Comparing elastographic strain images with modulus images obtained using nanoindentation: preliminary results using phantoms and tissue samples

Conventional elastography involves quasistatic mechanical compression (external or internal) of the tissue under ultrasonic insonification to obtain radiofrequency (RF) A-lines before and after compression. Cross-correlation of the pre- and postcompression A-lines results in displacement images with axial gradients that produce the strain images (strain elastograms). Though the strain elastograms show structural similarities to the modulus images, they are not related in a simple way to the modulus images because the strains depend on both modulus and geometry of the materials being deformed. Therefore, a quantification of the similarities between the strain and modulus images may enhance the interpretation confidence of strain elastograms in depicting tissue structure. To demonstrate similarities between modulus images and strain elastograms, a feasibility study of using nanoindentation to obtain modulus images of thin slices of tissue and tissue-mimicking phantoms (agar-gelatin mixtures) was performed first, with encouraging results. This was followed by a comparison of modulus images and strain elastograms obtained from the same sample slices. The experimental results indicated that, under certain experimental conditions, it is feasible to perform quantitative comparisons between strain images (using elastography) and modulus images. A good visual, as well as quantitative, correspondence between structures in the modulus and strain images could be obtained at a 3-mm scale.     

Noise performance and signal-to-noise ratio of shear strain elastograms

In this paper, we develop a theoretical expression for the signal-to-noise ratio (SNR) of shear strain elastograms. The previously-developed ideas for the axial strain filter (ASF) and lateral strain filter (LSF) are extended to define the concept of the shear strain filter (SSF). Some of our theoretical results are verified using simulations and phantom experiments. The results indicate that the signal-to-noise ratio of shear-strain elastograms (SNRsse) improves with increasing shear strain and with improvements in system parameters such as the sonographic signal-to-noise ratio (SNRs) beamwidth, center frequency and fractional bandwidth. The results also indicate that the amount of axial strain present along with the shear strain is an important parameter that determines the upper bound on SNRsse. The SNRsse will be higher in the absence of additional deformation due to axial strain.     

Noise reduction in elastograms using temporal stretching with multicompression averaging

Elastography uses estimates of the time delay (obtained by cross-correlation) to compute strain estimates in tissue due to quasistatic compression. Because the time delay estimates do not generally occur at the sampling intervals, the location of the cross-correlation peak does not give and accurate estimate of the time delay. Sampling errors in the time-delay estimate are reduced using signal interpolation techniques to obtain subsample time-delay estimates. Distortions of the echo signals due to tissue compression introduce correlation artifacts in the elastogram. These artifacts are reduced by a combination of small compressions and temporal stretching of the postcompression signal. Random noise effects in the resulting elastograms are reduced by averaging several elastograms, obtained from successive small compressions (assuming that the errors are uncorrelated). Multicompression averaging with temporal stretching is shown to increase the signal-to-noise ratio in the elastogram by an order of magnitude, without sacrificing sensitivity, resolution or dynamic range. The strain filter concept is extended in this article to theoretically characterize the performance of multicompression averaging with temporal stretching.     

Cardiac motion and strain detection using 4D CT images: comparison with tagged MRI,and echocardiography

Ischemic heart disease is a leading cause of death in the modern world. Coronary obstruction is the basis for ischemic heart disease and leads to decreased cardiac supply and decreased contractility of the myocardium. Recently, high quality 4D computed tomography (CT) has become available for cardiac imaging and provides the clinician with high quality anatomical images. In this article, a new method is proposed to detect 3D motion and strain from 4D cardiac CT images by constraining intensity constancy, myocardial volume changes and motion smoothness assumptions. The proposed method is validated by using manual tracking of the cardiac CT landmarks. The average error for the manual tracking, by an expert, was 2.9 ± 0.9 mm. As an additional validation, the cardiac CT strain values were compared to the cardiac tagged magnetic resonance imaging (MRI) and 2D B-mode echocardiography strain values of the same patients. The correlation ratio was significantly high for CT and tagged MRI radial strain values (r = 0.76, 95 % confidence interval, P < 0.001). The correlation ratio was meaningful for CT and echocardiography radial strain values as well (r = 0.67, 95 % confidence interval, P < 0.001). The correlation ratio for CT and tagged MRI circumferential strain values was acceptable (r = 0.73, 95 % confidence interval, P < 0.001), while the correlation ratio for CT and echocardiography circumferential strain values was good as well (r = 0.61, 95 % confidence interval, P < 0.001). In general, motion and strain values computed from cardiac CT images agree with motion and strain values computed from tagged MRI and echocardiography images.     

