improvement in the diagnostic accuracy of low-dose data. In particular, at 1/2 dose, DL yielded an area-under-the-ROC-curve (AUC) of 0.799, which is nearly identical to the AUC = 0.801 obtained by OSEM at full-dose ( ${p}$ -value = 0.73); similar results were also obtained for FBP reconstruction. Moreover, even at 1/8 dose, DL achieved AUC = 0.770 for OSEM, which is above the AUC = 0.755 obtained at full-dose by FBP. These results indicate that, compared to conventional reconstruction filtering, DL denoising can allow for additional dose reduction without sacrificing the diagnostic accuracy in SPECT-MPI. |
| Databáze: | OpenAIRE |
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