One of the most common algorithms used in linear-array photoacoustic imaging, is Delay-and-Sum (DAS) beamformer due to its simple implementation. The results show that this algorithm results in a low resolution and high sidelobes. In this paper, it is proposed to use the sparse-based algorithm in order to suppress the noise level efficiently and improve the image quality. The forward problem of the beamforming is defined through a Least square (LS) method, and a ℓ1-norm regularization term is added to the problem which forces the sparsity of the output to the existing minimization problem. The new robust method, named sparse beamforming (SB) method, significantly suppresses the sidelobes and reduces the noise level due to the sparse added term. Numerical results show that SB leads to signal-to-noise-ratio improvement about 98.69 dB and 82.26 dB, in average, compared to DAS and Delay-Multiply-and-Sum (DMAS), respectively. Also, the full-width-half-maximum is improved about 396 μm and 123 μm, in average, compared to DAS and DMAS algorithms, respectively, using the proposed SB method, which indicates the good performance of SB method in image enhancement.
In linear-array photoacoustic imaging, different types of algorithms and beamformers are used to construct the images. Delay-and-Sum (DAS), as a non-adaptive algorithm, is one of the most popular algorithms used due to its low complexity. However, the results obtained from this algorithm contain high sidelobes and wide mainlobe. The adaptive Minimum Variance (MV) beamformer can address these limitations and improve the images in terms of resolution and contrast. In this paper, it is proposed to suppress the sidelobes more efficiently compared to MV by eliminating the effect of the samples caused by noise and interference. This would be achieved by zeroing the samples corresponding to the lower values of the calculated weights. In the other words, in the proposed MV-based-sparse subarray (MVB-S) method, the subarrays are considered to be sparse. The results show that MVB-S method leads to signal-to-noise-ratio improvement about 39.72 dB and 18.92 dB in average, compared to DAS and MV, respectively, which indicates the good performance of MVB-S method in noise reduction and sidelobe suppression.
One of the most common algorithms used in Photoacoustic and ultrasound image reconstruction, is the nonadaptive Delay-and-Sum (DAS) beamformer. The results show that this algorithm suffers from low resolution and high level of sidelobes. In this paper, it is suggested to weight the DAS beamformed signals to address these limitations and improve the image quality. The new weighting factor, named Delay-Multiply-and-StandardDeviation (DMASD) is designed in the way that the standard deviation of the mutual coupled and multiplied delayed signals is calculated, normalized and multiplied to the DAS formula. Quantitative results obtained from the numerical study show that the proposed DMASD weighting factor improves the Signal-to-Noise-Ratio for about 48.62 dB and 46.53 dB, compared to DAS and the Delay-and-Standard-Deviation (DASD) weighting factor, respectively, at the depth of 35 mm. Also, the Full-Width-Half-Maximum is improved about 0.78 mm and 0.84 mm, compared to DAS and DASD weighting factor, respectively, at the same depth using the proposed DMASD weighting factor, which indicates the improvement of resolution.
Differentiation between benign and malignant nodules is a problem encountered by radiologists when visualizing computed tomography (CT) scans. Adenocarcinomas and granulomas have a characteristic spiculated appearance and may be fluorodeoxyglucose avid, making them difficult to distinguish for human readers. In this retrospective study, we aimed to evaluate whether a combination of radiomic texture and shape features from noncontrast CT scans can enable discrimination between granulomas and adenocarcinomas. Our study is composed of CT scans of 195 patients from two institutions, one cohort for training (N = 139) and the other (N = 56) for independent validation. A set of 645 three-dimensional texture and 24 shape features were extracted from CT scans in the training cohort. Feature selection was employed to identify the most informative features using this set. The top ranked features were also assessed in terms of their stability and reproducibility across the training and testing cohorts and between scans of different slice thickness. Three different classifiers were constructed using the top ranked features identified from the training set. These classifiers were then validated on the test set and the best classifier (support vector machine) yielded an area under the receiver operating characteristic curve of 77.8%.
Photoacoustic imaging (PAI) is a novel medical imaging modality that uses the advantages of the spatial resolution of ultrasound imaging and the high contrast of pure optical imaging. Analytical algorithms are usually employed to reconstruct the photoacoustic (PA) images as a results of their simple implementation. However, they provide a low accurate image. Model-based (MB) algorithms are used to improve the image quality and accuracy while a large number of transducers and data acquisition are needed. In this paper, we have combined the theory of compressed sensing (CS) with MB algorithms to reduce the number of transducer. Smoothed version of ℓ0-norm (Sℓ0) was proposed as the reconstruction method, and it was compared with simple iterative reconstruction (IR) and basis pursuit. The results show that Sℓ0 provides a higher image quality in comparison with other methods while a low number of transducers were. Quantitative comparison demonstrates that, at the same condition, the Sℓ0 leads to a peak-signal-to-noise ratio for about two times of the basis pursuit.
