Boris Perkhounkov, J. Stec, E. Sidky, Xiaochuan Pan
There has been much recent research effort directed toward spectral computed tomography (CT). An important step in realizing spectral CT is determining the spectral response of the scanning system so that the relation between material thicknesses and X-ray transmission intensity is known. We propose a few parameter spectrum model that can accurately model the X-ray transmission curves and has a form which is amenable to simultaneous spectral CT image reconstruction and CT system spectrum calibration. While the goal is to eventually realize the simultaneous image reconstruction/spectrum estimation algorithm, in this work we investigate the effectiveness of the model on spectrum estimation from simulated transmission measurements through known thicknesses of known materials. The simulated transmission measurements employ a typical X-ray spectrum used for CT and contain noise due to the randomness in detecting finite numbers of photons. The proposed model writes the X-ray spectrum as the exponential of a polynomial (EP) expansion. The model parameters are obtained by use of a standard software implementation of the Nelder-Mead simplex algorithm. The performance of the model is measured by the relative error between the predicted and simulated transmission curves. The estimated spectrum is also compared with the model X-ray spectrum. For reference, we also employ a polynomial (P) spectrum model and show performance relative to the proposed EP model.
{"title":"X-ray spectrum estimation from transmission measurements by an exponential of a polynomial model","authors":"Boris Perkhounkov, J. Stec, E. Sidky, Xiaochuan Pan","doi":"10.1117/12.2217100","DOIUrl":"https://doi.org/10.1117/12.2217100","url":null,"abstract":"There has been much recent research effort directed toward spectral computed tomography (CT). An important step in realizing spectral CT is determining the spectral response of the scanning system so that the relation between material thicknesses and X-ray transmission intensity is known. We propose a few parameter spectrum model that can accurately model the X-ray transmission curves and has a form which is amenable to simultaneous spectral CT image reconstruction and CT system spectrum calibration. While the goal is to eventually realize the simultaneous image reconstruction/spectrum estimation algorithm, in this work we investigate the effectiveness of the model on spectrum estimation from simulated transmission measurements through known thicknesses of known materials. The simulated transmission measurements employ a typical X-ray spectrum used for CT and contain noise due to the randomness in detecting finite numbers of photons. The proposed model writes the X-ray spectrum as the exponential of a polynomial (EP) expansion. The model parameters are obtained by use of a standard software implementation of the Nelder-Mead simplex algorithm. The performance of the model is measured by the relative error between the predicted and simulated transmission curves. The estimated spectrum is also compared with the model X-ray spectrum. For reference, we also employ a polynomial (P) spectrum model and show performance relative to the proposed EP model.","PeriodicalId":228011,"journal":{"name":"SPIE Medical Imaging","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132674925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
G. Y. Sandhu, Cuiping Li, O. Roy, Erik West, Katelyn Montgomery, M. Boone, N. Duric
Ultrasound waveform tomography techniques have shown promising results for the visualization and characterization of breast disease. By using frequency-domain waveform tomography techniques and a gradient descent algorithm, we have previously reconstructed the sound speed distributions of breasts of varying densities with different types of breast disease including benign and malignant lesions. By allowing the sound speed to have an imaginary component, we can model the intrinsic attenuation of a medium. We can similarly recover the imaginary component of the velocity and thus the attenuation. In this paper, we will briefly review ultrasound waveform tomography techniques, discuss attenuation and its relations to the imaginary component of the sound speed, and provide both numerical and ex vivo examples of waveform tomography attenuation reconstructions.
