Multi-exposure Speckle Imaging for Quantitative Evaluation of Cortical Blood Flow

Santorelli, Adam; Sullender, Colin T.; Dunn, Andrew K.

doi:10.1007/978-1-0716-2926-0_10

Cited by 2 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Multi-exposure speckle contrast imaging (MELSCI) addresses this limitation by using high-speed cameras to capture multiple exposure times within a set of contrast values [8]. MELSCI enables researchers to capture a range of exposure times, generating a more accurate and sensitive speckle contrast measurement, which will provide more detailed information about blood flow dynamics and perfusion in biological systems [9].…”

Section: Introductionmentioning

confidence: 99%

Enhancing blood flow prediction in multi-exposure laser speckle contrast imaging through ensemble learning with K-mean clustering

Jain,

Gupta

2024

Biomed. Phys. Eng. Express

View full text Add to dashboard Cite

Purpose: Accurately visualizing and measuring blood flow is of utmost importance in maintaining optimal health and preventing the onset of various chronic diseases. One promising imaging technique that aids in visualizing perfusion in biological tissues is Multi-exposure Laser Speckle Contrast Imaging (MELSCI). MELSCI technique allows real-time quantitative measurements using multiple exposure times to obtain precise and reliable blood flow data. Additionally, the application of machine learning (ML) techniques can further enhance the accuracy of blood flow prediction in this imaging modality. Method: Our study focused on developing and evaluating Ensemble Learning ML techniques along with clustering algorithms for predicting blood flow rates in MELSCI. The effectiveness of these techniques was assessed using performance parameters, including accuracy, F1-score, precision, recall, specificity, and classification error rate. Result: Notably, the study revealed that Ensemble Learning with clustering emerged as the most accurate technique, achieving an impressive accuracy rate of 98.5%. Furthermore, it demonstrated a high recall of more than 91%, F1-score, the precision of more than 90%, higher specificity of 99%, and least classification error of 1.5%, highlighting its suitability and sustainability for flow prediction in MELSCI.Conclusion: The study's findings imply that Ensemble Learning can significantly contribute to enhancing the accuracy of blood flow prediction in MELSCI. This advancement holds substantial promise for healthcare professionals and researchers, as it facilitates improved understanding and assessment of perfusion within biological tissues, which will contribute to the maintenance of good health and prevention of chronic diseases.

show abstract

Section: Introductionmentioning

confidence: 99%

Enhancing blood flow prediction in multi-exposure laser speckle contrast imaging through ensemble learning with K-mean clustering

Jain,

Gupta

2024

Biomed. Phys. Eng. Express

View full text Add to dashboard Cite

show abstract

“…To overcome these limitations, multi-exposure laser speckle contrast imaging (MECI) extends LSCI by capturing different exposure times with high-speed cameras, allowing for more accurate and sensitive speckle contrast measurements [ 8 ]. MECI provides real-time quantitative measurements and offers a deeper understanding of blood flow dynamics and perfusion in biological systems [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-exposure Laser Speckle Contrast Imaging (MECI)-Based Prediction of Blood Flow Using Random Forest (RF) With K-Means (KM)

Jain¹,

Gupta²

2023

Cureus

View full text Add to dashboard Cite

Blood flow prediction is very important for medical diagnosis, drug development, tissue engineering, and continuous monitoring. One commonly used method for studying blood flow is called multi-exposure laser speckle contrast imaging (MECI). It provides valuable insights into how blood flows through tissues and helps in diagnosing circulatory diseases. In our study, we used MECI to measure blood flow in real-time by taking multiple measurements with different exposure times and contrasts. To predict different blood flow rates ranging from 0.1 to 1 mm/s, we employed machine learning (ML) techniques like clustering and random forest (RF) or support vector machine (SVM) algorithms. The study showed that RF with K-means performance is found to be the most accurate technique for flow classification, with an accuracy of 98.5%, a precision of 92%, a specificity of 98.9%, and a classification error of 1.5%. Our study demonstrates that employing clustering and RF algorithms in MECI provides a robust and effective approach to predicting blood flow. This technique holds great potential for a wide range of applications in the medical and healthcare fields.

show abstract

Multi-exposure Speckle Imaging for Quantitative Evaluation of Cortical Blood Flow

Cited by 2 publications

References 16 publications

Enhancing blood flow prediction in multi-exposure laser speckle contrast imaging through ensemble learning with K-mean clustering

Enhancing blood flow prediction in multi-exposure laser speckle contrast imaging through ensemble learning with K-mean clustering

Multi-exposure Laser Speckle Contrast Imaging (MECI)-Based Prediction of Blood Flow Using Random Forest (RF) With K-Means (KM)

Contact Info

Product

Resources

About