Motion in nuclear cardiology imaging: types, artifacts, detection and correction techniques

In this paper, the authors review the field of motion detection and correction in nuclear cardiology with single photon emission computed tomography (SPECT) and positron emission tomography (PET) imaging systems. We start with a brief overview of nuclear cardiology applications and description of SPECT and PET imaging systems, then explaining the different types of motion and their related artefacts. Moreover, we classify and describe various techniques for motion detection and correction, discussing their potential advantages including reference to metrics and tasks, particularly towards improvements in image quality and diagnostic performance. In addition, we emphasize limitations encountered in different motion detection and correction methods that may challenge routine clinical applications and diagnostic performance.

iKarate: Karate Kata Guidance System

Smart coaching in martial arts is one of the recent research areas in Human Motion Analysis. Numerous moves are performed incorrectly during the performance. In this paper we offer a system that will record the Players' movements using IR (Infrared) camera sensor, store the data in a database, pre-process the data, classify the data using F-DTW (Fast Dynamic Time Warping) and then show the users an accurate report that contains every movement the player had done, their mistakes and how to improve their performance the next time. This approach focuses on the first seven movements of Karate Kata 1 (Hein Shodan). The system has reached an accuracy of 91.07% in classifying the moves and one common mistake for each move.

Blind Source Separation - Based Motion Detector for Sub-Micrometer, Periodic Displacement in Ultrasonic Imaging

Sub-micrometer, periodic motion detection using blind source separation (BSS) via principal component analysis (PCA) is presented in the context of magnetomotive ultrasound (MMUS) imaging and Shearwave Dispersion Ultrasound Vibrometry (SDUV). In MMUS, an oscillating external magnetic field displaces tissue loaded with superparamagnetic iron oxide (SPIO) particles, whereas in SDUV, periodic tissue motion is induced using acoustic radiation force (ARF) to measure visco-elastic properties. BSS motion detection performance in MMUS imaging and SDUV was compared against frequency-phase locked (FPL) and normalized cross-correlation (NCC) motion detectors, respectively, in silico and in experimental phantoms. Parametric MMUS phantom images constructed using the BSS method had nearly twice the SNR of the corresponding images constructed using FPL method when a 0.043 mm or smaller kernel size was used. In FEM models of SDUV, the error in the BSS-estimated viscoelastic properties of simulated materials was < 10%, whereas the error was > 20% using NCC when the simulated SNR was 15 dB. In a calibrated elasticity phantom, the amplitude of the motion was ≤ 0.5 μm for a scanner power level ≤ 20%. The median percent error in BSS-derived shear modulus of the phantom was -6.8%, -1.55%, -17.11% for power level of 20%, 15%, and 10%, respectively. The corresponding NCC-derived errors were 29.90%, 127.1%, and 244.70%. These results suggest the relevance of using BSS for the detection of sub-micrometer, periodic motion in MMUS and SDUV imaging, particularly when SNR is less than 15 dB and/or induced displacements are less than 0.5 μm.

A Study on the Effect of Regularization Matrices in Motion Estimation

Inverse problems are very frequent in computer vision and machine learning applications. Since noteworthy hints can be obtained from motion data, it is important to seek more robust models. The advantages of using a more general regularization matrix such as Λ=diag{λ₁,…,λ _K } to robustify motion estimation instead of a single parameter λ (Λ=λ I ) are investigated and formally stated in this paper, for the optical flow problem. Intuitively, this regularization scheme makes sense, but it is not common to encounter high-quality explanations from the engineering point of view. The study is further confirmed by experimental results and compared to the nonregularized Wiener filter approach.