Effect of lesion boundary conditions on axial strain elastograms: a parametric study

Ultrasound elastography produces strain images of compliant tissues under quasi-static compression. When a material is compressed, there are several parameters that affect the stress-distribution and, hence, the strain distribution in the material. The state of bonding of an inclusion to the background material is a critical parameter. Heretofore, in the field of elastography, the inclusion was considered to be firmly bonded to the background material and analytical solutions were derived for the elasticity problem involving simple geometries like circular inclusion (for two dimensional [2D]) and spherical inclusion (three dimensional [3D]). Under these conditions, simple analytical expressions relating the strain contrast to the modulus contrast were derived. However, it is known that the state of bonding of some tumors to their surrounding tissues depends on the type of the lesion. For example, benign lesions of the breast are known to be loosely bonded to the surrounding tissue, while malignant breast lesions are firmly bonded. In this study, we perform a parametric study using finite element modeling (FEM) to investigate the validity of the analytical expression relating the strain contrast to the modulus contrast, when the state of bonding at the inclusion/background interface spans a large dynamic range. The results suggest that estimated modulus contrast using the analytical expression is sensitive to the region-of-interest within the inclusion that is considered in the computation of the strain contrast. By considering the inclusion region lying along the axis of lateral symmetry instead of whole region of the inclusion, the estimated modulus contrast (obtained using the analytical expression present in the literature) can be computed to within a systematic error of 10% of the actual modulus contrast. Additional estimation errors are expected to accrue in experimental and in vivo conditions.     

Quantitative analysis of the hippocampus using images obtained from 7.0 T MRI

In-vivo volumetric measurements of hippocampus have proven to be highly informative for studying various neurological diseases such as Alzheimer's disease. The usefulness of volumetric imaging, however, has been limited due to the poor image resolutions obtained by currently available MRI images. In this study, a new result of volumetric image measurement of the hippocampus using 7.0 T MRI images of high contrast and resolution is described. To verify the usefulness of the proposed method, its reliability and sensitivity were examined and compared with existing imaging techniques such as 1.5 T or 3.0 T MRI imaging. The results of our study with 7.0 T MRI clearly demonstrated superior boundary detection for the hippocampal head, body, and tail compared with low field MRIs. In conclusion, robust and reproducible volumetric measurements as well as 3D images of clear contrast obtained with 7.0 T suggest the usefulness of high field MR imaging and its eventual use for the accurate diagnosis of hippocampal diseases and related research.     

A comparison of object-based and contextual pixel-based classifications using high and medium spatial resolution images

Object-based classification has demonstrated numerous advantages over non-contextual pixel-based classification due to its capability of modelling spatial information through image segmentation. Similarly, contextual pixel-based classification can also incorporate spatial information among neighbouring pixels to improve the performance of non-contextual pixel-based classification. However, to our knowledge, no study has compared object-based approaches with contextual pixel-based approaches for image classification. In this letter, we compared an object-based approach using a segmentation algorithm embedded in eCognition with a contextual pixel-based approach using Markov random fields. The performances were evaluated with a high spatial resolution image (3 m) and a medium spatial resolution image (30 m) using various thematic and geometric accuracy indices. The results showed that the classification accuracy of the contextual pixel-based approach is higher than the object-based approach on both images, and the values of geometric indices for the two approaches are comparable.     

2-D ultrasound strain images for breast cancer diagnosis using nonrigid subregion registration

Tissue elasticity of a lesion is a useful criterion for the diagnosis of breast ultrasound (US). Elastograms are created by comparing ultrasonic radio-frequency waveforms before and after a light-tissue compression. In this study, we evaluate the accuracy of continuous US strain image in the classification of benign from malignant breast tumors. A series of B-mode US images is applied and each case involves 60 continuous images obtained by using the steady artificial pressure of the US probe. In general, after compression by the US probe, a soft benign tumor will become flatter than a stiffened malignant tumor. We proposed a computer-aided diagnostic (CAD) system by utilizing the nonrigid image registration modality on the analysis of tumor deformation. Furthermore, we used some image preprocessing methods, which included the level set segmentation, to improve the performance. One-hundred pathology-proven cases, including 60 benign breast tumors and 40 malignant tumors, were used in the experiments to test the classification accuracy of the proposed method. Four characteristic values--normalized slope of metric value (NSM), normalized area difference (NAD), normalized standard deviation (NSD) and normalized center translation (NCT)--were computed for all cases. By using the support vector machine, the accuracy, sensitivity, specificity and positive and negative predictive values of the classification of continuous US strain images were satisfactory. The A(z) value of the support vector machine based on the four characteristic values used for the classification of solid breast tumors was 0.9358.     

