Task-driven image acquisition and reconstruction in cone-beam CT

Abstract

This work introduces a task-driven imaging framework that incorporates a mathematical definition of the imaging task, a model of the imaging system, and a patient-specific anatomical model to prospectively design image acquisition and reconstruction techniques to optimize task performance. The framework is applied to joint optimization of tube current modulation, view-dependent reconstruction kernel, and orbital tilt in cone-beam CT. The system model considers a cone-beam CT system incorporating a flat-panel detector and 3D filtered backprojection and accurately describes the spatially varying noise and resolution over a wide range of imaging parameters and in the presence of a realistic anatomical model. Task-based detectability index (d') is incorporated as the objective function in a task-driven optimization of image acquisition and reconstruction techniques. The orbital tilt was optimized through an exhaustive search across tilt angles ranging ±30°. For each tilt angle, the view-dependent tube current and reconstruction kernel (i.e., the modulation profiles) that maximized detectability were identified via an alternating optimization. The task-driven approach was compared with conventional unmodulated and automatic exposure control (AEC) strategies for a variety of imaging tasks and anthropomorphic phantoms. The task-driven strategy outperformed the unmodulated and AEC cases for all tasks. For example, d' for a sphere detection task in a head phantom was improved by 30% compared to the unmodulated case by using smoother kernels for noisy views and distributing mAs across less noisy views (at fixed total mAs) in a manner that was beneficial to task performance. Similarly for detection of a line-pair pattern, the task-driven approach increased d' by 80% compared to no modulation by means of view-dependent mA and kernel selection that yields modulation transfer function and noise-power spectrum optimal to the task. Optimization of orbital tilt identified the tilt angle that reduced quantum noise in the region of the stimulus by avoiding highly attenuating anatomical structures. The task-driven imaging framework offers a potentially valuable paradigm for prospective definition of acquisition and reconstruction protocols that improve task performance without increase in dose.

Task-driven optimization of CT tube current modulation and regularization in model-based iterative reconstruction

“…Following previous work (Gang et al 2015, Stayman and Siewerdsen 2012), we introduce a task-driven imaging framework for prospective optimization of image acquisition and reconstruction using a task-based objective function. Specifically, the objective function seeks to maximize detectability index – a task-based image quality metric commonly used for performance assessment of classification tasks (detection or discrimination).…”

“…The utility of this framework has been demonstrated primarily in the context of interventional imaging where an anatomical model is typically available in the form of a pre-operative or planning CT study (Gang et al 2015, Stayman and Siewerdsen 2012). To adopt the framework in diagnostic CT imaging scenarios, the anatomical model can be derived from a very-low dose 3D scout which replaces the traditional anterior-posterior (AP) and lateral radiographs (Yin et al 2015, Gomez et al 2017).…”

“…For CT or cone-beam CT (CBCT), this framework has been used to identify optimal acquisition trajectories in robotic c-arm systems (Stayman and Siewerdsen 2012), as well as TCM, view-dependent apodization kernels, and orbital tilt for FBP reconstruction (Gang et al 2015). In particular relevance to TCM, we derived the optimal modulation pattern in FBP reconstruction that maximizes detectability index for a range of imaging tasks while taking into account a complete system model including system blur, quantum and electronics noise (Gang et al 2015). It was found that the optimal TCM for radially symmetric task functions are comparable to the minimum variance solution proposed by Gies et al (Gies et al 1999), while that for asymmetric tasks are strongly dependent on the radial frequency of the task function.…”

Fourier Analysis of Noise Characteristics in Cone-Beam Microtomography Laboratory Scanners

“…As experimentally demonstrated by Gang et al [68], therefore, an "atypical" scanning protocol (e.g. the application of different convolution kernels to each projection and/or the irradiation of different x-ray beam intensity at each projection) may provide a better object detectability compared to the "typical" scanning protocol (i.e.…”

Task-Driven Optimization of Fluence Field and Regularization for Model-Based Iterative Reconstruction in Computed Tomography

“…We adopt a similar task-driven imaging framework as proposed in previous studies [25], [26] for prospective design of patient- and task-specific imaging parameters. In this work, we optimized parameters for a known imaging task (with unknown orientation) and leverage an anatomical model based on a previous CT scan.…”