Minimum variance beamforming (MVBF) is an adaptive beamforming technique, which aims to improve the lateral resolution by computing and applying signal-dependent apodization rather than predetermined apodization as typically done in conventional delay-and-sum (DAS) beamforming. Although studies have shown that the improvement in lateral resolution associated with MVBF is significant, the axial resolution remains unaffected. In this work, we combine MVBF and spiking deconvolution to improve both lateral and axial resolutions in synthetic aperture ultrasound imaging. We implement our new method and evaluate its performance using experimental datasets from a tissue-mimicking phantom. Our results show that our new method yields improved axial and lateral resolutions as well as image contrast.
{"title":"High-resolution synthetic aperture ultrasound imaging with minimum variance beamforming and spiking deconvolution","authors":"Junseob Shin, Lianjie Huang","doi":"10.1117/12.2217212","DOIUrl":"https://doi.org/10.1117/12.2217212","url":null,"abstract":"Minimum variance beamforming (MVBF) is an adaptive beamforming technique, which aims to improve the lateral resolution by computing and applying signal-dependent apodization rather than predetermined apodization as typically done in conventional delay-and-sum (DAS) beamforming. Although studies have shown that the improvement in lateral resolution associated with MVBF is significant, the axial resolution remains unaffected. In this work, we combine MVBF and spiking deconvolution to improve both lateral and axial resolutions in synthetic aperture ultrasound imaging. We implement our new method and evaluate its performance using experimental datasets from a tissue-mimicking phantom. Our results show that our new method yields improved axial and lateral resolutions as well as image contrast.","PeriodicalId":228011,"journal":{"name":"SPIE Medical Imaging","volume":"17 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":"125705839","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}
D. Xia, Zheng Zhang, P. Paysan, D. Seghers, M. Brehm, P. Munro, E. Sidky, C. Pelizzari, Xiaochuan Pan
Kilo-voltage cone-beam computed tomography (CBCT) plays an important role in image guided radiation therapy (IGRT) by providing 3D spatial information of tumor potentially useful for optimizing treatment planning. In current IGRT CBCT system, reconstructed images obtained with analytic algorithms, such as FDK algorithm and its variants, may contain artifacts. In an attempt to compensate for the artifacts, we investigate optimization-based reconstruction algorithms such as the ASD-POCS algorithm for potentially reducing arti- facts in IGRT CBCT images. In this study, using data acquired with a physical phantom and a patient subject, we demonstrate that the ASD-POCS reconstruction can significantly reduce artifacts observed in clinical re- constructions. Moreover, patient images reconstructed by use of the ASD-POCS algorithm indicate a contrast level of soft-tissue improved over that of the clinical reconstruction. We have also performed reconstructions from sparse-view data, and observe that, for current clinical imaging conditions, ASD-POCS reconstructions from data collected at one half of the current clinical projection views appear to show image quality, in terms of spatial and soft-tissue-contrast resolution, higher than that of the corresponding clinical reconstructions.