Automatic and quantitative measurement of alveolar bone level in OCT images using deep learning

We propose a method to automatically segment the periodontal structures of the tooth enamel and the alveolar bone using convolutional neural network (CNN) and to measure quantitatively and automatically the alveolar bone level (ABL) by detecting the cemento-enamel junction and the alveolar bone crest in optical coherence tomography (OCT) images. The tooth enamel and the alveolar bone regions were automatically segmented using U-Net, Dense-UNet, and U²-Net, and the ABL was quantitatively measured as the distance between the cemento-enamel junction and the alveolar bone crest using image processing. The mean distance difference (MDD) measured by our suggested method ranged from 0.19 to 0.22 mm for the alveolar bone crest (ABC) and from 0.18 to 0.32 mm for the cemento-enamel junction (CEJ). All CNN models showed the mean absolute error (MAE) of less than 0.25 mm in the x and y coordinates and greater than 90% successful detection rate (SDR) at 0.5 mm for both the ABC and the CEJ. The CNN models showed high segmentation accuracies in the tooth enamel and the alveolar bone regions, and the ABL measurements at the incisors by detected results from CNN predictions demonstrated high correlation and reliability with the ground truth in OCT images.     

Validation of quantitative analysis of intravascular ultrasound images

This study investigated the accuracy and reproducibility of a computer-aided method for quantification of intravascular ultrasound. The computer analysis system was developed on an IBM compatible PC/AT equipped with a framegrabber. The quantitative assessment of lumen area, lesion area and percent area obstruction was performed by tracing the boundaries of the free lumen and original lumen. Accuracy of the analysis system was tested in a phantom study. Echographic measurements of lumen and lesion area derived from 16 arterial specimens were compared with data obtained by histology. The differences in lesion area measurements between histology and ultrasound were minimal (mean ± SD: ?0.27±1.79 mm², p>0.05). Lumen area measurements from histology were significantly smaller than those with ultrasound due to mechanical deformation of histologic specimens (?5.38±5.09 mm², p<0.05). For comparison with angiography, 18 ultrasound cross-sections were obtainedin vivo from 8 healthy peripheral arteries. Luminal areas obtained by angiography were similar to those by ultrasound (?0.52±5.15 mm², p>0.05). Finally, intra- and interobserver variability of our quantitative method was evaluated in measurements of 100in vivo ultrasound images. The results showed that variations in lumen area measurements were low (5%) whereas variations in lesion area and percent area obstruction were relatively high (13%, 10%, respectively). Results of this study indicate that our quantitative method provides accurate and reproducible measurements of lumen and lesion area. Thus, intravascular ultrasound can be used for clinical investigation, including assessment of vascular stenosis and evaluation of therapeutic intervention.     

Three-dimensional automatic quantitative analysis of intravascular ultrasound images

Intravascular ultrasound (IVUS) has established itself as a useful tool for coronary assessment. The vast amount of data obtained by a single IVUS study renders manual analysis impractical for clinical use. A computerized method is needed to accelerate the process and eliminate user-dependency. In this study, a new algorithm is used to identify the lumen border and the media-adventitia border (the external elastic membrane). Setting an initial surface on the IVUS catheter perimeter and using active contour principles, the surface inflates until virtual force equilibrium defined by the surface geometry and image features is reached. The method extracts these features in three dimensions (3-D). Eight IVUS procedures were performed using an automatic pullback device. Using the ECG signal for synchronization, sets of images covering the entire studied region and corresponding to the same cardiac phase were sampled. Lumen and media-adventitia border contours were traced manually and compared to the automatic results obtained by the suggested method. Linear regression results for vessel area enclosed by the lumen and media-adventitia border indicate high correlation between manual vs. automatic tracings (y = 1.07 x -0.38; r = 0.98; SD = 0.112 mm(2); n = 88). These results indicate that the suggested algorithm may potentially provide a clinical tool for accurate lumen and plaque assessment.     

A quantitative comparison of regional myocardial motion in mice,rabbits and humans using in-vivo phase contrast CMR

Background

Genetically manipulated animals like mice or rabbits play an important role in the exploration of human cardiovascular diseases. It is therefore important to identify animal models that closely mimic physiological and pathological human cardiac function.

Methods

In-vivo phase contrast cardiovascular magnetic resonance (CMR) was used to measure regional three-directional left ventricular myocardial motion with high temporal resolution in mice (N=18), rabbits (N=8), and humans (N=20). Radial, long-axis, and rotational myocardial velocities were acquired in left ventricular basal, mid-ventricular, and apical short-axis locations.

