Machine Independence of Ultrasound-Based Quantification of Vitreous Echodensities

Purpose

Quantitative ultrasound (QUS) provides objective indices of Vision Degrading Myodesopsia (VDM) that correlate with contrast sensitivity (CS). To date, QUS methods were only tested on a single ultrasound machine. Here, we evaluate whether QUS measurements are machine independent.

Methods

In this cross-sectional study, 47 eyes (24 subjects; age = 53.2 ± 14.4 years) were evaluated with Freiburg acuity contrast testing (%Weber), and ultrasonography using 2 machines: one with a 15-MHz single-element transducer and one with a 5-ring, 20-MHz annular-array. Images were acquired from each system in sequential scans. Artifact-free, log-compressed envelope data were processed to yield three parameters (mean amplitude, M; energy, E; and percentage filled by echodensities, P50) and a composite score (C). A B-mode normalization method was applied to the 20-MHz datasets to match QUS parameters at both frequencies. Statistical analyses were performed to evaluate correlations among CS, E, M, P50, and C for both machines.

Results

QUS parameters from each machine correlated with CS (R ≥ 0.57, P < 0.001) and there was correlation between machines (R ≥ 0.84, P < 0.001). Correlations between CS and QUS parameters were statistically similar for both machines (P ≥ 0.14) except when the 20-MHz data were normalized (P = 0.04). Reproducibility of QUS parameters computed from 20-MHz data were satisfactory (52.3%-96.3%) with intraclass correlation values exceeding 0.80 (P < 0.001).

Conclusions

The high correlation between QUS parameters from both machines combined with a statistically similar correlation to CS suggests QUS is an effective, machine-independent, quantitative measure of vitreous echodensities.

Translational Relevance

QUS may be applied across clinical ophthalmic ultrasound scanners and imaging frequencies to effectively evaluate VDM.

RECURRENT FLOATERS AFTER LIMITED VITRECTOMY FOR VISION DEGRADING MYODESOPSIA

PURPOSE

Limited vitrectomy improves vision degrading myodesopsia, but the incidence of recurrent floaters postoperatively is not known. We studied patients with recurrent central floaters using ultrasonography and contrast sensitivity (CS) testing to characterize this subgroup and identify the clinical profile of patients at risk of recurrent floaters.

METHODS

A total of 286 eyes (203 patients, 60.6 ± 12.9 years) undergoing limited vitrectomy for vision degrading myodesopsia were studied retrospectively. Sutureless 25G vitrectomy was performed without intentional surgical posterior vitreous detachment (PVD) induction. CS (Freiburg Acuity Contrast test: Weber index, %W) and vitreous echodensity (quantitative ultrasonography) were assessed prospectively.

RESULTS

No eyes (0/179) with preoperative PVD experienced new floaters. Recurrent central floaters occurred in 14/99 eyes (14.1%) without complete preoperative PVD (mean follow-up = 39 months vs. 31 months in 85 eyes without recurrent floaters). Ultrasonography identified new-onset PVD in all 14 (100%) recurrent cases. Young (younger than 52 years; 71.4%), myopic (≥-3D; 85.7%), phakic (100%) men (92.9%) predominated. Reoperation was elected by 11 patients, who had partial PVD preoperatively in 5/11 (45.5%). At study entry, CS was degraded (3.55 ± 1.79 %W) but improved postoperatively by 45.6% (1.93 ± 0.86 %W, P = 0.033), while vitreous echodensity reduced by 86.6% ( P = 0.016). New-onset PVD postoperatively degraded CS anew, by 49.4% (3.28 ± 0.96 %W; P = 0.009) in patients electing reoperation. Repeat vitrectomy normalized CS to 2.00 ± 0.74%W ( P = 0.018).

CONCLUSION

Recurrent floaters after limited vitrectomy for vision degrading myodesopsia are caused by new-onset PVD, with younger age, male sex, myopia, and phakic status as risk factors. Inducing surgical PVD at the primary operation should be considered in these select patients to mitigate recurrent floaters.

High-frequency ultrasound point-of-care device to quantify myopia-induced microstructural changes in the anterior sclera

PURPOSE

To develop a point-of-care (POC) device using high-frequency ultrasound (US) for evaluating microstructural changes in the anterior sclera associated with myopia.

