# A Patient-Adaptive, High MI Abdominal Scanner

> **NIH NIH R01** · DUKE UNIVERSITY · 2020 · $570,788

## Abstract

A Patient-Adaptive, High MI Abdominal Scanner
We propose to construct and clinically evaluate an adaptive ultrasonic scanner that quickly and automatically
adjusts system controls to optimize image quality and assists the sonographer in selecting a favorable acoustic
window. We hypothesize that the quality of adaptively-optimized and guided window-selection images will
exceed those acquired under conventional scanning conditions. We will test these hypotheses on a modified
commercial scanner under the realistic clinical condition of Hepatocellular Carcinoma (HCC) screening.
Optimized images will have rapidly- and adaptively-selected transmit power, frequency, focal depth(s), imaging
mode (fundamental or harmonic) and other imaging parameters and will be acquired at two Mechanical Indices
(the manufacturer’s default setting (MI=1.2), and the “patient-optimized” MI up to a limit of 2.5). On a significant
subset of patients, our previous work has shown significant image quality improvements and increased depths
of penetration associated with increased MI levels. Our initial studies, presented in this application, show the
potential clinical benefits of automated selection of MI and other imaging parameters. Automated selection of
MI, as proposed, will realize the ALARA (As Low as Reasonably Achievable) principle for acoustic exposure.
Currently, sonographers acquire dozens of individual images during HCC screening for physician review and
documentation. A number of published studies and our experience indicate that sonographers use system
controls quite sparingly, especially the transmit power level control. Automated selection of imaging
parameters and guided selection of acoustic windows should not only improve image quality and depth of
penetration, but should also improve the efficiency of scanning procedures and reduce sonographers’
ergonomic challenges. Our initial results and the clinical literature also demonstrate the importance of acoustic
window selection in improving image quality and the physical challenges that this task presents to
sonographers using current methods, especially in overweight and obese patients.
We propose to use the spatial coherence of backscattered echo signals as an image quality feedback
parameter. Temporal coherence reflects the electronic SNR and can be used to measure the effective imaging
depth in the liver. Our newly developed image quality metric, Lag One Coherence (LOC), quantifies the
combined image-degrading effects of reverberation, off-axis scatterers, phase aberration and limited SNR. Our
initial phantom and in vivo data demonstrate the robustness of the LOC image quality metric in rapidly
determining the optimum patient-specific settings for transmit power, harmonic vs. fundamental imaging, focal
depth, and frequency. Our initial data also supports the utility of the LOC in the real-time assessment of the
quality of various acoustic windows. We propose to further explore the optimization of these and ...

## Key facts

- **NIH application ID:** 9924596
- **Project number:** 5R01EB026574-03
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Gregg E. Trahey
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $570,788
- **Award type:** 5
- **Project period:** 2018-08-01 → 2022-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9924596

## Citation

> US National Institutes of Health, RePORTER application 9924596, A Patient-Adaptive, High MI Abdominal Scanner (5R01EB026574-03). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/9924596. Licensed CC0.

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