Results

Regional analysis revealed different patterns of motion: 1) In humans and rabbits, the apex showed slower radial velocities compared to the base. 2) Significant differences within species were seen in the pattern of long-axis motion. Long-axis velocities during systole were fairly homogeneously distributed in mice, whereas humans showed a dominant component in the lateral wall and rabbits in the base. 3) Rotational velocities and twist showed the most distinct patterns in both temporal evolution and relative contribution of base, mid-ventricle and apex, respectively. Interestingly, a marked difference in rotational behavior during early-systole was found in mice, which exhibited clockwise rotation in all slice locations compared to counter-clockwise rotation in rabbits and humans.

Conclusions

Phase contrast CMR revealed subtle, but significantly different regional myocardial motion patterns in mice, rabbits and humans. This finding has to be considered when investigating myocardial motion pattern in small animal models of heart disease.     

A preliminary study of a quantitative analysis method for high speed laryngoscopic images

High speed laryngoscopic study provides new information on the vocal fold vibratory patterns which has not been made possible before. Qualitative analysis of high speed laryngoscopic images is commonly reported in the literature. However, quantitative studies are necessary to provide objective measures for research and clinical purposes. This study reports the development of the High Speed Video Processing Program in analysing high-speed laryngoscopic video images obtained from 15 non-dysphonic speakers. Nine glottal ratio indices that represented the full opened glottal area, glottal width and glottal length were computed using the processing program. Data from two dysphonic participants were included to illustrate the manifestation of these glottal measures in dysphonic voices. Some of these glottal ratio index measures showed high variability across participants. However, significant differences were found in a number of index measures among the creaky, modal and falsetto registers in the non-dysphonic participants. In addition, the glottal area ratio index was found to be specifically sensitive in detecting dysphonia in high speed glottal images. The proposed semi-automatic High Speed Video Processing Program showed value in the analysis of high speed laryngoscopic images.     

A quantitative comparison of NIRS and fMRI across multiple cognitive tasks

Near infrared spectroscopy (NIRS) is an increasingly popular technology for studying brain function. NIRS presents several advantages relative to functional magnetic resonance imaging (fMRI), such as measurement of concentration changes in both oxygenated and deoxygenated hemoglobin, finer temporal resolution, and ease of administration, as well as disadvantages, most prominently inferior spatial resolution and decreased signal-to-noise ratio (SNR). While fMRI has become the gold standard for in vivo imaging of the human brain, in practice NIRS is a more convenient and less expensive technology than fMRI. It is therefore of interest to many researchers how NIRS compares to fMRI in studies of brain function. In the present study we scanned participants with simultaneous NIRS and fMRI on a battery of cognitive tasks, placing NIRS probes over both frontal and parietal brain regions. We performed detailed comparisons of the signals in both temporal and spatial domains. We found that NIRS signals have significantly weaker SNR, but are nonetheless often highly correlated with fMRI measurements. Both SNR and the distance between the scalp and the brain contributed to variability in the NIRS/fMRI correlations. In the spatial domain, we found that a photon path forming an ellipse between the NIRS emitter and detector correlated most strongly with the BOLD response. Taken together these findings suggest that, while NIRS can be an appropriate substitute for fMRI for studying brain activity related to cognitive tasks, care should be taken when designing studies with NIRS to ensure that: 1) the spatial resolution is adequate for answering the question of interest and 2) the design accounts for weaker SNR, especially in brain regions more distal from the scalp.     

应变率成像与定量组织速度成像技术评价高血压病左室舒张功能

目的探讨应变率成像与定量组织速度成像技术评价高血压病左室舒张功能的临床价值。方法采用脉冲多普勒、应变率成像与定量组织速度成像技术检测了30例高血压病患者和30例正常人的二尖瓣口舒张期血流频谱、左室基底部和中部(左室前壁、下壁、前间隔、后壁、后间隔和侧壁)心内膜下心肌层的应变率曲线以及二尖瓣环间隔、侧壁、前壁、下壁四个位置的舒张早期运动峰值速度Ea波与舒张晚期运动峰值速度Aa波。结果高血压病组的E/A与正常对照组比较降低(P<0.05),而左室舒张早期与舒张晚期峰值应变率比值(SRe/SRa)及Ea/Aa与正常对照组比较显著降低(P<0.01)。两组的Ea/Aa与E/A呈显著正相关(高血压病组:r=0.712,P<0.01;正常对照组:r=0.748,P<0.01);两组的SRe/SRa与E/A也呈显著正相关(高血压病组:r=0.805,P<0.01;正常对照组:r=0.769,P<0.01)。结论Ea/Aa、SRe/SRa比E/A能更敏感反映高血压病患者的左室舒张功能状况。