Compressed sensing requires compressible data, incoherent acquisition and a nonlinear reconstruction algorithm to force creation of a compressible image consistent with the acquired data. MRI images are compressible using various transforms (commonly total variation or wavelets). Incoherent acquisition of MRI data by appropriate selection of pseudo-random or non-Cartesian locations in k-space is straightforward. Increasingly, commercial scanners are sold with enough computing power to enable iterative reconstruction in reasonable times. Therefore integration of compressed sensing into commercial MRI products and clinical practice is beginning. MRI frequently requires the tradeoff of spatial resolution, temporal resolution and volume of spatial coverage to obtain reasonable scan times. Compressed sensing improves scan efficiency and reduces the need for this tradeoff. Benefits to the user will include shorter scans, greater patient comfort, better image quality, more contrast types per patient slot, the enabling of previously impractical applications, and higher throughput. Challenges to vendors include deciding which applications to prioritize, guaranteeing diagnostic image quality, maintaining acceptable usability and workflow, and acquisition and reconstruction algorithm details. Application choice depends on which customer needs the vendor wants to address. The changing healthcare environment is putting cost and productivity pressure on healthcare providers. The improved scan efficiency of compressed sensing can help alleviate some of this pressure. Image quality is strongly influenced by image compressibility and acceleration factor, which must be appropriately limited. Usability and workflow concerns include reconstruction time and user interface friendliness and response. Reconstruction times are limited to about one minute for acceptable workflow. The user interface should be designed to optimize workflow and minimize additional customer training. Algorithm concerns include the decision of which algorithms to implement as well as the problem of optimal setting of adjustable parameters. It will take imaging vendors several years to work through these challenges and provide solutions for a wide range of applications.
Many studies have been conducted on the utilization of solid state detectors for computed tomography (CT). One of the important performance parameters for the solid state detector has been shown to be the primary speed and afterglow. In this paper, we present a detailed investigation on the signal decay characteristics of the HiLightTM scintillating detector. We first develop an analytical model to fully characterize the detector impulse response. The model sensitivity to x-ray photon energy, detector aging, and radiation exposure is then established and analyzed. The impact of various decay time constants on CT image quality is subsequently illustrated with computer simulations and phantom experiments. Finally, a recursive correction approach is derived and evaluated.
KEYWORDS: Data acquisition, Reconstruction algorithms, Scanners, Computer simulations, Computed tomography, Image quality, Medical imaging, Sensors, Signal to noise ratio, Collimators
This paper deals with methods of reducing the total time required to acquire the projection data for a set of contiguous CT images. Normally during the acquisition of a set of slices, the patient is held stationary during data collection and translated to the next axial location during an inter-scan delay. The authors will demonstrate, using computer simulations and scans of volunteers on a modified scanner, how acceptable image quality is achieved if the patient translation time is overlapped with data acquisition. If the concurrent patient translation is ignored, structured artifacts significantly degrade resulting reconstructions. A number of algorithms are presented to minimize the structured artifacts through the use of projection modulation using the data from individual and multiple slices. Comparison is made of the methods with respect to structured artifacts, noise, resolution and susceptibility to motion. Review of preliminary clinical feedback by a panel of radiologists has indicated that the residual image degradation is tolerable for selected applications when it is critical to acquire more slices in a patient breathing cycle than is possible with conventional scanning. The method is a useful protocol when some image quality can be traded for increased scan rate. Applications include increased contrast utilization and minimization of registration artifacts.
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