Motion Artifact Reduction in Ultrasound Based Thermal Strain Imaging of Atherosclerotic Plaques Using Time Series Analysis

Separated Respiratory Phases for In Vivo Ultrasonic Thermal Strain Imaging

Thermal strain imaging (TSI) uses echo shifts in ultrasonic B-scan images to estimate changes in temperature which is of great values for thermotherapies. However, for in vivo applications, it is difficult to overcome the artifacts and errors arising from physiological motions. Here, a respiration separated TSI (RS-TSI) method is proposed, which can be considered as carrying out TSI in each of the exhalation and inhalation phases and then combining the results. Normalized cross correlation (NXcorr) coefficient between RF images along the timeline are used to extract the respiratory frequency, after which reference frames are selected to identify the exhalation and inhalation phases, and the two phases are divided quasi-periodically. RF images belonging to both phases are selected by applying NXcorr thresholds, and motion compensation together with a second frame selection helps to obtain two finely matched image sequences. After TSI calculations for each phase, the two processes are merged into one through extrapolation and interphase averaging. Compared to TSI based on dynamic frame selection (DFS), RS-TSI ensures that frames are selected during both the exhalation and inhalation phases while setting the frame selection range according to the respiratory frequency helps to improve motion compensation. The temporal intervals of TSI output are approximately half that employing DFS.

Non-invasive Assessment of Liver Fat in ob/ob Mice Using Ultrasound-Induced Thermal Strain Imaging and Its Correlation with Hepatic Triglyceride Content

Non-alcoholic fatty liver disease is the accumulation of triglycerides in liver. In its malignant form, it can proceed to steatohepatitis, fibrosis, cirrhosis, cancer and ultimately liver impairment, leading to liver transplantation. In a previous study, ultrasound-induced thermal strain imaging (US-TSI) was used to distinguish between excised fatty livers from obese mice and non-fatty livers from control mice. In this study, US-TSI was used to quantify lipid composition of fatty livers in ob/ob mice (n = 28) at various steatosis stages. A strong correlation coefficient was observed (R² = 0.85) between lipid composition measured with US-TSI and hepatic triglyceride content. Hepatic triglyceride content is used to quantify adipose tissue in liver. The ob/ob mice were divided into three groups based on the degree of steatosis that is used in clinics: none, mild and moderate. A non-parametric Kruskal-Wallis test was conducted to determine if US-TSI can potentially differentiate among the steatosis grades in non-alcoholic fatty liver disease.

Thermal strain imaging in vivo using interpolated IQ-images

Thermal strain imaging (TSI) is a promising technique for ultrasonic thermometry, especially in the applications of thermal therapies. The accuracy of TSI is dependent on the sampling rate and line density of B-Scan images, and the prevalent IQ-demodulated ultrasound data outputted from low- and middle-end machines are therefore insufficient. Here, the feasibility of using interpolated IQ images for TSI (based on the "infinitesimal echo strain filter" model) is studied through in vivo experiments targeting the perirenal fat of pigs. It is demonstrated that, axial interpolations, especially those using the zero-padding algorithm, can recover the capabilities of the low-sampling-rate complex IQ images in TSI, and make their performances comparable to those of RF/IQ complex images with higher sample rate. Meanwhile, interpolations along the lateral direction can increase the line density of IQ images, reduce TSI errors, and reveal more details in the temperature maps. In the experiments, the variation in the thermometry coefficient (the k-value) is well below 3%. The findings here bring down the requirement of high sampling rate as well as high line density of US images in TSI, making it possible to be applied on common US machines.