{"title":"Frequency-domain ultrasound waveform tomography breast attenuation imaging","authors":"G. Y. Sandhu, Cuiping Li, O. Roy, Erik West, Katelyn Montgomery, M. Boone, N. Duric","doi":"10.1117/12.2218374","DOIUrl":"https://doi.org/10.1117/12.2218374","url":null,"abstract":"Ultrasound waveform tomography techniques have shown promising results for the visualization and characterization of breast disease. By using frequency-domain waveform tomography techniques and a gradient descent algorithm, we have previously reconstructed the sound speed distributions of breasts of varying densities with different types of breast disease including benign and malignant lesions. By allowing the sound speed to have an imaginary component, we can model the intrinsic attenuation of a medium. We can similarly recover the imaginary component of the velocity and thus the attenuation. In this paper, we will briefly review ultrasound waveform tomography techniques, discuss attenuation and its relations to the imaginary component of the sound speed, and provide both numerical and ex vivo examples of waveform tomography attenuation reconstructions.","PeriodicalId":228011,"journal":{"name":"SPIE Medical Imaging","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115141618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mayumi Suzuki, T. Ikeda, C. Ishihara, Shinta Takano, H. Masuzawa
To diagnose heart valve incompetence, i.e., one of the most serious cardiac dysfunctions, it is essential to obtain images of fast-moving valves at high spatial and temporal resolution. Ultrasound synthetic transmit aperture (STA) imaging has the potential to achieve high spatial resolution by synthesizing multiple pre-beamformed images obtained with corresponding multiple transmissions. However, applying STA to fast-moving targets is difficult due to serious target deformation. We propose a high-frame-rate STA (fast STA) imaging method that uses a reduced number of transmission events needed for each image. Fast STA is expected to suppress deformation of moving targets; however, it may result in deteriorated spatial resolution. In this study, we conducted a simulation study to evaluate fast STA. We quantitatively evaluated the reduction in deformation and deterioration of spatial resolution with a model involving a radially moving valve at the maximum speed of 0.5 m/s. The simulated raw channel data of the valve phantom was processed with offline beamforming programs. We compared B-mode images obtained through single received-line in a transmission (SRT) method, STA, and fast STA. The results show that fast STA with four-times-reduced events is superior in reconstructing the original shape of the moving valve to other methods. The accuracy of valve location is 97 and 100% better than those with SRT and STA, respectively. The resolution deterioration was found to be below the annoyance threshold considering the improved performance of the shape reconstruction. The obtained results are promising for providing more precise diagnostic information on cardiovascular diseases.
{"title":"Precise reconstruction of fast moving cardiac valve in high frame rate synthetic transmit aperture ultrasound imaging","authors":"Mayumi Suzuki, T. Ikeda, C. Ishihara, Shinta Takano, H. Masuzawa","doi":"10.1117/12.2216580","DOIUrl":"https://doi.org/10.1117/12.2216580","url":null,"abstract":"To diagnose heart valve incompetence, i.e., one of the most serious cardiac dysfunctions, it is essential to obtain images of fast-moving valves at high spatial and temporal resolution. Ultrasound synthetic transmit aperture (STA) imaging has the potential to achieve high spatial resolution by synthesizing multiple pre-beamformed images obtained with corresponding multiple transmissions. However, applying STA to fast-moving targets is difficult due to serious target deformation. We propose a high-frame-rate STA (fast STA) imaging method that uses a reduced number of transmission events needed for each image. Fast STA is expected to suppress deformation of moving targets; however, it may result in deteriorated spatial resolution. In this study, we conducted a simulation study to evaluate fast STA. We quantitatively evaluated the reduction in deformation and deterioration of spatial resolution with a model involving a radially moving valve at the maximum speed of 0.5 m/s. The simulated raw channel data of the valve phantom was processed with offline beamforming programs. We compared B-mode images obtained through single received-line in a transmission (SRT) method, STA, and fast STA. The results show that fast STA with four-times-reduced events is superior in reconstructing the original shape of the moving valve to other methods. The accuracy of valve location is 97 and 100% better than those with SRT and STA, respectively. The resolution deterioration was found to be below the annoyance threshold considering the improved performance of the shape reconstruction. The obtained results are promising for providing more precise diagnostic information on cardiovascular diseases.","PeriodicalId":228011,"journal":{"name":"SPIE Medical Imaging","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116087139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ultrasound image quality enhancement is a problem of considerable interest in medical imaging modality and an ongoing challenge to date. This paper investigates a method based on frequency-shift low-pass filtering (FSLF) and least mean square adaptive filtering (LMSAF) for ultrasound image quality enhancement. FSLF is used for processing the ultrasound signal in the frequency domain, while LMSAPF in the time domain. Firstly, FSLF shifts the center frequency of the focused signal to zero. Then the real and imaginary part of the complex data are filtered respectively by finite impulse response (FIR) low-pass filter. Thus the information around the center frequency are retained while the undesired ones, especially background noises are filtered. Secondly, LMSAF multiplies the signals with an automatically adjusted weight vector to further eliminate the noises and artifacts. Through the combination of the two filters, the ultrasound image is expected to have less noises and artifacts and higher resolution, and contrast. The proposed method was verified with the RF data of the CIRS phantom 055A captured by SonixTouch DAQ system. Experimental results show that the background noises and artifacts can be efficiently restrained, the wire object has a higher resolution and the contrast ratio (CR) can be enhanced for about 12dB to 15dB at different image depth comparing to delay-and-sum (DAS).
