Receiver-Operating Characteristic Analysis of Eigen-Based Clutter Filters for Ultrasound Color Flow Imaging

The eigen-based filter has theoretically established itself as a potent solution in ultrasound color flow imaging (CFI) for combating against clutter arising from moving tissues. Yet, it remains poorly understood on how much gain in flow detection sensitivity and specificity can be delivered by this adaptive clutter filter. Here, we investigated the receiver operating characteristic (ROC) of the eigen-based clutter filter to statistically evaluate its efficacy. Our investigation was conducted using a new vascular phantom testbed that incorporated both intrinsic tissue motion (vessel pulsation: 7.58 cm/s peak velocity) and extrinsic tissue motion (vibration: 5-Hz frequency, 2.98 cm/s peak velocity), as well as pulsatile flow (pulse rate: 60 beats/min; systolic flow rate: 6.5 mL/s). The eigen-filter (single-ensemble formulation) was applied to CFI raw data sets obtained from the phantom's short-axis view (slow-time ensemble size: 12; pulse repetition frequency: 2 kHz; and ultrasound frequency: 5 MHz), and post-filter Doppler power was compared between flow and tissue regions. Results show that, in the presence of vessel pulsation and tissue vibration, the eigen-filter yielded a high true positive rate in depicting flow pixels in CFI frames (0.945 and 0.917, respectively, during peak systole and end diastole at 60° beam-flow angle), while maintaining a low false alarm rate (0.10) in rendering tissue pixels. Also, the eigen-filter posed ROC curves whose area under curve was higher than those for the polynomial regression filter (statistically significant; t-test p values were less than 0.05). These findings serve well to substantiate the merit of using eigen-filters to enhance the vascular visualization capability of CFI.

A GPU-Parallelized Eigen-Based Clutter Filter Framework for Ultrasound Color Flow Imaging

Eigen-filters with attenuation response adapted to clutter statistics in color flow imaging (CFI) have shown improved flow detection sensitivity in the presence of tissue motion. Nevertheless, its practical adoption in clinical use is not straightforward due to the high computational cost for solving eigendecompositions. Here, we provide a pedagogical description of how a real-time computing framework for eigen-based clutter filtering can be developed through a single-instruction, multiple data (SIMD) computing approach that can be implemented on a graphical processing unit (GPU). Emphasis is placed on the single-ensemble-based eigen-filtering approach (Hankel singular value decomposition), since it is algorithmically compatible with GPU-based SIMD computing. The key algebraic principles and the corresponding SIMD algorithm are explained, and annotations on how such algorithm can be rationally implemented on the GPU are presented. Real-time efficacy of our framework was experimentally investigated on a single GPU device (GTX Titan X), and the computing throughput for varying scan depths and slow-time ensemble lengths was studied. Using our eigen-processing framework, real-time video-range throughput (24 frames/s) can be attained for CFI frames with full view in azimuth direction (128 scanlines), up to a scan depth of 5 cm ( λ pixel axial spacing) for slow-time ensemble length of 16 samples. The corresponding CFI image frames, with respect to the ones derived from non-adaptive polynomial regression clutter filtering, yielded enhanced flow detection sensitivity in vivo, as demonstrated in a carotid imaging case example. These findings indicate that the GPU-enabled eigen-based clutter filtering can improve CFI flow detection performance in real time.

Clutter Rejection methods in Doppler color flow imaging: Single-ensemble vs. multi-ensemble

Clutter filter which efficiently increases the estimation accuracy of blood flow velocity attracts much attentions in Doppler color flow imaging. In this paper, we briefly introduce several state-of-the-art clutter suppression methods based on our recently works and categorize them to the singleensemble method and multi-ensemble one. Then the computing complexity of all competing methods were analyzed in-depth to provide a valuable suggestion at the real clinical situations. Furthermore, two group of clinical experiments on volunteer's carotid artery and kidney data were used to evaluate performance between single-ensemble methods and multi-ensemble ones. The results verify that the single-ensemble method shows best clutter rejection when imaging single carotid vessel, while the multi-ensemble method achieves a higher performance for kidney imaging which includes multi-stage vessels.

Direct Extraction of Tumor Response Based on Ensemble Empirical Mode Decomposition for Image Reconstruction of Early Breast Cancer Detection by UWB

A direct extraction method of tumor response based on ensemble empirical mode decomposition (EEMD) is proposed for early breast cancer detection by ultra-wide band (UWB) microwave imaging. With this approach, the image reconstruction for the tumor detection can be realized with only extracted signals from as-detected waveforms. The calibration process executed in the previous research for obtaining reference waveforms which stand for signals detected from the tumor-free model is not required. The correctness of the method is testified by successfully detecting a 4 mm tumor located inside the glandular region in one breast model and by the model located at the interface between the gland and the fat, respectively. The reliability of the method is checked by distinguishing a tumor buried in the glandular tissue whose dielectric constant is 35. The feasibility of the method is confirmed by showing the correct tumor information in both simulation results and experimental results for the realistic 3-D printed breast phantom.

Clutter Mitigation in Echocardiography Using Sparse Signal Separation

In ultrasound imaging, clutter artifacts degrade images and may cause inaccurate diagnosis. In this paper, we apply a method called Morphological Component Analysis (MCA) for sparse signal separation with the objective of reducing such clutter artifacts. The MCA approach assumes that the two signals in the additive mix have each a sparse representation under some dictionary of atoms (a matrix), and separation is achieved by finding these sparse representations. In our work, an adaptive approach is used for learning the dictionary from the echo data. MCA is compared to Singular Value Filtering (SVF), a Principal Component Analysis- (PCA-) based filtering technique, and to a high-pass Finite Impulse Response (FIR) filter. Each filter is applied to a simulated hypoechoic lesion sequence, as well as experimental cardiac ultrasound data. MCA is demonstrated in both cases to outperform the FIR filter and obtain results comparable to the SVF method in terms of contrast-to-noise ratio (CNR). Furthermore, MCA shows a lower impact on tissue sections while removing the clutter artifacts. In experimental heart data, MCA obtains in our experiments clutter mitigation with an average CNR improvement of 1.33 dB.