Thibault Marin

A quantitative evaluation study of four-dimensional gated cardiac SPECT reconstruction

In practice, gated cardiac SPECT images suffer from a number of degrading factors, including distance-dependent blur, attenuation, scatter and increased noise due to gating. Recently, we proposed a motion-compensated approach for four-dimensional (4D) reconstruction for gated cardiac SPECT and demonstrated that use of motion-compensated temporal smoothing could be effective for suppressing the increased noise due to lowered counts in individual gates. In this work, we further develop this motion-compensated 4D approach by also taking into account attenuation and scatter in the reconstruction process, which are two major degrading factors in SPECT data. In our experiments, we conducted a thorough quantitative evaluation of the proposed 4D method using Monte Carlo simulated SPECT imaging based on the 4D NURBS-based cardiac-torso (NCAT) phantom. In particular, we evaluated the accuracy of the reconstructed left ventricular myocardium using a number of quantitative measures including regional bias-variance analyses and wall intensity uniformity. The quantitative results demonstrate that use of motion-compensated 4D reconstruction can improve the accuracy of the reconstructed myocardium, which in turn can improve the detectability of perfusion defects. Moreover, our results reveal that while traditional spatial smoothing could be beneficial, its merit would become diminished with the use of motion-compensated temporal regularization. As a preliminary demonstration, we also tested our 4D approach on patient data. The reconstructed images from both simulated and patient data demonstrated that our 4D method can improve the definition of the LV wall.

Generalization Evaluation of Machine Learning Numerical Observers for Image Quality Assessment

In this paper, we present two new numerical observers (NO) based on machine learning for image quality assessment. The proposed NOs aim to predict human observer performance in a cardiac perfusion-defect detection task for single-photon emission computed tomography (SPECT) images. Human observer (HumO) studies are now considered to be the gold standard for task-based evaluation of medical images. However such studies are impractical for use in early stages of development for imaging devices and algorithms, because they require extensive involvement of trained human observers who must evaluate a large number of images.

Numerical Surrogates for Human Observers in Myocardial Motion Evaluation From SPECT Images

In medical imaging, the gold standard for image-quality assessment is a task-based approach in which one evaluates human observer performance for a given diagnostic task (e.g., detection of a myocardial perfusion or motion defect). To facilitate practical task-based image-quality assessment, model observers are needed as approximate surrogates for human observers. In cardiac-gated SPECT imaging, diagnosis relies on evaluation of the myocardial motion as well as perfusion. Model observers for the perfusion-defect detection task have been studied previously, but little effort has been devoted toward development of a model observer for cardiac-motion defect detection. In this work, we describe two model observers for predicting human observer performance in detection of cardiac-motion defects. Both proposed methods rely on motion features extracted using previously reported deformable mesh model for myocardium motion estimation. The first method is based on a Hotelling linear discriminant that is similar in concept to that used commonly for perfusion-defect detection. In the second method, based on relevance vector machines (RVM) for regression, we compute average human observer performance by first directly predicting individual human observer scores, and then using multi reader receiver operating characteristic analysis. Our results suggest that the proposed RVM model observer can predict human observer performance accurately, while the new Hotelling motion-defect detector is somewhat less effective.

Deformable left-ventricle mesh model for motion-compensated filtering in cardiac gated SPECT

PURPOSE In this article, the authors present a motion-compensated spatiotemporal processing algorithm to reduce noise in cardiac gated SPECT. Cardiac gated SPECT data are particularly noisy because the acquired photon data are divided among a number of time frames (gates). Classical spatial reconstruction and processing techniques offer noise reduction but they are usually applied on each frame separately and fail to utilize temporal correlation between frames. METHODS In this work, the authors present a motion-compensated spatiotemporal postreconstruction filter offering noise reduction while minimizing motion-blur artifacts. The proposed method can be used regardless of the type of image-reconstruction method (analytical or iterative). The between-frame volumetric myocardium motion is estimated using a deformable mesh model based on the model of the myocardial surfaces. The estimated motion is then used to perform spatiotemporal filtering along the motion trajectories. Both the motion-estimation and spatiotemporal filtering methods seek to maintain the wall brightening seen during cardiac contraction. Wall brightening is caused by the partial volume effect, which is usually viewed as an artifact; however, wall brightening is a useful signature in clinical practice because it allows the clinician to visualize wall thickening. Therefore, the authors seek in their method to preserve the brightening effect. RESULTS The authors find that the proposed method offers better noise reduction than several existing methods as quantitatively evaluated by signal-to-noise ratio, bias-variance plots, and ejection fraction analysis as well as on tested clinical data. CONCLUSIONS The proposed method mitigates for noise in cardiac gated SPECT images using a postreconstruction motion-compensated filtering approach. Visual as well as quantitative evaluation show considerable improvement in image quality.

