In this paper, we present a numerical observer for image quality assessment, aiming to predict human observer accuracy
in a cardiac perfusion defect detection task for single-photon emission computed tomography (SPECT). In medical
imaging, image quality should be assessed by evaluating the human observer accuracy for a specific diagnostic task.
This approach is known as task-based assessment. Such evaluations are important for optimizing and testing imaging
devices and algorithms. Unfortunately, human observer studies with expert readers are costly and time-demanding. To
address this problem, numerical observers have been developed as a surrogate for human readers to predict human
diagnostic performance. The channelized Hotelling observer (CHO) with internal noise model has been found to predict
human performance well in some situations, but does not always generalize well to unseen data. We have argued in the
past that finding a model to predict human observers could be viewed as a machine learning problem. Following this
approach, in this paper we propose a channelized relevance vector machine (CRVM) to predict human diagnostic scores
in a detection task. We have previously used channelized support vector machines (CSVM) to predict human scores and
have shown that this approach offers better and more robust predictions than the classical CHO method. The comparison
of the proposed CRVM with our previously introduced CSVM method suggests that CRVM can achieve similar
generalization accuracy, while dramatically reducing model complexity and computation time.
In medical imaging, image quality is commonly assessed by measuring the performance of a human observer performing
a specific diagnostic task. However, in practice studies involving human observers are time consuming and difficult to
implement. Therefore, numerical observers have been developed, aiming to predict human diagnostic performance to
facilitate image quality assessment. In this paper, we present a numerical observer for assessment of cardiac motion in
cardiac-gated SPECT images. Cardiac-gated SPECT is a nuclear medicine modality used routinely in the evaluation of
coronary artery disease. Numerical observers have been developed for image quality assessment via analysis of
detectability of myocardial perfusion defects (e.g., the channelized Hotelling observer), but no numerical observer for
cardiac motion assessment has been reported. In this work, we present a method to design a numerical observer aiming
to predict human performance in detection of cardiac motion defects. Cardiac motion is estimated from reconstructed
gated images using a deformable mesh model. Motion features are then extracted from the estimated motion field and
used to train a support vector machine regression model predicting human scores (human observers' confidence in the
presence of the defect). Results show that the proposed method could accurately predict human detection performance
and achieve good generalization properties when tested on data with different levels of post-reconstruction filtering.
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