A Novel Normalized Cross-Correlation Speckle-Tracking Ultrasound Algorithm for the Evaluation of Diaphragm Deformation

Ultrasound assessment of diaphragmatic dysfunction in non-critically ill patients: relevant indicators and update

Diaphragm dysfunction (DD) can be classified as mild, resulting in diaphragmatic weakness, or severe, resulting in diaphragmatic paralysis. Various factors such as prolonged mechanical ventilation, surgical trauma, and inflammation can cause diaphragmatic injury, leading to negative outcomes for patients, including extended bed rest and increased risk of pulmonary complications. Therefore, it is crucial to protect and monitor diaphragmatic function. Impaired diaphragmatic function directly impacts ventilation, as the diaphragm is the primary muscle involved in inhalation. Even unilateral DD can cause ventilation abnormalities, which in turn lead to impaired gas exchange, this makes weaning from mechanical ventilation challenging and contributes to a higher incidence of ventilator-induced diaphragm dysfunction and prolonged ICU stays. However, there is insufficient research on DD in non-ICU patients, and DD can occur in all phases of the perioperative period. Furthermore, the current literature lacks standardized ultrasound indicators and diagnostic criteria for assessing diaphragmatic dysfunction. As a result, the full potential of diaphragmatic ultrasound parameters in quickly and accurately assessing diaphragmatic function and guiding diagnostic and therapeutic decisions has not been realized.

An optimized optical-flow-based method for quantitative tracking of ultrasound-guided right diaphragm deformation

OBJECTIVES

To develop a quantitative analysis method for right diaphragm deformation. This method is based on optical flow and applied to diaphragm ultrasound imaging.

METHODS

This study enrolls six healthy subjects and eight patients under mechanical ventilation. Dynamic images with 3-5 breathing cycles were acquired from three directions of right diaphragm by a portable ultrasound system. Filtering and density clustering algorithms are used for denoising Digital Imaging and Communications in Medicine (DICOM) data. An optical flow based method is applied to track movements of the right diaphragm. An improved drift correction algorithm is used to optimize the results. The method can automatically analyze the respiratory cycle, inter-frame/cumulative vertical and horizontal displacements, and strain of the input right diaphragm ultrasound image.

RESULTS

The optical-flow-based diaphragm ultrasound image motion tracking algorithm can accurately track the right diaphragm during respiratory motion. There are significant differences in horizontal and vertical displacements in each section (p-values < 0.05 for all). Significant differences are found between healthy subjects and mechanical ventilation patients for both horizontal and vertical displacements in Section III (p-values < 0.05 for both). There is no significant difference in global strain in each section between healthy subjects and mechanical ventilation patients (p-values > 0.05 for all).

CONCLUSIONS

The developed method can quantitatively evaluate the inter-frame/cumulative displacement of the diaphragm in both horizontal and vertical directions, as well as the global strain in three different imaging planes. The above indicators can be used to evaluate diaphragmatic dynamics.

Noninvasive Aortic Ultrafast Pulse Wave Velocity Associated With Framingham Risk Model: in vivo Feasibility Study

Background

Aortic pulse wave velocity (PWV) enables the direct assessment of aortic stiffness, which is an independent risk factor of cardiovascular (CV) events. The aim of this study is to evaluate the association between aortic PWV and CV risk model classified into three groups based on the Framingham risk score (FRS), i.e., low-risk (&lt;10%), intermediate-risk (10~20%) and high-risk (&gt;20%).

Methods

To noninvasively estimate local PWV in an abdominal aorta, a high-spatiotemporal resolution PWV measurement method (&gt;1 kHz) based on wide field-of-view ultrafast curved array imaging (ufcPWV) is proposed. In the ufcPWV measurement, a new aortic wall motion tracking algorithm based on adaptive reference frame update is performed to compensate errors from temporally accumulated out-of-plane motion. In addition, an aortic pressure waveform is simultaneously measured by applanation tonometry, and a theoretical PWV based on the Bramwell-Hill model (bhPWV) is derived. A total of 69 subjects (aged 23–86 years) according to the CV risk model were enrolled and examined with abdominal ultrasound scan.

Results

The ufcPWV was significantly correlated with bhPWV (r = 0.847, p &lt; 0.01), and it showed a statistically significant difference between low- and intermediate-risk groups (5.3 ± 1.1 vs. 8.3 ± 3.1 m/s, p &lt; 0.01), and low- and high-risk groups (5.3 ± 1.1 vs. 10.8 ± 2.5 m/s, p &lt; 0.01) while there is no significant difference between intermediate- and high-risk groups (8.3 ± 3.1 vs. 10.8 ± 2.5 m/s, p = 0.121). Moreover, it showed a significant difference between two evaluation groups [low- (&lt;10%) vs. higher-risk group (≥10%)] (5.3 ± 1.1 vs. 9.4 ± 3.1 m/s, p &lt; 0.01) when the intermediate- and high-risk groups were merged into a higher-risk group.

Conclusion

This feasibility study based on CV risk model demonstrated that the aortic ufcPWV measurement has the potential to be a new approach to overcome the limitations of conventional systemic measurement methods in the assessment of aortic stiffness.