Channelized relevance vector machine as a numerical observer for cardiac perfusion defect detection task

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.

Using GPUs to Accelerate Advanced MRI Reconstruction with Field Inhomogeneity Compensation

Publisher Summary This chapter focuses on a GPU implementation for a fast advanced non-Cartesian MRI reconstruction algorithm with field inhomogeneity compensation. Magnetic resonance imaging (MRI) is a flexible diagnostic tool, providing image contrast relating to the structure, function, and biochemistry of virtually every system in the body. However, the technique is generally slow and has low sensitivity, which limits its application in the clinical environment. Several challenges exist that limit the application of MRI in the clinical environment. Traditionally, the main limitations in MRI have been due to the manner in which data are sampled in clinical scans. The techniques of tiling have been applied with constant memory, loop invariant code motion, storing variables in registers, and using single-precision floating-point computations on the GPU kernels. The parallel structure of the reconstruction algorithms makes it suitable for parallel programming on GPUs. Accelerating this kind of algorithm can allow for more accurate image reconstruction while keeping computation times short enough for clinical use. Thus, the use of GPUs will enable improved trade-offs between data acquisition time, signal-to-noise ratio, and the severity of artifacts owing to nonideal physical effects during the MRI imaging experiment.

Numerical observer for cardiac motion assessment

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.

Motion compensated spatio-temporal filtering of cardiac gated SPECT images

Here we describe a post-reconstruction motion-compensated spatio-temporal processing algorithm for cardiac gated SPECT using estimated myocardium motion. In SPECT systems, radioactive dose limitations result in low photon count and therefore, reconstructed images are corrupted by noise. This is even more so in gated SPECT where the data is divided in a number of time frames. Existing noise reduction methods include spatio-temporal filtering, however, temporal filtering usually does not account for the heart motion thus introducing an artifact known as motion blur. This paper presents a motion-compensated temporal filtering method along with a motion estimation algorithm. Motion estimation is performed using a deformable mesh model, fitted to the left ventricle, which can track the myocardium displacement between the time frames. Temporal filtering is then applied utilizing the estimated myocardium displacement. Additionally, both motion estimation and temporal filtering account for the partial volume effect which is manifested by an image brightening effect when the heart wall thickens. Thickening is an important clinical indicator and as such has to be preserved by temporal filtering. The presented quantitative assessments demonstrate that the proposed method provide better images while preserving the brightening effect in comparison to methods used in clinical practice.

Motion-compensated reconstruction of gated cardiac SPECT images using a deformable mesh model

We propose an algorithm for iterative, motion-compensated reconstruction of cardiac-gated SPECT. Dose limitations in SPECT lead to high level of noise in the projection data and further in the reconstructed images. Several reconstruction techniques have been reported to mitigate for the noise effects but they process each time frame individually and do not account for data temporal correlation. Advanced methods that allow for motion-compensated noise reduction use uniformly sampled pixels grid to represent images. Here we present a motion-compensated 4D reconstruction algorithm using content adaptive deformable mesh model (which is based on a deformable non-uniform sampling grid) for cardiac-gated SPECT. The proposed method tracks myocardial motion and utilizes the estimated motion to apply a cardiac-motion compensated temporal smoothing constraint during reconstruction. The temporal constraint is enforced between iterations of mesh based maximum-likelihood expectation-maximization algorithm. Specifically, temporal filtering is applied, in mesh domain, along the motion trajectory between iterations. The motion trajectory is estimation using our previously reported deformable mesh motion estimation technique. Visual comparisons as well as quantitative evaluation show that the proposed method achieves better noise reduction compared to several clinically used methods.

Motion-compensated temporal summation of cardiac gated SPECT images using a deformable mesh model

We propose a motion-compensated non-rigid summation method for noise reduction in cardiac gated SPECT. This approach generates a static SPECT image containing counts from all frames of the gated sequence while accounting for heart motion to avoid motion-blur artifact. Static cardiac images typically suffer from heart motion occurring during acquisition which introduces the so-called motion blur artifact. Gated acquisitions, on the other hand, are characterized by lower counts in each individual frame, thus resulting in noisy images. Methods have been proposed to sum the gated sequence along the time dimension while accounting for heart motion, but they do not account for partial volume effect, manifested by an intensity increase as the myocardium contracts. The partial volume effect, a useful diagnostic feature has to be accounted for during both motion estimation and temporal summation. The proposed method relies on a deformable mesh model to estimate heart motion while accounting for the partial volume effect. The estimated motion is further used to perform non-rigid summation along the time dimension. We show that the proposed method yields visual improvement on clinical data. In addition, quantitative evaluation from phantom studies proves that the proposed method achieves better noise reduction performance than available clinical techniques.