Intravascular Photothermal Strain Imaging for Lipid Detection

Cardiovascular disease (CVD) is one of the major threats to humanity, accounting for one-third of the world's deaths. For patients with high-risk CVD, plaque rupture can lead to critical condition. It is therefore important to determine the stability of the plaque and classify the patient's risk level. Lipid content is an important determinant of plaque stability. However, conventional intravascular imaging methods have limitations in finding lipids. Therefore, new intravascular imaging techniques for plaque risk assessment are urgently needed. In this study, a novel photothermal strain imaging (pTSI) was applied to an intravascular imaging system for detecting lipids in plaques. As a combination of thermal strain imaging and laser-induced heating, pTSI differentiates lipids from other tissues based on changes in ultrasound (US) velocity with temperature change. We designed an optical pathway to an intravascular ultrasound catheter to deliver 1210-nm laser and US simultaneously. To establish the feasibility of the intravascular pTSI system, we experimented with a tissue-mimicking phantom made of fat and gelatin. Due to the difference in the strain during laser heating, we can clearly distinguish fat and gelatin in the phantom. The result demonstrates that pTSI could be used with conventional intravascular imaging methods to detect the plaque lipid.

Multi-Focus Beamforming for Thermal Strain Imaging Using a Single Ultrasound Linear Array Transducer

Ultrasound-induced thermal strain imaging (TSI) has been used successfully to identify lipid- and water-based tissues in atherosclerotic plaques in some research settings. However, TSI faces several challenges to be realized in clinics. These challenges include motion artifacts and displacement tracking accuracy, as well as limited heating capability, which contributes to low thermal strain signal-to-noise ratio, and a limited field of view. Our goal was to address the challenge in heating tissue in TSI. Current TSI systems use separate heating and imaging transducers, which require physical alignment of the heating and imaging beams and result in a bulky setup that limits in vivo operation. We evaluated a new design for heating beams that can be implemented on a linear array imaging transducer and can provide improved heating area and efficiency as compared with previous implementations. The heating beams designed were implemented with a clinical linear array imaging transducer connected to a research ultrasound platform. In vitro experiments using tissue-mimicking phantoms with no blood flow revealed that the new design resulted in an effective heating area of approximately 0.85 cm2 and a 0.3°C temperature rise in 2 s of heating, which compared well with in silico finite-element simulations. With the new heating beams, TSI was found to be able to detect a lipid-mimicking rubber inclusion with a diameter of 1 cm from the water-based gelatin background, with a strain contrast of 2.3 (+0.14% strain in the rubber inclusion and -0.06% strain in the gelatin background). Lastly, lipid-based tissue in a 1-cm-diameter human carotid endarterectomy (CEA) sample was identified in good agreement with histology.

Uncertainty estimation for temperature measurement with diagnostic ultrasound

Background

Ultrasound therapies are promising, non-invasive applications with potential to significantly improve, e.g. cancer therapies like viro- or immunotherapy or surgical applications. However, a crucial step towards their breakthrough is still missing: affordable and easy-to-handle quality assurance tools for therapy devices and ways to verify treatment planning algorithms. This deficiency limits the safety and comparability of treatments.

Methods

To overcome this deficiency accurate spatial and temporal temperature maps could be used. In this paper, the suitability of temperature calculation based on time-shifts of diagnostic ultrasound backscattered signals (echo-time-shift) is investigated and associated uncertainties are estimated. Different analysis variations were used to calculate the time-shifts: discrete and continuous methods as well as different frames as a reference for temperature calculation (4 s before, 16 s before the frame of interest, base frame). A sigmoid function was fitted and used to calculate temperatures. Two-dimensional temperature maps recorded during and after therapeutic ultrasound sonication were examined. All experiments were performed in agar-graphite phantoms mimicking non-fatty tissue, with high-intensity focused ultrasound being the source of heating.

Results

Continuous methods are more accurate than discrete ones, and uncertainties of calculated temperatures are in general lower, the earlier the reference frame was recorded. Depending on the purpose of the measurement, a compromise has to be made between the following: calculation accuracy (early reference frame), tolerance towards small movements (late reference frame), reproducing large temperature changes or cooling processes (reference frame at a certain point in time), speed of the algorithm (discrete (fast) vs. continuous (slower) shift calculation), and spatial accuracy (interval size for index-shift calculation). Within the range from 20 °C to 44 °C, uncertainties as low as 12.4 % are possible, being mainly due to medium properties.