{"title":"Frequency-shift low-pass filtering and least mean square adaptive filtering for ultrasound imaging","authors":"Shanshan Wang, Chunyu Li, Mingyue Ding, M. Yuchi","doi":"10.1117/12.2216066","DOIUrl":"https://doi.org/10.1117/12.2216066","url":null,"abstract":"Ultrasound image quality enhancement is a problem of considerable interest in medical imaging modality and an ongoing challenge to date. This paper investigates a method based on frequency-shift low-pass filtering (FSLF) and least mean square adaptive filtering (LMSAF) for ultrasound image quality enhancement. FSLF is used for processing the ultrasound signal in the frequency domain, while LMSAPF in the time domain. Firstly, FSLF shifts the center frequency of the focused signal to zero. Then the real and imaginary part of the complex data are filtered respectively by finite impulse response (FIR) low-pass filter. Thus the information around the center frequency are retained while the undesired ones, especially background noises are filtered. Secondly, LMSAF multiplies the signals with an automatically adjusted weight vector to further eliminate the noises and artifacts. Through the combination of the two filters, the ultrasound image is expected to have less noises and artifacts and higher resolution, and contrast. The proposed method was verified with the RF data of the CIRS phantom 055A captured by SonixTouch DAQ system. Experimental results show that the background noises and artifacts can be efficiently restrained, the wire object has a higher resolution and the contrast ratio (CR) can be enhanced for about 12dB to 15dB at different image depth comparing to delay-and-sum (DAS).","PeriodicalId":228011,"journal":{"name":"SPIE Medical Imaging","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130416809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent clinical practice of ultrasound imaging, the importance of high-frame-rate imaging is growing. Simultaneous multiple transmission is one way to increase frame rate while maintaining a spatial resolution and signal-to-noise ratio. However, this technique has an inherent issue in that "cross-talk artifacts" appear between the multiple transmitted pulses. In this study, a novel method providing higher-frame-rate ultrasound imaging with reduced cross-talk by combining a synthetic aperture and spatial coded excitation is proposed. In the proposed method, two coded transmission beams are simultaneously excited during beam steering in the lateral direction. Parallel receive beamforming is then performed in the region around individual transmission beams. Decoding is carried out by using two beamformed signals from a region where laterally neighboring transmission beams overlap. All decoded beamformed signals are then synthesized coherently. The proposed method was evaluated using a simulated phantom image under the assumption of imaging with a general sector probe. Results showed that the method achieved twice the frame rate while maintaining image resolution (105%) and reducing cross-talk artifacts from −37 dB to less than −57 dB.
