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.
In this work, we present a four-dimensional reconstruction technique for cardiac gated SPECT images using a content-adaptive deformable mesh model. Cardiac gated SPECT images are affected by a high level of noise.
Noise reduction methods usually do not account for cardiac motion and therefore introduce motion blur-an artifact
that can decrease diagnostic accuracy. Additionally, image reconstruction methods typically rely on uniform
sampling and Cartesian griding for image representation. The proposed method utilizes a mesh representation
of the images in order to utilize the benefits of content-adaptive nonuniform sampling. The mesh model allows
for accurate representation of important regions while significantly compressing the data. The content-adaptive
deformable mesh model is generated by combining nodes generated on the full torso using pre-reconstructed emission
and attenuation images with nodes accurately sampled on the left ventricle. Ventricular nodes are further
displaced according to cardiac motion using our previously introduced motion estimation technique. The resulting
mesh structure is then used to perform iterative image reconstruction using a mesh-based maximum-likelihood
expectation-maximization algorithm. Finally, motion-compensated post-reconstruction temporal filtering is applied
in the mesh domain using the deformable mesh model. Reconstructed images as well as quantitative
evaluation show that the proposed method offers improved image quality while reducing the data size.
In this paper, we present a numerical observer for assessment of cardiac motion in nuclear medicine. Numerical
observers are used in medical imaging as a surrogate for human observers to automatically measure the diagnostic
quality of medical images. The most commonly used quality measurement is the detection performance in a detection
task. In this work, we present a new numerical observer aiming to measure image quality for the task of cardiac motiondefect
detection in cardiac SPECT imaging. The proposed observer utilizes a linear discriminant on features extracted
from cardiac motion, characterized by a deformable mesh model of the left ventricle and myocardial brightening.
Simulations using synthetic data indicate that the proposed method can effectively capture the cardiac motion and
provide an accurate prediction of the human observer performance.
We present a post-reconstruction motion-compensated spatio-temporal filtering method for noise reduction in cardiac
gated SPECT images. SPECT imaging suffers from low photon count due to radioactive dose limitations resulting in a
high noise level in the reconstructed images. This is especially true in gated cardiac SPECT where the total number of
counts is divided into a number of gates (time frames). Classical spatio-temporal filtering approaches, used in gated
cardiac SPECT for noise reduction, do not accurately account for myocardium motion and brightening and therefore
perform sub-optimally. The proposed post-reconstruction method consists of two steps: motion and brightening
estimation and spatio-temporal motion-compensated filtering. In the first step we utilize a left ventricle model and a
deformable mesh structure. The second step, which consists of motion-compensated spatio-temporal filtering, makes use
of estimated myocardial motion to enable accurate smoothing. Additionally, the algorithm preserves myocardial
brightening, a result of partial volume effect which is widely used as a diagnostic feature. The proposed method is
evaluated quantitatively to assess noise reduction and the influence on estimated ejection fraction.