{"title":"Optimization-based reconstruction for reduction of CBCT artifact in IGRT","authors":"D. Xia, Zheng Zhang, P. Paysan, D. Seghers, M. Brehm, P. Munro, E. Sidky, C. Pelizzari, Xiaochuan Pan","doi":"10.1117/12.2217234","DOIUrl":"https://doi.org/10.1117/12.2217234","url":null,"abstract":"Kilo-voltage cone-beam computed tomography (CBCT) plays an important role in image guided radiation therapy (IGRT) by providing 3D spatial information of tumor potentially useful for optimizing treatment planning. In current IGRT CBCT system, reconstructed images obtained with analytic algorithms, such as FDK algorithm and its variants, may contain artifacts. In an attempt to compensate for the artifacts, we investigate optimization-based reconstruction algorithms such as the ASD-POCS algorithm for potentially reducing arti- facts in IGRT CBCT images. In this study, using data acquired with a physical phantom and a patient subject, we demonstrate that the ASD-POCS reconstruction can significantly reduce artifacts observed in clinical re- constructions. Moreover, patient images reconstructed by use of the ASD-POCS algorithm indicate a contrast level of soft-tissue improved over that of the clinical reconstruction. We have also performed reconstructions from sparse-view data, and observe that, for current clinical imaging conditions, ASD-POCS reconstructions from data collected at one half of the current clinical projection views appear to show image quality, in terms of spatial and soft-tissue-contrast resolution, higher than that of the corresponding clinical reconstructions.","PeriodicalId":228011,"journal":{"name":"SPIE Medical Imaging","volume":"104 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":"127650988","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}
A Doppler ultrasound clutter filter that enables estimation of low velocity blood flow could considerably improve ultrasound as a tool for clinical diagnosis and monitoring, including for the evaluation of vascular diseases and tumor perfusion. Conventional Doppler ultrasound is currently used for visualizing and estimating blood flow. However, conventional Doppler is limited by frame rate and tissue clutter caused by involuntary movement of the patient or sonographer. Spectral broadening of the clutter due to tissue motion limits ultrasound’s ability to detect blood flow less than about 5mm/s at an 8MHz center frequency. We propose a clutter filtering technique that may increase the sensitivity of Doppler measurements to at least as low as 0.41mm/s. The proposed filter uses an adaptive demodulation scheme that decreases the bandwidth of the clutter. To test the performance of the adaptive demodulation method at removing sonographer hand motion, six volunteer subjects acquired data from a basic quality assurance phantom. Additionally, to test initial in vivo feasibility, an arterial occlusion reactive hyperemia study was performed to assess the efficiency of the proposed filter at preserving signals from blood velocities 2mm/s or greater. The hand motion study resulted in initial average bandwidths of 577Hz (28.5mm/s), which were decreased to 7.28Hz (0.36mm/s) at -60 dB at 3cm using our approach. The in vivo power Doppler study resulted in 15.2dB and 0.15dB dynamic ranges between the lowest and highest blood flow time points for the proposed filter and conventional 50Hz high pass filter, respectively.
{"title":"Perfusion imaging with non-contrast ultrasound","authors":"Jaime Tierney, D. Dumont, B. Byram","doi":"10.1117/12.2216901","DOIUrl":"https://doi.org/10.1117/12.2216901","url":null,"abstract":"A Doppler ultrasound clutter filter that enables estimation of low velocity blood flow could considerably improve ultrasound as a tool for clinical diagnosis and monitoring, including for the evaluation of vascular diseases and tumor perfusion. Conventional Doppler ultrasound is currently used for visualizing and estimating blood flow. However, conventional Doppler is limited by frame rate and tissue clutter caused by involuntary movement of the patient or sonographer. Spectral broadening of the clutter due to tissue motion limits ultrasound’s ability to detect blood flow less than about 5mm/s at an 8MHz center frequency. We propose a clutter filtering technique that may increase the sensitivity of Doppler measurements to at least as low as 0.41mm/s. The proposed filter uses an adaptive demodulation scheme that decreases the bandwidth of the clutter. To test the performance of the adaptive demodulation method at removing sonographer hand motion, six volunteer subjects acquired data from a basic quality assurance phantom. Additionally, to test initial in vivo feasibility, an arterial occlusion reactive hyperemia study was performed to assess the efficiency of the proposed filter at preserving signals from blood velocities 2mm/s or greater. The hand motion study resulted in initial average bandwidths of 577Hz (28.5mm/s), which were decreased to 7.28Hz (0.36mm/s) at -60 dB at 3cm using our approach. The in vivo power Doppler study resulted in 15.2dB and 0.15dB dynamic ranges between the lowest and highest blood flow time points for the proposed filter and conventional 50Hz high pass filter, respectively.","PeriodicalId":228011,"journal":{"name":"SPIE Medical Imaging","volume":"1 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":"127963158","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}
B. Chakraborty, B. Heyde, M. Alessandrini, J. D’hooge
Image registration techniques using free-form deformation models have shown promising results for 3D myocardial strain estimation from ultrasound. However, the use of this technique has mostly been limited to research institutes due to the high computational demand, which is primarily due to the computational load of the regularization term ensuring spatially smooth cardiac strain estimates. Indeed, this term typically requires evaluating derivatives of the transformation field numerically in each voxel of the image during every iteration of the optimization process. In this paper, we replace this time-consuming step with a closed-form solution directly associated with the transformation field resulting in a speed up factor of ~10-60,000, for a typical 3D B-mode image of 2503 and 5003 voxels, depending upon the size and the parametrization of the transformation field. The performance of the numeric and the analytic solutions was contrasted by computing tracking and strain accuracy on two realistic synthetic 3D cardiac ultrasound sequences, mimicking two ischemic motion patterns. Mean and standard deviation of the displacement errors over the cardiac cycle for the numeric and analytic solutions were 0.68±0.40 mm and 0.75±0.43 mm respectively. Correlations for the radial, longitudinal and circumferential strain components at end-systole were 0.89, 0.83 and 0.95 versus 0.90, 0.88 and 0.92 for the numeric and analytic regularization respectively. The analytic solution matched the performance of the numeric solution as no statistically significant differences (p>0.05) were found when expressed in terms of bias or limits-of-agreement.
