A support-based reconstruction for SENSE MRI

A Survey of the Techniques for The Identification and Classification of Human Actions from Visual Data

Recognition of human actions form videos has been an active area of research because it has applications in various domains. The results of work in this field are used in video surveillance, automatic video labeling and human-computer interaction, among others. Any advancements in this field are tied to advances in the interrelated fields of object recognition, spatio- temporal video analysis and semantic segmentation. Activity recognition is a challenging task since it faces many problems such as occlusion, view point variation, background differences and clutter and illumination variations. Scientific achievements in the field have been numerous and rapid as the applications are far reaching. In this survey, we cover the growth of the field from the earliest solutions, where handcrafted features were used, to later deep learning approaches that use millions of images and videos to learn features automatically. By this discussion, we intend to highlight the major breakthroughs and the directions the future research might take while benefiting from the state-of-the-art methods.

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 × 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.

Quantification of perfusion in murine myocardium: A retrospectively triggered T1 -based ASL method using model-based reconstruction

PURPOSE

A method for the quantification of perfusion in murine myocardium is demonstrated. The method allows for the reconstruction of perfusion maps on arbitrary time points in the heart cycle while addressing problems that arise due to the irregular heart beat of mice.

METHODS

A flow-sensitive alternating inversion recovery arterial spin labeling method using an untriggered FLASH-read out with random sampling is used. Look-Locker conditions are strictly maintained. No dummy pulses or mechanism to reduce deviation from Look-Locker conditions are needed. Electrocardiogram and respiratory data are recorded for retrospective gating and triggering. A model-based technique is used to reconstruct missing k-space data to cope with the undersampling inherent in retrospectively gated methods. Acquisition and reconstruction were validated numerically and in phantom measurements before in vivo experimentation.

RESULTS

Quantitative perfusion maps were acquired within a single slice measurement time of 11 min. Perfusion values are in good accordance to literature values. Myocardial infarction could be clearly visualized and results were confirmed with histological results.

CONCLUSION

The proposed method is capable of producing quantitative perfusion maps on arbitrary positions in the heart cycle within a short measurement time. The method is robust against irregular breathing patterns and heart rate changes and can be implemented on all scanners.

Energy preserved sampling for compressed sensing MRI

The sampling patterns, cost functions, and reconstruction algorithms play important roles in optimizing compressed sensing magnetic resonance imaging (CS-MRI). Simple random sampling patterns did not take into account the energy distribution in k-space and resulted in suboptimal reconstruction of MR images. Therefore, a variety of variable density (VD) based samplings patterns had been developed. To further improve it, we propose a novel energy preserving sampling (ePRESS) method. Besides, we improve the cost function by introducing phase correction and region of support matrix, and we propose iterative thresholding algorithm (ITA) to solve the improved cost function. We evaluate the proposed ePRESS sampling method, improved cost function, and ITA reconstruction algorithm by 2D digital phantom and 2D in vivo MR brains of healthy volunteers. These assessments demonstrate that the proposed ePRESS method performs better than VD, POWER, and BKO; the improved cost function can achieve better reconstruction quality than conventional cost function; and the ITA is faster than SISTA and is competitive with FISTA in terms of computation time.