Conclusions

Temperature measurements using the echo-time-shift method might be useful for validation of treatment plan algorithms. This might also be a comparatively accurate, fast, and affordable method for laboratory and clinical quality assessment. Further research is necessary to improve filter algorithms and to extend this method to multiple foci and the usage of temperature-dependent tissue quantities. We used an analytical approach to investigate the uncertainties of temperature measurement. Different analysis variations are compared to determine temperature distribution and development over time.

An adaptive displacement estimation algorithm for improved reconstruction of thermal strain

Thermal strain imaging (TSI) can be used to differentiate between lipid and water-based tissues in atherosclerotic arteries. However, detecting small lipid pools in vivo requires accurate and robust displacement estimation over a wide range of displacement magnitudes. Phase-shift estimators such as Loupas' estimator and time-shift estimators such as normalized cross-correlation (NXcorr) are commonly used to track tissue displacements. However, Loupas' estimator is limited by phase-wrapping and NXcorr performs poorly when the SNR is low. In this paper, we present an adaptive displacement estimation algorithm that combines both Loupas' estimator and NXcorr. We evaluated this algorithm using computer simulations and an ex vivo human tissue sample. Using 1-D simulation studies, we showed that when the displacement magnitude induced by thermal strain was >λ/8 and the electronic system SNR was >25.5 dB, the NXcorr displacement estimate was less biased than the estimate found using Loupas' estimator. On the other hand, when the displacement magnitude was ≤λ/4 and the electronic system SNR was ≤25.5 dB, Loupas' estimator had less variance than NXcorr. We used these findings to design an adaptive displacement estimation algorithm. Computer simulations of TSI showed that the adaptive displacement estimator was less biased than either Loupas' estimator or NXcorr. Strain reconstructed from the adaptive displacement estimates improved the strain SNR by 43.7 to 350% and the spatial accuracy by 1.2 to 23.0% (P < 0.001). An ex vivo human tissue study provided results that were comparable to computer simulations. The results of this study showed that a novel displacement estimation algorithm, which combines two different displacement estimators, yielded improved displacement estimation and resulted in improved strain reconstruction.

Detecting hepatic steatosis using ultrasound-induced thermal strain imaging: an ex vivo animal study

Hepatic steatosis or fatty liver disease occurs when lipids accumulate within the liver and can lead to steatohepatitis, cirrhosis, liver cancer and eventual liver failure requiring liver transplant. Conventional brightness mode (B-mode) ultrasound (US) is the most common noninvasive diagnostic imaging modality used to diagnose hepatic steatosis in clinics. However, it is mostly subjective or requires a reference organ such as the kidney or spleen with which to compare. This comparison can be problematic when the reference organ is diseased or absent. The current work presents an alternative approach to noninvasively detecting liver fat content using US-induced thermal strain imaging (US-TSI). This technique is based on the difference in the change in the speed of sound as a function of temperature between water- and lipid-based tissues. US-TSI was conducted using two system configurations including a mid-frequency scanner with a single linear array transducer (5-14 MHz) for both imaging and heating and a high-frequency (13-24 MHz) small animal imaging system combined with a separate custom-designed US heating transducer array. Fatty livers (n = 10) with high fat content (45.6 ± 11.7%) from an obese mouse model and control livers (n = 10) with low fat content (4.8 ± 2.9%) from wild-type mice were embedded in gelatin. Then, US imaging was performed before and after US induced heating. Heating time periods of ∼ 3 s and ∼ 9.2 s were used for the mid-frequency imaging and high-frequency imaging systems, respectively, to induce temperature changes of approximately 1.5 °C. The apparent echo shifts that were induced as a result of sound speed change were estimated using 2D phase-sensitive speckle tracking. Following US-TSI, histology was performed to stain lipids and measure percentage fat in the mouse livers. Thermal strain measurements in fatty livers (-0.065 ± 0.079%) were significantly (p < 0.05) higher than those measured in control livers (-0.124 ± 0.037%). Using histology as a gold standard to classify mouse livers, US-TSI had a sensitivity and specificity of 70% and 90%, respectively. The area under the receiver operating characteristic curve was 0.775. This ex vivo study demonstrates the feasibility of using US-TSI to detect fatty livers and warrants further investigation of US-TSI as a diagnostic tool for hepatic steatosis.