{"title":"Fast myocardial strain estimation from 3D ultrasound through elastic image registration with analytic regularization","authors":"B. Chakraborty, B. Heyde, M. Alessandrini, J. D’hooge","doi":"10.1117/12.2216781","DOIUrl":"https://doi.org/10.1117/12.2216781","url":null,"abstract":"Image registration techniques using free-form deformation models have shown promising results for 3D myocardial strain estimation from ultrasound. However, the use of this technique has mostly been limited to research institutes due to the high computational demand, which is primarily due to the computational load of the regularization term ensuring spatially smooth cardiac strain estimates. Indeed, this term typically requires evaluating derivatives of the transformation field numerically in each voxel of the image during every iteration of the optimization process. In this paper, we replace this time-consuming step with a closed-form solution directly associated with the transformation field resulting in a speed up factor of ~10-60,000, for a typical 3D B-mode image of 2503 and 5003 voxels, depending upon the size and the parametrization of the transformation field. The performance of the numeric and the analytic solutions was contrasted by computing tracking and strain accuracy on two realistic synthetic 3D cardiac ultrasound sequences, mimicking two ischemic motion patterns. Mean and standard deviation of the displacement errors over the cardiac cycle for the numeric and analytic solutions were 0.68±0.40 mm and 0.75±0.43 mm respectively. Correlations for the radial, longitudinal and circumferential strain components at end-systole were 0.89, 0.83 and 0.95 versus 0.90, 0.88 and 0.92 for the numeric and analytic regularization respectively. The analytic solution matched the performance of the numeric solution as no statistically significant differences (p>0.05) were found when expressed in terms of bias or limits-of-agreement.","PeriodicalId":228011,"journal":{"name":"SPIE Medical Imaging","volume":"37 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":"133431550","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}
It has been observed that many pathological process increase the elastic modulus of soft tissue compared to normal. In order to image tissue stiffness using ultrasound, a mechanical compression is applied to tissues of interest and local tissue deformation is measured. Based on the mechanical excitation, ultrasound stiffness imaging methods are classified as compression or strain imaging which is based on external compression and Acoustic Radiation Force Impulse (ARFI) imaging which is based on force generated by focused ultrasound. When ultrasound is focused on tissue, shear wave is generated in lateral direction and shear wave velocity is proportional to stiffness of tissues. The work presented in this paper investigates strain elastography and ARFI imaging in clinical cancer diagnostics using real time patient data. Ultrasound B-mode imaging, strain imaging, ARFI displacement and ARFI shear wave velocity imaging were conducted on 50 patients (31 Benign and 23 malignant categories) using Siemens S2000 machine. True modulus contrast values were calculated from the measured shear wave velocities. For ultrasound B-mode, ARFI displacement imaging and strain imaging, observed image contrast and Contrast to Noise Ratio were calculated for benign and malignant cancers. Observed contrast values were compared based on the true modulus contrast values calculated from shear wave velocity imaging. In addition to that, student unpaired t-test was conducted for all the four techniques and box plots are presented. Results show that, strain imaging is better for malignant cancers whereas ARFI imaging is superior than strain imaging and B-mode for benign lesions representations.