{"title":"Higher-frame-rate ultrasound imaging with reduced cross-talk by combining a synthetic aperture and spatial coded excitation","authors":"C. Ishihara, T. Ikeda, H. Masuzawa","doi":"10.1117/12.2214534","DOIUrl":"https://doi.org/10.1117/12.2214534","url":null,"abstract":"In recent clinical practice of ultrasound imaging, the importance of high-frame-rate imaging is growing. Simultaneous multiple transmission is one way to increase frame rate while maintaining a spatial resolution and signal-to-noise ratio. However, this technique has an inherent issue in that \"cross-talk artifacts\" appear between the multiple transmitted pulses. In this study, a novel method providing higher-frame-rate ultrasound imaging with reduced cross-talk by combining a synthetic aperture and spatial coded excitation is proposed. In the proposed method, two coded transmission beams are simultaneously excited during beam steering in the lateral direction. Parallel receive beamforming is then performed in the region around individual transmission beams. Decoding is carried out by using two beamformed signals from a region where laterally neighboring transmission beams overlap. All decoded beamformed signals are then synthesized coherently. The proposed method was evaluated using a simulated phantom image under the assumption of imaging with a general sector probe. Results showed that the method achieved twice the frame rate while maintaining image resolution (105%) and reducing cross-talk artifacts from −37 dB to less than −57 dB.","PeriodicalId":228011,"journal":{"name":"SPIE Medical Imaging","volume":"9790 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129084707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Frequency-space prediction filtering (FXPF), also known as FX deconvolution, is a technique originally developed for random noise attenuation in seismic imaging. FXPF attempts to reduce random noise in seismic data by modeling only real signals that appear as linear or quasilinear events in the aperture domain. In medical ultrasound imaging, channel radio frequency (RF) signals from the main lobe appear as horizontal events after receive delays are applied while acoustic clutter signals from off-axis scatterers and electronic noise do not. Therefore, FXPF is suitable for preserving only the main-lobe signals and attenuating the unwanted contributions from clutter and random noise in medical ultrasound imaging. We adapt FXPF to ultrasound imaging, and evaluate its performance using simulated data sets from a point target and an anechoic cyst. Our simulation results show that using only 5 iterations of FXPF achieves contrast-to-noise ratio (CNR) improvements of 67 % in a simulated noise-free anechoic cyst and 228 % in a simulated anechoic cyst contaminated with random noise of 15 dB signal-to-noise ratio (SNR). Our findings suggest that ultrasound imaging with FXPF attenuates contributions from both acoustic clutter and random noise and therefore, FXPF has great potential to improve ultrasound image contrast for better visualization of important anatomical structures and detection of diseased conditions.
{"title":"Frequency-space prediction filtering for acoustic clutter and random noise attenuation in ultrasound imaging","authors":"Junseob Shin, Lianjie Huang","doi":"10.1117/12.2216566","DOIUrl":"https://doi.org/10.1117/12.2216566","url":null,"abstract":"Frequency-space prediction filtering (FXPF), also known as FX deconvolution, is a technique originally developed for random noise attenuation in seismic imaging. FXPF attempts to reduce random noise in seismic data by modeling only real signals that appear as linear or quasilinear events in the aperture domain. In medical ultrasound imaging, channel radio frequency (RF) signals from the main lobe appear as horizontal events after receive delays are applied while acoustic clutter signals from off-axis scatterers and electronic noise do not. Therefore, FXPF is suitable for preserving only the main-lobe signals and attenuating the unwanted contributions from clutter and random noise in medical ultrasound imaging. We adapt FXPF to ultrasound imaging, and evaluate its performance using simulated data sets from a point target and an anechoic cyst. Our simulation results show that using only 5 iterations of FXPF achieves contrast-to-noise ratio (CNR) improvements of 67 % in a simulated noise-free anechoic cyst and 228 % in a simulated anechoic cyst contaminated with random noise of 15 dB signal-to-noise ratio (SNR). Our findings suggest that ultrasound imaging with FXPF attenuates contributions from both acoustic clutter and random noise and therefore, FXPF has great potential to improve ultrasound image contrast for better visualization of important anatomical structures and detection of diseased conditions.","PeriodicalId":228011,"journal":{"name":"SPIE Medical Imaging","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115908770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nuno Almeida, S. Sarvari, F. Orderud, O. Gérard, J. D’hooge, E. Samset
