High‐speed data acquisition in a diffraction tomography system employing large‐scale toroidal arrays

André, Michael P.; Janée, Helmar S.; Martin, Peter; Otto, Gregory P.; Spivey, Brett A.; Palmer, Doug

doi:10.1002/(sici)1098-1098(1997)8:1<137::aid-ima15>3.3.co;2-1

Cited by 43 publications

(31 citation statements)

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“…The Born approximation is generally seen to giving a better description of backscattering, while the Rytov approximation yields better results for the forward propagation in particular in the presence of objects which are large relative to the wavelength of the propagating field. Born and Rytov approximations are frequently used in diffraction tomography [13][14][15] since they allow for the modeling of effects such as interference, diffraction, and refraction. Geometrical acoustics, on the other hand, describes energy propagating along rays and is a high-frequency approximation.…”

Section: Introductionmentioning

confidence: 99%

Refraction corrected transmission ultrasound computed tomography for application in breast imaging

Jackowski

Dione³

et al. 2010

Medical Physics

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Purpose:We present an iterative framework for CT reconstruction from transmission ultrasound data which accurately and efficiently models the strong refraction effects that occur in our target application: Imaging the female breast. Methods: Our refractive ray tracing framework has its foundation in the fast marching method ͑FNMM͒ and it allows an accurate as well as efficient modeling of curved rays. We also describe a novel regularization scheme that yields further significant reconstruction quality improvements. A final contribution is the development of a realistic anthropomorphic digital breast phantom based on the NIH Visible Female data set. Results: Our system is able to resolve very fine details even in the presence of significant noise, and it reconstructs both sound speed and attenuation data. Excellent correspondence with a traditional, but significantly more computationally expensive wave equation solver is achieved. Conclusions: Apart from the accurate modeling of curved rays, decisive factors have also been our regularization scheme and the high-quality interpolation filter we have used. An added benefit of our framework is that it accelerates well on GPUs where we have shown that clinical 3D reconstruction speeds on the order of minutes are possible.

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Section: Introductionmentioning

confidence: 99%

Refraction corrected transmission ultrasound computed tomography for application in breast imaging

Jackowski

Dione³

et al. 2010

Medical Physics

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“…Examples include the work of Carson et a1 (1981), Andre et al (1997), Johnson et al (1999), Marmarelis et al (2002), Liu and Waag (1997), Duric et al ( 2005Duric et al ( , 2007. Chang et al (2007) also applied limited-angle UCT to clinical breast radio-frequency (RF) signals collected by conventional B-mode ultrasound machine.…”

Section: Introductionmentioning

confidence: 99%

Ultrasonographic Evaluation of Hyoid–Larynx Approximation in Dysphagic Stroke Patients

Huang

Hsieh

Chang

et al. 2009

Ultrasound in Medicine & Biology

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We discuss a bent-ray ultrasound tomography algorithm with total-variation (TV) regularization.We have applied this algorithm to 61 in vivo breast datasets collected with our in-house clinical prototype for imaging sound-speed distributions in the breast. Our analysis showed that TV regularization could preserve sharper lesion edges than the classic Tikhonov regularization.Furthermore, the image quality of our TV bent-ray sound-speed tomograms was superior to that of the straight-ray counterparts for all types of breasts within BI-RADS density categories 1-4.For all four breast types from fatty to dense, the improvements for average sharpness (in the unit of (m· s) -1) of lesion edges in our TV bent-ray tomograms are between 2.1 to 3.4 fold compared to the straight ray tomograms. Reconstructed sound-speed tomograms illustrated that our algorithm could successfully image fatty and glandular tissues within the breast. We calculated the mean sound-speed values for fatty tissue and breast parenchyma as 1422 ± 9 ml s (mean± SD) and1487 ± 21 ml s, respectively. Based on 32 lesions in a cohort of 61 patients, we also found that the mean sound-speed for malignant breast lesions (1548±17 mls) was higher, on average, than that of benign ones (1513±27 mls) (one-sided p

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“…Obtain the differences between AIC i (i = 1,...n) and AIC min : (3) 3. Compute the Akaike weights for each data point within the time window: (4) 4. Obtain the TOF value using the weighted average: (5) where t i (i = 1; .…”

Section: Using a Weighted Average Model To Determinementioning

confidence: 99%

“…Ultrasound sound-speed tomography has great potential to detect and diagnose breast cancer ( [1][2][3][4]). A clinical prototype of ultrasound breast-imaging system with a ring array, termed the computed ultrasound risk evaluation (CURE), has been developed at the Karmanos Cancer Institute, Wayne State University in Detroit, MI for ultrasound tomography [5] (Fig.…”

Section: Introductionmentioning

confidence: 99%

An improved automatic time-of-flight picker for medical ultrasound tomography

et al. 2009

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Objective and motivation-Time-of-flight (TOF) tomography used by a clinical ultrasound tomography device can efficiently and reliably produce sound-speed images of the breast for cancer diagnosis. Accurate picking of TOFs of transmitted ultrasound signals is extremely important to ensure high-resolution and high-quality ultrasound sound-speed tomograms. Since manually picking is time-consuming for large datasets, we developed an improved automatic TOF picker based on the Akaike information criterion (AIC), as described in this paper.Methods-We make use of an approach termed multi-model inference (model averaging), based on the calculated AIC values, to improve the accuracy of TOF picks. By using multi-model inference, our picking method incorporates all the information near the TOF of ultrasound signals. Median filtering and reciprocal pair comparison are also incorporated in our AIC picker to effectively remove outliers.Results-We validate our AIC picker using synthetic ultrasound waveforms, and demonstrate that our automatic TOF picker can accurately pick TOFs in the presence of random noise with absolute amplitudes up to 80% of the maximum absolute signal amplitude. We apply the new method to 1160 in vivo breast ultrasound waveforms, and compare the picked TOFs with manual picks and amplitude threshold picks. The mean value and standard deviation between our TOF picker and manual picking are 0.4 μs and 0.29 μs, while for amplitude threshold picker the values are 1.02 μs and 0.9 μs, respectively. Tomograms for in vivo breast data with high signal-to-noise ratio (SNR) (~25 dB) and low SNR (~18 dB) clearly demonstrate that our AIC picker is much less sensitive to the SNRs of the data, compared to the amplitude threshold picker.Discussion and conclusions-The picking routine developed here is aimed at determining reliable quantitative values, necessary for adding diagnostic information to our clinical ultrasound tomography device -CURE. It has been successfully adopted into CURE, and allows us to generate such values reliably. We demonstrate that in vivo sound-speed tomograms with our TOF picks significantly improve the reconstruction accuracy and reduce image artifacts.

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High‐speed data acquisition in a diffraction tomography system employing large‐scale toroidal arrays

Abstract: This report describes the development of an experimenyet, the basic process of image formation has remained the same: This article describes an ultrasound system based on the prin-

Cited by 43 publications

References 0 publications

Refraction corrected transmission ultrasound computed tomography for application in breast imaging

Refraction corrected transmission ultrasound computed tomography for application in breast imaging

Ultrasonographic Evaluation of Hyoid–Larynx Approximation in Dysphagic Stroke Patients

An improved automatic time-of-flight picker for medical ultrasound tomography

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