{"title":"Comparison of ultrasound B-mode, strain imaging, acoustic radiation force impulse displacement and shear wave velocity imaging using real time clinical breast images","authors":"Kavitha Manickam, R. Machireddy, B. Raghavan","doi":"10.1117/12.2217250","DOIUrl":"https://doi.org/10.1117/12.2217250","url":null,"abstract":"It has been observed that many pathological process increase the elastic modulus of soft tissue compared to normal. In order to image tissue stiffness using ultrasound, a mechanical compression is applied to tissues of interest and local tissue deformation is measured. Based on the mechanical excitation, ultrasound stiffness imaging methods are classified as compression or strain imaging which is based on external compression and Acoustic Radiation Force Impulse (ARFI) imaging which is based on force generated by focused ultrasound. When ultrasound is focused on tissue, shear wave is generated in lateral direction and shear wave velocity is proportional to stiffness of tissues. The work presented in this paper investigates strain elastography and ARFI imaging in clinical cancer diagnostics using real time patient data. Ultrasound B-mode imaging, strain imaging, ARFI displacement and ARFI shear wave velocity imaging were conducted on 50 patients (31 Benign and 23 malignant categories) using Siemens S2000 machine. True modulus contrast values were calculated from the measured shear wave velocities. For ultrasound B-mode, ARFI displacement imaging and strain imaging, observed image contrast and Contrast to Noise Ratio were calculated for benign and malignant cancers. Observed contrast values were compared based on the true modulus contrast values calculated from shear wave velocity imaging. In addition to that, student unpaired t-test was conducted for all the four techniques and box plots are presented. Results show that, strain imaging is better for malignant cancers whereas ARFI imaging is superior than strain imaging and B-mode for benign lesions representations.","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":"133704430","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}
Thor Bechsgaard, K. Hansen, A. Brandt, S. Holbek, L. Lönn, C. Strandberg, N. Bækgaard, M. B. Nielsen, J. Jensen
Chronic venous disease is a common condition leading to varicose veins, leg edema, post-thrombotic syndrome and venous ulcerations. Ultrasound (US) is the main modality for examination of venous disease. Color Doppler and occasionally spectral Doppler US (SDUS) are used for evaluation of the venous flow. Peak velocities measured by SDUS are rarely used in a clinical setting for evaluating chronic venous disease due to inadequate reproducibility mainly caused by the angle dependency of the estimate. However, estimations of blood velocities are of importance in characterizing venous disease. Transverse Oscillation US (TOUS), a non-invasive angle independent method, has been implemented on a commercial scanner. TOUS's advantage compared to SDUS is a more elaborate visualization of complex flow. The aim of this study was to evaluate, whether TOUS perform equal to SDUS for recording velocities in the veins of the lower limbs. Four volunteers were recruited for the study. A standardized flow was provoked with a cuff compression-decompression system placed around the lower leg. The average peak velocity in the popliteal vein of the four volunteers was 151.5 cm/s for SDUS and 105.9 cm/s for TOUS (p <0.001). The average of the peak velocity standard deviations (SD) were 17.0 cm/s for SDUS and 13.1 cm/s for TOUS (p <0.005). The study indicates that TOUS estimates lower peak velocity with improved SD when compared to SDUS. TOUS may be a tool for evaluation of venous disease providing quantitative measures for the evaluation of venous blood flow.