In this paper, we present an automatic solution for segmentation and quantification of the left atrium (LA) from 3D cardiac ultrasound. A model-based framework is applied, making use of (deformable) active surfaces to model the endocardial surfaces of cardiac chambers, allowing incorporation of a priori anatomical information in a simple fashion. A dual-chamber model (LA and left ventricle) is used to detect and track the atrio-ventricular (AV) plane, without any user input. Both chambers are represented by parametric surfaces and a Kalman filter is used to fit the model to the position of the endocardial walls detected in the image, providing accurate detection and tracking during the whole cardiac cycle. This framework was tested in 20 transthoracic cardiac ultrasound volumetric recordings of healthy volunteers, and evaluated using manual traces of a clinical expert as a reference. The 3D meshes obtained with the automatic method were close to the reference contours at all cardiac phases (mean distance of 0.03±0.6 mm). The AV plane was detected with an accuracy of −0.6±1.0 mm. The LA volumes assessed automatically were also in agreement with the reference (mean ±1.96 SD): 0.4±5.3 ml, 2.1±12.6 ml, and 1.5±7.8 ml at end-diastolic, end-systolic and pre-atrial-contraction frames, respectively. This study shows that the proposed method can be used for automatic volumetric assessment of the LA, considerably reducing the analysis time and effort when compared to manual analysis.
{"title":"Automatic left-atrial segmentation from cardiac 3D ultrasound: a dual-chamber model-based approach","authors":"Nuno Almeida, S. Sarvari, F. Orderud, O. Gérard, J. D’hooge, E. Samset","doi":"10.1117/12.2216666","DOIUrl":"https://doi.org/10.1117/12.2216666","url":null,"abstract":"In this paper, we present an automatic solution for segmentation and quantification of the left atrium (LA) from 3D cardiac ultrasound. A model-based framework is applied, making use of (deformable) active surfaces to model the endocardial surfaces of cardiac chambers, allowing incorporation of a priori anatomical information in a simple fashion. A dual-chamber model (LA and left ventricle) is used to detect and track the atrio-ventricular (AV) plane, without any user input. Both chambers are represented by parametric surfaces and a Kalman filter is used to fit the model to the position of the endocardial walls detected in the image, providing accurate detection and tracking during the whole cardiac cycle. This framework was tested in 20 transthoracic cardiac ultrasound volumetric recordings of healthy volunteers, and evaluated using manual traces of a clinical expert as a reference. The 3D meshes obtained with the automatic method were close to the reference contours at all cardiac phases (mean distance of 0.03±0.6 mm). The AV plane was detected with an accuracy of −0.6±1.0 mm. The LA volumes assessed automatically were also in agreement with the reference (mean ±1.96 SD): 0.4±5.3 ml, 2.1±12.6 ml, and 1.5±7.8 ml at end-diastolic, end-systolic and pre-atrial-contraction frames, respectively. This study shows that the proposed method can be used for automatic volumetric assessment of the LA, considerably reducing the analysis time and effort when compared to manual analysis.","PeriodicalId":228011,"journal":{"name":"SPIE Medical Imaging","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121893179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
C. A. Villagómez-Hoyos, M. Stuart, Thor Bechsgaard, M. Nielsen, J. Jensen
This work presents the first in vivo results of 2-D high frame rate vector velocity imaging for transthoracic cardiac imaging. Measurements are made on a healthy volunteer using the SARUS experimental ultrasound scanner connected to an intercostal phased-array probe. Two parasternal long-axis view (PLAX) are obtained, one centred at the aortic valve and another centred at the left ventricle. The acquisition sequence was composed of 3 diverging waves for high frame rate synthetic aperture flow imaging. For verification a phantom measurement is performed on a transverse straight 5 mm diameter vessel at a depth of 100 mm in a tissue-mimicking phantom. A flow pump produced a 2 ml/s constant flow with a peak velocity of 0.2 m/s. The average estimated flow angle in the ROI was 86.22° ± 6.66° with a true flow angle of 90°. A relative velocity bias of −39% with a standard deviation of 13% was found. In-vivo acquisitions show complex flow patterns in the heart. In the aortic valve view, blood is seen exiting the left ventricle cavity through the aortic valve into the aorta during the systolic phase of the cardiac cycle. In the left ventricle view, blood flow is seen entering the left ventricle cavity through the mitral valve and splitting in two ways when approximating the left ventricle wall. The work presents 2-D velocity estimates on the heart from a non-invasive transthoracic scan. The ability of the method detecting flow regardless of the beam angle could potentially reveal a more complete view of the flow patterns presented on the heart.