Photoacoustic imaging (PAI), is a promising medical imaging technique that provides the high contrast of the optical imaging and the resolution of ultrasound (US) imaging. Among all the methods, Three-dimensional (3D) PAI provides a high resolution and accuracy. One of the most common algorithms for 3D PA image reconstruction is delay-and-sum (DAS). However, the quality of the reconstructed image obtained from this algorithm is not satisfying, having high level of sidelobes and a wide mainlobe. In this paper, delay-multiply-andsum (DMAS) algorithm is suggested to overcome these limitations in 3D PAI. It is shown that DMAS algorithm is an appropriate reconstruction technique for 3D PAI and the reconstructed images using this algorithm are improved in the terms of the width of mainlobe and sidelobes, compared to DAS. Also, the quantitative results show that DMAS improves signal-to-noise ratio (SNR) and full-width-half-maximum (FW HM) for about 25 dB and 0.2 mm, respectively, compared to DAS.
Delay and sum (DAS) is the most common beamforming algorithm in linear-array photoacoustic imaging (PAI) as a result of its simple implementation. However, it leads to a low resolution and high sidelobes. Delay multiply and sum (DMAS) was used to address the incapabilities of DAS, providing a higher image quality. However, the resolution improvement is not well enough compared to eigenspace-based minimum variance (EIBMV). In this paper, the EIBMV beamformer has been combined with DMAS algebra, called EIBMV-DMAS, using the expansion of DMAS algorithm. The proposed method is used as the reconstruction algorithm in linear-array PAI. EIBMV-DMAS is experimentally evaluated where the quantitative and qualitative results show that it outperforms DAS, DMAS and EIBMV. The proposed method degrades the sidelobes for about 365 %, 221 % and 40 %, compared to DAS, DMAS and EIBMV, respectively. Moreover, EIBMV-DMAS improves the SNR about 158 %, 63 % and 20 %, respectively.
In photoacoustic imaging, delay-and-sum (DAS) beamformer is a common beamforming algorithm having a simple implementation. However, it results in a poor resolution and high sidelobes. To address these challenges, a new algorithm namely delay-multiply-and-sum (DMAS) was introduced having lower sidelobes compared to DAS. To improve the resolution of DMAS, a beamformer is introduced using minimum variance (MV) adaptive beamforming combined with DMAS, so-called minimum variance-based DMAS (MVB-DMAS). It is shown that expanding the DMAS equation results in multiple terms representing a DAS algebra. It is proposed to use the MV adaptive beamformer instead of the existing DAS. MVB-DMAS is evaluated numerically and experimentally. In particular, at the depth of 45 mm MVB-DMAS results in about 31, 18, and 8 dB sidelobes reduction compared to DAS, MV, and DMAS, respectively. The quantitative results of the simulations show that MVB-DMAS leads to improvement in full-width-half-maximum about 96%, 94%, and 45% and signal-to-noise ratio about 89%, 15%, and 35% compared to DAS, DMAS, MV, respectively. In particular, at the depth of 33 mm of the experimental images, MVB-DMAS results in about 20 dB sidelobes reduction in comparison with other beamformers.
Delay-and-Sum (DAS) beamformer is the most common beamforming algorithm in Photoacoustic imaging (PAI) due to its simple implementation and real time imaging. However, it provides poor resolution and high levels of sidelobe. A new algorithm named Delay-Multiply-and-Sum (DMAS) was introduced. Using DMAS leads to lower levels of sidelobe compared to DAS, but resolution is not satisfying yet. In this paper, a novel beamformer is introduced based on the combination of Minimum Variance (MV) adaptive beamforming and DMAS, so-called Minimum Variance-Based DMAS (MVB-DMAS). It is shown that expanding the DMAS equation leads to some terms which contain a DAS equation. It is proposed to use MV adaptive beamformer instead of existing DAS inside the DMAS algebra expansion. MVB-DMAS is evaluated numerically compared to DAS, DMAS and MV and Signal-to-noise ratio (SNR) metric is presented. It is shown that MVB-DMAS leads to higher image quality and SNR for about 13 dB, 3 dB and 2 dB in comparison with DAS, DMAS and MV, respectively.
In this paper we introduce Spatially Aware Expectation Maximization (SpAEM), a new parameter estimation method which incorporates information pertaining to spatial prior probability into the traditional expectation- maximization framework. For estimating the parameters of a given class, the spatial prior probability allows us to weight the contribution of any pixel based on the probability of that pixel belonging to the class of interest. In this paper we evaluate SpAEM for the problem of prostate capsule segmentation in transrectal ultrasound (TRUS) images. In cohort of 6 patients, SpAEM qualitatively and quantitatively outperforms traditional EM in distinguishing the foreground (prostate) from background (non-prostate) regions by around 45% in terms of the Sorensen Dice overlap measure, when compared against expert annotations. The variance of the estimated parameters measured via Cramer-Rao Lower Bound suggests that SpAEM yields unbiased estimates. Finally, on a synthetic TRUS image, the Cramer-Von Mises (CVM) criteria shows that SpAEM improves the estimation accuracy by around 51% and 88% for prostate and background, respectively, as compared to traditional EM.
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