{"title":"Blood flow velocity in the popliteal vein using transverse oscillation ultrasound","authors":"Thor Bechsgaard, K. Hansen, A. Brandt, S. Holbek, L. Lönn, C. Strandberg, N. Bækgaard, M. B. Nielsen, J. Jensen","doi":"10.1117/12.2216725","DOIUrl":"https://doi.org/10.1117/12.2216725","url":null,"abstract":"Chronic venous disease is a common condition leading to varicose veins, leg edema, post-thrombotic syndrome and venous ulcerations. Ultrasound (US) is the main modality for examination of venous disease. Color Doppler and occasionally spectral Doppler US (SDUS) are used for evaluation of the venous flow. Peak velocities measured by SDUS are rarely used in a clinical setting for evaluating chronic venous disease due to inadequate reproducibility mainly caused by the angle dependency of the estimate. However, estimations of blood velocities are of importance in characterizing venous disease. Transverse Oscillation US (TOUS), a non-invasive angle independent method, has been implemented on a commercial scanner. TOUS's advantage compared to SDUS is a more elaborate visualization of complex flow. The aim of this study was to evaluate, whether TOUS perform equal to SDUS for recording velocities in the veins of the lower limbs. Four volunteers were recruited for the study. A standardized flow was provoked with a cuff compression-decompression system placed around the lower leg. The average peak velocity in the popliteal vein of the four volunteers was 151.5 cm/s for SDUS and 105.9 cm/s for TOUS (p <0.001). The average of the peak velocity standard deviations (SD) were 17.0 cm/s for SDUS and 13.1 cm/s for TOUS (p <0.005). The study indicates that TOUS estimates lower peak velocity with improved SD when compared to SDUS. TOUS may be a tool for evaluation of venous disease providing quantitative measures for the evaluation of venous blood flow.","PeriodicalId":228011,"journal":{"name":"SPIE Medical Imaging","volume":"7 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":"116908293","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 (U/S) is a fast and non-expensive imaging modality that is used for the examination of various anatomical structures, e.g. the kidneys. One important task for automatic organ tracking or computer-aided diagnosis is the identification of the organ region. During this process the exact information about the transducer location and orientation is usually unavailable. This renders the implementation of such automatic methods exceedingly challenging. In this work we like to introduce a new automatic method for the detection of the kidney in 3D U/S images. This novel technique analyses the U/S image data along virtual scan lines. Here, characteristic texture changes when entering and leaving the symmetric tissue regions of the renal cortex are searched for. A subsequent feature accumulation along a second scan direction produces a 2D heat map of renal cortex candidates, from which the kidney location is extracted in two steps. First, the strongest candidate as well as its counterpart are extracted by heat map intensity ranking and renal cortex size analysis. This process exploits the heat map gap caused by the renal pelvis region. Substituting the renal pelvis detection with this combined cortex tissue feature increases the detection robustness. In contrast to model based methods that generate characteristic pattern matches, our method is simpler and therefore faster. An evaluation performed on 61 3D U/S data sets showed, that in 55 cases showing none or minor shadowing the kidney location could be correctly identified.
{"title":"Automated kidney detection for 3D ultrasound using scan line searching","authors":"M. Noll, A. Nadolny, S. Wesarg","doi":"10.1117/12.2217127","DOIUrl":"https://doi.org/10.1117/12.2217127","url":null,"abstract":"Ultrasound (U/S) is a fast and non-expensive imaging modality that is used for the examination of various anatomical structures, e.g. the kidneys. One important task for automatic organ tracking or computer-aided diagnosis is the identification of the organ region. During this process the exact information about the transducer location and orientation is usually unavailable. This renders the implementation of such automatic methods exceedingly challenging. In this work we like to introduce a new automatic method for the detection of the kidney in 3D U/S images. This novel technique analyses the U/S image data along virtual scan lines. Here, characteristic texture changes when entering and leaving the symmetric tissue regions of the renal cortex are searched for. A subsequent feature accumulation along a second scan direction produces a 2D heat map of renal cortex candidates, from which the kidney location is extracted in two steps. First, the strongest candidate as well as its counterpart are extracted by heat map intensity ranking and renal cortex size analysis. This process exploits the heat map gap caused by the renal pelvis region. Substituting the renal pelvis detection with this combined cortex tissue feature increases the detection robustness. In contrast to model based methods that generate characteristic pattern matches, our method is simpler and therefore faster. An evaluation performed on 61 3D U/S data sets showed, that in 55 cases showing none or minor shadowing the kidney location could be correctly identified.","PeriodicalId":228011,"journal":{"name":"SPIE Medical Imaging","volume":"23 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":"123969331","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}
Hariharan Ravishankar, Pavan Annangi, M. Washburn, Justin D. Lanning
In a typical ultrasound scan, a sonographer measures Kidney morphology to assess renal abnormalities. Kidney morphology can also help to discriminate between chronic and acute kidney failure. The caliper placements and volume measurements are often time consuming and an automated solution will help to improve accuracy, repeatability and throughput. In this work, we developed an automated Kidney morphology measurement solution from long axis Ultrasound scans. Automated kidney segmentation is challenging due to wide variability in kidney shape, size, weak contrast of the kidney boundaries and presence of strong edges like diaphragm, fat layers. To address the challenges and be able to accurately localize and detect kidney regions, we present a two-step algorithm that makes use of edge and texture information in combination with anatomical cues. First, we use an edge analysis technique to localize kidney region by matching the edge map with predefined templates. To accurately estimate the kidney morphology, we use textural information in a machine learning algorithm framework using Haar features and Gradient boosting classifier. We have tested the algorithm on 45 unseen cases and the performance against ground truth is measured by computing Dice overlap, % error in major and minor axis of kidney. The algorithm shows successful performance on 80% cases.