METHODS

The proposed POC device must satisfy four primary requirements for effective clinical use: the measurement component is handheld; the software must be simple and provide real-time feedback; patient safety and health data security requirements set forth by relevant governing bodies must be satisfied and the measurement data must have sufficient signal-to-noise ratio (SNR) and repeatability. Radiofrequency (RF) echo data acquired by the POC device will be processed using our quantitative US methods to characterise tissue microstructure and biomechanical properties.

RESULTS

All stated requirements have been met in the developed POC device. The high-frequency transducer is housed in a custom, 3D-printed, pen-like holder that allows for easy measurements of the anterior sclera. Custom software provides a simple interface for data acquisition, real-time data display and secure data storage. Exposimetry measurements of the US pressure field indicate device compliance with United States Food and Drug Administration limits for ophthalmic US. In vivo measurements on a volunteer suggest the RF data SNR and acquisition consistency are suitable for quantitative analysis.

CONCLUSIONS

A fully functioning POC device using high-frequency US has been created for evaluating the microstructure of the anterior sclera. Planned studies using the POC device to scan the eyes of myopia patients will help clarify how the anterior sclera microstructure may be affected by myopia. If effective, this portable, inexpensive and user-friendly system could be an important part of routine eye examinations.

Classification of Metastatic Lymph Nodes In Vivo Using Quantitative Ultrasound at Clinical Frequencies

Quantitative ultrasound (QUS) methods characterizing the backscattered echo signal have been of use in assessing tissue microstructure. High-frequency (30 MHz) QUS methods have been successful in detecting metastases in surgically excised lymph nodes (LNs), but limited evidence exists regarding the efficacy of QUS for evaluating LNs in vivo at clinical frequencies (2-10 MHz). In this study, a clinical scanner and 10-MHz linear probe were used to collect radiofrequency (RF) echo data of LNs in vivo from 19 cancer patients. QUS methods were applied to estimate parameters derived from the backscatter coefficient (BSC) and statistics of the envelope-detected RF signal. QUS parameters were used to train classifiers based on linear discriminant analysis (LDA) and support vector machines (SVMs). Two BSC-based parameters, scatterer diameter and acoustic concentration, were the most effective for accurately detecting metastatic LNs, with both LDA and SVMs achieving areas under the receiver operating characteristic (AUROC) curve ≥0.94. A strategy of classifying LNs based on the echo frame with the highest cancer probability improved performance to 88% specificity at 100% sensitivity (AUROC = 0.99). These results provide encouraging evidence that QUS applied at clinical frequencies may be effective at accurately identifying metastatic LNs in vivo, helping in diagnosis while reducing unnecessary biopsies and surgical treatments.

Advanced Topics in Quantitative Acoustic Microscopy

Quantitative acoustic microscopy (QAM) reconstructs two-dimensional (2D) maps of the acoustic properties of thin tissue sections. Using ultrahigh frequency transducers (≥ 100 MHz), unstained, micron-thick tissue sections affixed to glass are raster scanned to collect radiofrequency (RF) echo data and generate parametric maps with resolution approximately equal to the ultrasound wavelength. 2D maps of speed of sound, mass density, acoustic impedance, bulk modulus, and acoustic attenuation provide unique and quantitative information that is complementary to typical optical microscopy modalities. Consequently, many biomedical researchers have great interest in utilizing QAM instruments to investigate the acoustic and biomechanical properties of tissues at the micron scale. Unfortunately, current state-of-the-art QAM technology is costly, requires operation by a trained user, and is accompanied by substantial experimental challenges, many of which become more onerous as the transducer frequency is increased. In this chapter, typical QAM technology and standard image formation methods are reviewed. Then, novel experimental and signal processing approaches are presented with the specific goal of reducing QAM instrument costs and improving ease of use. These methods rely on modern techniques based on compressed sensing and sparsity-based deconvolution methods. Together, these approaches could serve as the basis of the next generation of QAM instruments that are affordable and provide high-resolution QAM images with turnkey solutions requiring nearly no training to operate.

Experimental Validation of Perfusion Imaging With HOSVD Clutter Filters

Novel pulsed-Doppler methods for perfusion imaging are validated using dialysis cartridges as perfusion phantoms. Techniques that were demonstrated qualitatively at 24 MHz, in vivo, are here examined quantitatively at 5 and 12.5 MHz using phantoms with the blood-mimicking fluid flow within cellulose microfibers. One goal is to explore a variety of flow states to optimize measurement sensitivity and flow accuracy. The results show that 2-3-s echo acquisitions at roughly 10 frames/s yield the highest sensitivity to flows of 1-4 mL/min. A second goal is to examine methods for setting the parameters of higher order singular value decomposition (HOSVD) clutter filters. For stationary or moving clutter, the velocity of the blood-mimicking fluid in the microfibers is consistently estimated within measurement uncertainty (mean coefficient of variation = 0.26). Power Doppler signals were equivalent for stationary and moving clutter after clutter filtering, increasing approximately 3 dB/mL/min of blood-mimicking fluid flow for 0 ≤ q ≤ 4 mL/min. Comparisons between phantom and preclinical images show that peripheral perfusion imaging can be reliably achieved without contrast enhancement.