{"title":"High frame rate synthetic aperture vector flow imaging for transthoracic echocardiography","authors":"C. A. Villagómez-Hoyos, M. Stuart, Thor Bechsgaard, M. Nielsen, J. Jensen","doi":"10.1117/12.2216707","DOIUrl":"https://doi.org/10.1117/12.2216707","url":null,"abstract":"This work presents the first in vivo results of 2-D high frame rate vector velocity imaging for transthoracic cardiac imaging. Measurements are made on a healthy volunteer using the SARUS experimental ultrasound scanner connected to an intercostal phased-array probe. Two parasternal long-axis view (PLAX) are obtained, one centred at the aortic valve and another centred at the left ventricle. The acquisition sequence was composed of 3 diverging waves for high frame rate synthetic aperture flow imaging. For verification a phantom measurement is performed on a transverse straight 5 mm diameter vessel at a depth of 100 mm in a tissue-mimicking phantom. A flow pump produced a 2 ml/s constant flow with a peak velocity of 0.2 m/s. The average estimated flow angle in the ROI was 86.22° ± 6.66° with a true flow angle of 90°. A relative velocity bias of −39% with a standard deviation of 13% was found. In-vivo acquisitions show complex flow patterns in the heart. In the aortic valve view, blood is seen exiting the left ventricle cavity through the aortic valve into the aorta during the systolic phase of the cardiac cycle. In the left ventricle view, blood flow is seen entering the left ventricle cavity through the mitral valve and splitting in two ways when approximating the left ventricle wall. The work presents 2-D velocity estimates on the heart from a non-invasive transthoracic scan. The ability of the method detecting flow regardless of the beam angle could potentially reveal a more complete view of the flow patterns presented on the heart.","PeriodicalId":228011,"journal":{"name":"SPIE Medical Imaging","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122893411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
There is growing evidence that reverberation is a primary mechanism of clinical image degradation. This has led to a number of new approaches to suppress reverberation, including our recently proposed model-based algorithm. The algorithm can work well, but it must be trained to reject clutter, while preserving the signal of interest. One way to do this is to use simulated data, but current simulation methods that include multipath scattering are slow and do not readily allow separation of clutter and signal. Here, we propose a more convenient pseudo non-linear simulation method that utilizes existing linear simulation tools like Field II. The approach functions by linearly simulating scattered wavefronts at shallow depths, and then time-shifting these wavefronts to deeper depths. The simulation only requires specification of the first and last scatterers encountered by a multiply reflected wave and a third point that establishes the arrival time of the reverberation. To maintain appropriate 2D correlation, this set of three points is fixed for the entire simulation and is shifted as with a normal linear simulation scattering field. We show example images, and we compute first order speckle statistics as a function of scatterer density. We perform ex vivo measures of reverberation where we find that the average speckle SNR is 1.73, which we can simulate with 2 reverberation scatterers per resolution cell. We also compare ex vivo lateral speckle statistics to those from linear and pseudo non-linear simulation data. Finally, the van Cittert-Zernike curve was shown to match empirical and theoretical observations.