{"title":"Automated kidney morphology measurements from ultrasound images using texture and edge analysis","authors":"Hariharan Ravishankar, Pavan Annangi, M. Washburn, Justin D. Lanning","doi":"10.1117/12.2216802","DOIUrl":"https://doi.org/10.1117/12.2216802","url":null,"abstract":"In a typical ultrasound scan, a sonographer measures Kidney morphology to assess renal abnormalities. Kidney morphology can also help to discriminate between chronic and acute kidney failure. The caliper placements and volume measurements are often time consuming and an automated solution will help to improve accuracy, repeatability and throughput. In this work, we developed an automated Kidney morphology measurement solution from long axis Ultrasound scans. Automated kidney segmentation is challenging due to wide variability in kidney shape, size, weak contrast of the kidney boundaries and presence of strong edges like diaphragm, fat layers. To address the challenges and be able to accurately localize and detect kidney regions, we present a two-step algorithm that makes use of edge and texture information in combination with anatomical cues. First, we use an edge analysis technique to localize kidney region by matching the edge map with predefined templates. To accurately estimate the kidney morphology, we use textural information in a machine learning algorithm framework using Haar features and Gradient boosting classifier. We have tested the algorithm on 45 unseen cases and the performance against ground truth is measured by computing Dice overlap, % error in major and minor axis of kidney. The algorithm shows successful performance on 80% cases.","PeriodicalId":228011,"journal":{"name":"SPIE Medical Imaging","volume":"18 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120846250","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}
Spectral x-ray imaging based on photon-counting x-ray detectors (PCXD) is an area of growing interest. By measuring the energy of x-ray photons, a spectral CT system can better differentiate elements using a single scan. However, the spatial resolution achievable with most PCXDs limits their application, particularly in preclinical CT imaging. Consequently, our group is developing a hybrid micro-CT scanner based on a high-resolution, energy-integrating (EID) detector and a lower-resolution, PCXD. To complement this system, we propose and demonstrate a hybrid, spectral CT reconstruction algorithm which robustly combines the spectral contrast of the PCXD with the spatial resolution of the EID. Specifically, the high-resolution, spectrally resolved data (X) is recovered as the sum of two matrices: one with low column rank (XL) determined from the EID data and one with intensity gradient sparse columns (XS) corresponding to the upsampled spectral contrast obtained from the PCXD data. We test the proposed algorithm in a feasibility study focused on molecular imaging of atherosclerotic plaque using activatable iodine and gold nanoparticles. The results show accurate estimation of material concentrations at increased spatial resolution for a voxel size ratio between the PCXD and the EID of 500 μm3:100 μm3. Specifically, regularized, iterative reconstruction of the MOBY mouse phantom around the K-edges of iodine (33.2 keV) and gold (80.7 keV) reduces the reconstruction error by more than a factor of three relative to least-squares, algebraic reconstruction. Likewise, the material decomposition accuracy into iodine, gold, calcium, and water improves by more than a factor of two.
