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Registered images of the integrated backscatter coefficient. (a) 60 dB L38-22v parametric IBC image, (b) SHG, and (c) 60 dB L22-14v parametric IBC image. The solid white arrows point out the dense collagen bundle visible in SHG and in both IBC images. The dashed red arrows point out regions of high backscatter with no clear correspondence to SHG. The dash-dotted blue arrow points out a band of dense collagen not visible on the IBC images. These images are maximum intensity projections of a 400 µm section, corresponding to a region-of-interest above the off-axis clutter. All images are registered onto the SHG coordinate system. The scale bars are 2000 µm long. The colorbars are in dB.
Source publication
High-frequency quantitative ultrasound is a potential non-invasive source of imaging cell-tissue scale biomarkers for major diseases such as heart disease, cancer, and preterm birth. However, one of the barriers to developing such biomarkers is that it is labor-intensive to compare quantitative ultrasound images to optical images of the tissue stru...
Citations
... 22,23 To estimate the BSC or ACS, traditionally the IQ data is converted back into RF data first, and then the QUS parameters are estimated from the RF. 24,25 However, because the power spectrum from the IQ data and RF data are similar to each other, and QUS is a spectral-based method, it is reasonable that the IQ data could be used to calculate QUS parameters without a loss in information. Furthermore, every time the RF data is converted to IQ or IQ converted back to RF, potential artifacts can appear caused by phase imbalance, local oscillator leakage, and/or local oscillator phase noise. ...
Quantitative ultrasound (QUS) is an imaging technique which includes spectral-based parameterization. Typical spectral-based parameters include the backscatter coefficient (BSC) and attenuation coefficient slope (ACS). Traditionally, spectral-based QUS relies on the radio frequency (RF) signal to calculate the spectral-based parameters. Many clinical and research scanners only provide the in-phase and quadrature (IQ) signal. To acquire the RF data, the common approach is to convert IQ signal back into RF signal via mixing with a carrier frequency. In this study, we hypothesize that the performance, that is, accuracy and precision, of spectral-based parameters calculated directly from IQ data is as good as or better than using converted RF data. To test this hypothesis, estimation of the BSC and ACS using RF and IQ data from software, physical phantoms and in vivo rabbit data were analyzed and compared. The results indicated that there were only small differences in estimates of the BSC between when using the original RF, the IQ derived from the original RF and the RF reconverted from the IQ, that is, root mean square errors (RMSEs) were less than 0.04. Furthermore, the structural similarity index measure (SSIM) was calculated for ACS maps with a value greater than 0.96 for maps created using the original RF, IQ data and reconverted RF. On the other hand, the processing time using the IQ data compared to RF data were substantially less, that is, reduced by more than a factor of two. Therefore, this study confirms two things: (1) there is no need to convert IQ data back to RF data for conducting spectral-based QUS analysis, because the conversion from IQ back into RF data can introduce artifacts. (2) For the implementation of real-time QUS, there is an advantage to convert the original RF data into IQ data to conduct spectral-based QUS analysis because IQ data-based QUS can improve processing speed.
... This technique is highly accessible due to its low cost, portability, and utility within a variety of clinical applications (e.g., echocardiogram, prenatal monitoring, guided biopsy, etc.). One can retrieve elastic properties of tissue (4,5), blood flow information with Doppler setting (6), and acoustic properties (such as effective scattering size or radiofrequency [RF] envelope statistics) using phantom-calibrated Quantitative Ultrasound (QUS) (7)(8)(9). For instance, QUS has been utilized for noninvasively monitoring xenograft response to heat treatment (10) and radiotherapy (11). ...
... Laser power may need adjustment. 7. Light from the sample plane is collected once more via the camera at the gate position of maximum intensity to maximize the SNR of TPSF acquisition while ensuring that there is no detector saturation. ...
During drug development, preclinical in vivo validation of a viable drug candidate is a necessary unavoidable step before successful Food and Drug Administration’s (FDA) approval process for clinical trial. Conventional preclinical in vivo studies are performed using immunocompromised mouse models – including cell-line-based and patient-derived xenograft (PDX) models. Further, the validation of a drug’s delivery efficacy is performed ex vivo – that is, via animal sacrifice, tissue extraction and subsequent biomolecular assays. This method is time/financially consuming, does not permit longitudinal study, requires many animal subjects, and does not accurately reflect in vivo physiology. For any viable preclinical imaging platform, especially ones associated with the assessment of drug delivery and efficacy, it is crucial to be able to image the whole body of small animal models. Hence, any proposed imaging approach should be able to image a large field of view with high sensitivity. In addition, biological investigations are greatly enhanced by the ability to image multiple biomarkers simultaneously. This can be done by multiplexing the fluorescence probes spectrally and via lifetime sensing. However, increasing the dimensionality of the acquired data leads to significantly increased complexity in the imaging apparatus, data quantification and experimental protocols. Förster Resonance Energy Transfer (FRET) imaging can sense protein-protein interaction events at the distance of 2-10nm, which is the range in which binding of antibodies/protein ligands to their respective receptors often occur. Indeed, we have demonstrated the capability of Macroscopic Fluorescence Lifetime Imaging (MFLI)-FRET for noninvasive, whole-body quantitative monitoring of receptor-ligand binding (e.g. Transferrin-Transferrin Receptor, [Tf-TfR]) both in tumor xenografts and for monitoring pharmacokinetic activity in mice.
The capability to monitor drug efficacy noninvasively over an entire live intact animal model would offer many significant improvements to the current paradigm: true longitudinal studies, decrease of the number of animal models needed (minimizing intra-animal variances), intact physiological context and, if imaging can be performed fast enough, pharmacokinetic monitoring post-injection. Most importantly, a technique capable of whole-body imaging as described above should result in higher success rates upon reaching human trials, given the significantly richer information that can be collected and used to decide whether to proceed with a candidate. The objective of the project presented herein is to further develop a cost-effective, noninvasive, and user-friendly whole-body optical imaging workflow capable of meeting these needs. Hence, the results of this project will act as a giant leap forward with regards to propelling optical molecular imaging into conventional preclinical research and development.