Physics-guided machine learning for 3-D quantitative quasi-static elasticity imaging

We present a 3D extension of the Autoprogressive Method (AutoP) for quantitative quasi-static ultrasonic elastography (QUSE) based on sparse sampling of force-displacement measurements. Compared to current model-based inverse methods, our approach requires neither geometric nor constitutive model assumptions. We build upon our previous report for 2D QUSE and demonstrate the feasibility of recovering the 3D linear-elastic material property distribution of gelatin phantoms under compressive loads. Measurements of boundary geometry, applied surface forces, and axial displacements enter into AutoP where a Cartesian neural network constitutive model (CaNNCM) interacts with finite element analyses to learn physically consistent material properties with no prior constitutive model assumption. We introduce a new regularization term uniquely suited to AutoP that improves the ability of CaNNCMs to extract information about spatial stress distributions from measurement data. Results of our study demonstrate that acquiring multiple sets of force-displacement measurements by moving the US probe to different locations on the phantom surface not only provides AutoP with the necessary information for a CaNNCM to learn the 3D material property distribution, but may significantly improve the accuracy of the Young's modulus estimates. Furthermore, we investigate the trade-offs of decreasing the contact area between the US transducer and phantom surface in an effort to increase sensitivity to surface force variations without additional instrumentation. Each of these modifications improves the ability of CaNNCMs trained in AutoP to learn the spatial distribution of Young's modulus from force-displacement measurements.

Data-Driven Elasticity Imaging Using Cartesian Neural Network Constitutive Models and the Autoprogressive Method

Quasi-static elasticity imaging techniques rely on model-based mathematical inverse methods to estimate mechanical parameters from force-displacement measurements. These techniques introduce simplifying assumptions that preclude exploration of unknown mechanical properties with potential diagnostic value. We previously reported a data-driven approach to elasticity imaging using artificial neural networks (NNs) that circumvents limitations associated with model-based inverse methods. NN constitutive models can learn stress-strain behavior from force-displacement measurements using the autoprogressive (AutoP) method without prior assumptions of the underlying constitutive model. However, information about internal structure was required. We invented Cartesian NN constitutive models (CaNNCMs) that learn the spatial variations of material properties. We are presenting the first implementation of CaNNCMs trained with AutoP to develop data-driven models of 2-D linear-elastic materials. Both simulated and experimental force-displacement data were used as input to AutoP to show that CaNNCMs are able to model both continuous and discrete material property distributions with no prior information of internal object structure. Furthermore, we demonstrate that CaNNCMs are robust to measurement noise and can reconstruct reasonably accurate Young's modulus images from a sparse sampling of measurement data. CaNNCMs are an important step toward clinical use of data-driven elasticity imaging using AutoP.

An information-based machine learning approach to elasticity imaging

An information-based technique is described for applications in mechanical property imaging of soft biological media under quasi-static loads. We adapted the Autoprogressive method that was originally developed for civil engineering applications for this purpose. The Autoprogressive method is a computational technique that combines knowledge of object shape and a sparse distribution of force and displacement measurements with finite-element analyses and artificial neural networks to estimate a complete set of stress and strain vectors. Elasticity imaging parameters are then computed from estimated stresses and strains. We introduce the technique using ultrasonic pulse-echo measurements in simple gelatin imaging phantoms having linear-elastic properties so that conventional finite-element modeling can be used to validate results. The Autoprogressive algorithm does not require any assumptions about the material properties and can, in principle, be used to image media with arbitrary properties. We show that by selecting a few well-chosen force-displacement measurements that are appropriately applied during training and establish convergence, we can estimate all nontrivial stress and strain vectors throughout an object and accurately estimate an elastic modulus at high spatial resolution. This new method of modeling the mechanical properties of tissue-like materials introduces a unique method of solving the inverse problem and is the first technique for imaging stress without assuming the underlying constitutive model.