A Readout IC Using Two-Step Fastest Signal Identification for Compact Data Acquisition of PET Systems

A readout integrated circuit (ROIC) using two-step fastest signal identification (FSI) is proposed to reduce the number of input channels of a data acquisition (DAQ) block with a high-channel reduction ratio. The two-step FSI enables the proposed ROIC to filter out useless input signals that arise from scattering and electrical noise without using complex and bulky circuits. In addition, an asynchronous fastest signal identifier and a self-trimmed comparator are proposed to identify the fastest signal without using a high-frequency clock and to reduce misidentification, respectively. The channel reduction ratio of the proposed ROIC is 16: 1 and can be extended to 16 x N: 1 using N ROICs. To verify the performance of the two-step FSI, the proposed ROIC was implemented into a gamma photon detector module using a Geiger-mode avalanche photodiode with a lutetium-yttrium oxyorthosilicate array. The measured minimum detectable time is 1 ns. The difference of the measured energy and timing resolution between with and without the two-step FSI are 0.8% and 0.2 ns, respectively, which are negligibly small. These measurement results show that the proposed ROIC using the two-step FSI reduces the number of input channels of the DAQ block without sacrificing the performance of the positron emission tomography (PET) systems

A Support-Based Reconstruction for SENSE MRI

A novel, rapid algorithm to speed up and improve the reconstruction of sensitivity encoding (SENSE) MRI was proposed in this paper. The essence of the algorithm was that it iteratively solved the model of simple SENSE on a pixel-by-pixel basis in the region of support (ROS). The ROS was obtained from scout images of eight channels by morphological operations such as opening and filling. All the pixels in the FOV were paired and classified into four types, according to their spatial locations with respect to the ROS, and each with corresponding procedures of solving the inverse problem for image reconstruction. The sensitivity maps, used for the image reconstruction and covering only the ROS, were obtained by a polynomial regression model without extrapolation to keep the estimation errors small. The experiments demonstrate that the proposed method improves the reconstruction of SENSE in terms of speed and accuracy. The mean square errors (MSE) of our reconstruction is reduced by 16.05% for a 2D brain MR image and the mean MSE over the whole slices in a 3D brain MRI is reduced by 30.44% compared to those of the traditional methods. The computation time is only 25%, 45%, and 70% of the traditional method for images with numbers of pixels in the orders of 103, 104, and 105–107, respectively

A Support-Based Reconstruction for SENSE MRI

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