{"title":"Resolution-enhancing hybrid, spectral CT reconstruction","authors":"D. Clark, C. Badea","doi":"10.1117/12.2216935","DOIUrl":"https://doi.org/10.1117/12.2216935","url":null,"abstract":"Spectral x-ray imaging based on photon-counting x-ray detectors (PCXD) is an area of growing interest. By measuring the energy of x-ray photons, a spectral CT system can better differentiate elements using a single scan. However, the spatial resolution achievable with most PCXDs limits their application, particularly in preclinical CT imaging. Consequently, our group is developing a hybrid micro-CT scanner based on a high-resolution, energy-integrating (EID) detector and a lower-resolution, PCXD. To complement this system, we propose and demonstrate a hybrid, spectral CT reconstruction algorithm which robustly combines the spectral contrast of the PCXD with the spatial resolution of the EID. Specifically, the high-resolution, spectrally resolved data (X) is recovered as the sum of two matrices: one with low column rank (XL) determined from the EID data and one with intensity gradient sparse columns (XS) corresponding to the upsampled spectral contrast obtained from the PCXD data. We test the proposed algorithm in a feasibility study focused on molecular imaging of atherosclerotic plaque using activatable iodine and gold nanoparticles. The results show accurate estimation of material concentrations at increased spatial resolution for a voxel size ratio between the PCXD and the EID of 500 μm3:100 μm3. Specifically, regularized, iterative reconstruction of the MOBY mouse phantom around the K-edges of iodine (33.2 keV) and gold (80.7 keV) reduces the reconstruction error by more than a factor of three relative to least-squares, algebraic reconstruction. Likewise, the material decomposition accuracy into iodine, gold, calcium, and water improves by more than a factor of two.","PeriodicalId":228011,"journal":{"name":"SPIE Medical Imaging","volume":"38 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":"127973998","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}
Microbubble-based contrast agents are commonly used in ultrasound imaging to help differentiate the blood pool from the endocardial wall. It is essential to use an agent which produces high image intensity relative to the surrounding tissue, commonly referred to contrast effect. When exposed to ultrasound waves, microbubbles produce an intense backscatter signal in addition to the contrast produced by the fluctuating size of the microbubbles. However, over time, the microbubble concentration depletes, leading to reduced visual enhancement. The retention time associated with contrast effect varies according to the frequency and power level of the ultrasound wave, as well as the contrast agent used. The primary objective of this study was to investigate and identify the most appropriate image acquisition parameters that render optimal contrast effect for two intravenous contrast agents, Optison™ and Definity™. Several controlled in vitro experiments were conducted using an experimental apparatus that featured a perfused tissue-emulating phantom. A continuous flow of contrast agent was imaged using ultrasound at different frequencies and power levels, while a pulse wave Doppler device was used to monitor the concentration of the contrast agent solution. The contrast effect was determined based on the image intensity inside the flow pipe mimicking the blood-pool relative to the intensity of the surrounding phantom material mimicking cardiac tissue. To identify the combination of parameters that yielded optimal visualization for each contrast agent tested, the contrast effect was assessed at different microbubble concentrations and different ultrasound imaging frequencies and transmission power levels.
{"title":"Experimental characterization, comparison and image quality assessment of two ultrasound contrast agents: Optison and Definity","authors":"Amy C. Hughes, S. Day, C. Linte, K. Schwarz","doi":"10.1117/12.2217741","DOIUrl":"https://doi.org/10.1117/12.2217741","url":null,"abstract":"Microbubble-based contrast agents are commonly used in ultrasound imaging to help differentiate the blood pool from the endocardial wall. It is essential to use an agent which produces high image intensity relative to the surrounding tissue, commonly referred to contrast effect. When exposed to ultrasound waves, microbubbles produce an intense backscatter signal in addition to the contrast produced by the fluctuating size of the microbubbles. However, over time, the microbubble concentration depletes, leading to reduced visual enhancement. The retention time associated with contrast effect varies according to the frequency and power level of the ultrasound wave, as well as the contrast agent used. The primary objective of this study was to investigate and identify the most appropriate image acquisition parameters that render optimal contrast effect for two intravenous contrast agents, Optison™ and Definity™. Several controlled in vitro experiments were conducted using an experimental apparatus that featured a perfused tissue-emulating phantom. A continuous flow of contrast agent was imaged using ultrasound at different frequencies and power levels, while a pulse wave Doppler device was used to monitor the concentration of the contrast agent solution. The contrast effect was determined based on the image intensity inside the flow pipe mimicking the blood-pool relative to the intensity of the surrounding phantom material mimicking cardiac tissue. To identify the combination of parameters that yielded optimal visualization for each contrast agent tested, the contrast effect was assessed at different microbubble concentrations and different ultrasound imaging frequencies and transmission power levels.","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":"128079855","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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