{"title":"A pseudo non-linear method for fast simulations of ultrasonic reverberation","authors":"B. Byram, J. Shu","doi":"10.1117/12.2216839","DOIUrl":"https://doi.org/10.1117/12.2216839","url":null,"abstract":"There is growing evidence that reverberation is a primary mechanism of clinical image degradation. This has led to a number of new approaches to suppress reverberation, including our recently proposed model-based algorithm. The algorithm can work well, but it must be trained to reject clutter, while preserving the signal of interest. One way to do this is to use simulated data, but current simulation methods that include multipath scattering are slow and do not readily allow separation of clutter and signal. Here, we propose a more convenient pseudo non-linear simulation method that utilizes existing linear simulation tools like Field II. The approach functions by linearly simulating scattered wavefronts at shallow depths, and then time-shifting these wavefronts to deeper depths. The simulation only requires specification of the first and last scatterers encountered by a multiply reflected wave and a third point that establishes the arrival time of the reverberation. To maintain appropriate 2D correlation, this set of three points is fixed for the entire simulation and is shifted as with a normal linear simulation scattering field. We show example images, and we compute first order speckle statistics as a function of scatterer density. We perform ex vivo measures of reverberation where we find that the average speckle SNR is 1.73, which we can simulate with 2 reverberation scatterers per resolution cell. We also compare ex vivo lateral speckle statistics to those from linear and pseudo non-linear simulation data. Finally, the van Cittert-Zernike curve was shown to match empirical and theoretical observations.","PeriodicalId":228011,"journal":{"name":"SPIE Medical Imaging","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123263707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Computed tomography (CT) is an ill-posed problem. Reconstruction on unstructured grid reduces the computational cost and alleviates the ill-posedness by decreasing the dimension of the solution space. However, there was no systematic study on edge-preserving regularization methods for CT reconstruction on unstructured grid. In this work, we propose a novel regularization method for CT reconstruction on unstructured grid, such as triangular or tetrahedral meshes generated from the initial images reconstructed via analysis reconstruction method (e.g., filtered back-projection). The proposed regularization method is modeled as a three-term optimization problem, containing a weighted least square fidelity term motivated by the simultaneous algebraic reconstruction technique (SART). The related cost function contains two non-differentiable terms, which bring difficulty to the development of the fast solver. A fixed-point proximity algorithm with SART is developed for solving the related optimization problem, and accelerating the convergence. Finally, we compare the regularized CT reconstruction method to SART with different regularization methods. Numerical experiments demonstrated that the proposed regularization method on unstructured grid is effective to suppress noise and preserve edge features.
{"title":"Regularized CT reconstruction on unstructured grid","authors":"Yun Chen, Yao Lu, Xiangyuan Ma, Yuesheng Xu","doi":"10.1117/12.2217381","DOIUrl":"https://doi.org/10.1117/12.2217381","url":null,"abstract":"Computed tomography (CT) is an ill-posed problem. Reconstruction on unstructured grid reduces the computational cost and alleviates the ill-posedness by decreasing the dimension of the solution space. However, there was no systematic study on edge-preserving regularization methods for CT reconstruction on unstructured grid. In this work, we propose a novel regularization method for CT reconstruction on unstructured grid, such as triangular or tetrahedral meshes generated from the initial images reconstructed via analysis reconstruction method (e.g., filtered back-projection). The proposed regularization method is modeled as a three-term optimization problem, containing a weighted least square fidelity term motivated by the simultaneous algebraic reconstruction technique (SART). The related cost function contains two non-differentiable terms, which bring difficulty to the development of the fast solver. A fixed-point proximity algorithm with SART is developed for solving the related optimization problem, and accelerating the convergence. Finally, we compare the regularized CT reconstruction method to SART with different regularization methods. Numerical experiments demonstrated that the proposed regularization method on unstructured grid is effective to suppress noise and preserve edge features.","PeriodicalId":228011,"journal":{"name":"SPIE Medical Imaging","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125375782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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