Session C: 1:45PM – 3:15PM

Engineering, Session C – Oral Presentations, Pano East, Union

SESSION C (1:45-3:15PM)
Location: Pano East, A. Ray Olpin University Union


Optimization of Computation Framework and User Interface to Analyze 4D Flow Cardiac MRI
Garrett McClellan, University of Utah

Faculty Mentor Lucas Timmins, University of Utah

SESSION C 1:45-2:00PM
Pano East, Union

Cardiovascular disease (CVD) is a rapidly growing problem that has become responsible for the highest percentage of global deaths[1][2]. The detection of CVD in patients is critical for proper treatment, with one such technique being cardiac magnetic resonance imaging (cMRI)[3]. Collected cMRI data can be used directly for the calculation of wall shear stress (WSS), which is the frictional force exerted on the wall of vessel by the blood[4]. The WSS calculation removes the need for complex patient-specific computational modeling techniques[5] and can be done using velocity or volumetric flowrate[4][6]. WSS was found to be a factor in the correlation between blood flow and atherosclerosis[7]. Atherosclerosis is the build-up of plaque on vessel walls and was found as an underlying condition in patients who developed CVD[8] thus it can be used in the detection of a patient’s risk of CVD. The aim of this project was to use MATLAB R2021a to optimize the computational algorithms used for the calculation of WSS directly from cMRI data, as well as to create a graphics user interface (app) for the calculation and validation of the WSS technique as a CVD detection method. Originally there were multiple MATLAB functions written and utilized in separate MATLAB scripts performing the calculations. These functions were optimized and then the MATLAB App Designer was used to create the app that utilized the optimized functions to analyze the cMRI data. The app is much easier to validate and repeat the WSS method than the previously used process. This project is significant because as this app evolves and these calculation methods are validated it will have the potential to provide an earlier less invasive CVD detection method. Providing the patients more time to make lifestyle changes, potentially having a greater impact on their health, drastically reducing their risk of CVD.
[1] “Cardiovascular diseases (cvds),” World Health Organization. [Online]. Available: [Accessed: 05-Aug-2022].
[2] G. A. Roth, G. A. Mensah, and V. Fuster, “The global burden of cardiovascular diseases and risks,” Journal of the American College of Cardiology, vol. 76, no. 25, pp. 2980-2981, 2020.
[3] “Heart disease,” Mayo Clinic, 05-Aug-2022. [Online]. Available: [Accessed: 05-Aug-2022].
[4]E. Iffrig, L. H. Timmins, R. El Sayed, W. R. Taylor, and J. N. Oshinski, “A new method for quantifying abdominal aortic wall shear stress using phase contrast magnetic resonance imaging and the Womersley Solution,” Journal of Biomechanical Engineering, vol. 144, no. 9, 2022.
[5]  L. H. Timmins, D. S. Molony, P. Eshtehardi, M. C. McDaniel, J. N. Oshinski, D. P. Giddens, and H. Samady, “Oscillatory wall shear stress is a dominant flow characteristic affecting lesion progression patterns and plaque vulnerability in patients with coronary artery disease,” Journal of The Royal Society Interface, vol. 14, no. 127, p. 20160972, 2017.
[6] Womersley, J. R. “Method for the Calculation of Velocity, Rate of Flow and Viscous Drag in Arteries When the Pressure Gradient Is Known.” The Journal of Physiology, vol. 127, no. 3, 1955, pp. 553-563.
[7] C. K. Zarins, D. P. Giddens, B. K. Bharadvaj, V. S. Sottiurai, R. F. Mabon, and S. Glagov, “Carotid bifurcation atherosclerosis. quantitative correlation of plaque localization with flow velocity profiles and wall shear stress.,” Circulation Research, vol. 53, no. 4, pp. 502-514, 1983.
[8] V. J. Dzau, E. M. Antman, H. R. Black, D. L. Hayes, J. A. E. Manson, J. Plutzky, J. J. Popma, and W.Stevenson, “The cardiovascular disease continuum validated: Clinical evidence of improved patient outcomes,” Circulation, vol. 114, no. 25, pp. 2850-2870, 2006.


Electrical Impedance Dermography as a Biomarker for Non-Melanoma Skin Cancer
Elaine Wong, University of Utah

Faculty Mentor Benjamin Sanchez, University of Utah

Pano East, Union

Clinical diagnosis of basal cell (BCC) and squamous cell (SCC) carcinoma subtypes is challenging. There are multiple subtypes of BCC and SCC that can be difficult to distinguish clinically and ideally require different biopsy techniques for optimal histologic analysis and therapeutic decision-making1. Visual detection of BCC and SCC can be facilitated with the aid of dermoscopy2 but determining prior to biopsy whether a lesion is superficial or more deeply invasive is usually not possible. There is a great clinical need to develop new technologies to augment visual skin examination to guide biopsy-decision-making and improve management of lesions suspicious for BCC and SCC. To date, there is no bedside technique available that is low cost, easily applied, quantitative, objective, and capable of overcoming these diagnostic hurdles. EID is a newer non-invasive, quantitative, and objective tool sensitive enough to detect alterations in the electrical properties of skin cancers. The overarching hypothesis of my proposal is that EID can be used to distinguish BCC subtypes and between SCC-in situ, invasive SCC, and inflamed keratosis that cannot be appreciated clinically. The “superficial” form of BCC is confined to the epidermis and can be effectively treated by non-surgical means. The “nodular” form of BCC consists of a collection of round tumor cells occupying the upper part of the dermis and can be treated by destruction or surgically depending on its size and location. “Micronodular” and “infiltrative” forms of BCC consist of smaller aggregates of tumor cells or angulated or stranded tumor cells, respectively, infiltrating the deeper dermis and usually require surgical treatment. Importantly, these invasive subtypes of BCC can present as papules or plaques that cannot reliably be distinguished clinically from nodular or the more superficial subtype of BCC. The superficial form of SCC can resemble BCC, and it is challenging clinically to distinguish this entity from invasive SCC; the former is best biopsied by shave technique while the latter is best biopsied by punch technique to assess depth of invasion3. These histologic changes cannot be reliably appreciated visually and thus distinguishing subtypes of BCC and SCC presents a clinical conundrum. EID technology could contribute to overall clinical assessment by increasing confidence and diagnostic accuracy that will inform biopsy-decision making in patients with lesions suspicious for skin cancer. My research showed EID to be very effective and efficient at diagnosing BCC and SCC. In the BCC study, I obtained a specificity of 88%. Similarly, in the SCC study, I achieved an averaged area under the curve of 0.968, sensitivity of 94.6%, and specificity of 96.9% (Fig.1). In both cases, my results exceed the diagnostic accuracy of using the dermoscope, the clinical gold standard technology.



Using in-vivo models to understand limitations of clinical applications of light scattering spectroscopy for biopsies
Sarthak Tiwari, University of Utah

Faculty Mentor Robert Hitchcock, University of Utah

SESSION C 2:25-2:40PM
Pano East, Union

Invasive biopsies are critical in the medical diagnosis and characterization of diseased tissues, but are hindered by risk of infection, tissue damage, and high post-processing time. Optical approaches for tissue characterization allows for faster and less invasive procedures [1]. One promising optical approach is light-scattering spectroscopy (LSS) [2, 3]. Using LSS, we distinguished tissue based on properties like nuclear density [4], and tissue composition [3]. However, questions remain about the efficacy of LSS in vivo, and its clinical applications. A potential use of LSS is identifying the cardiac conduction system, e.g. to avoid damaging during surgical repair of congenital defects. Here, we describe a study on the in situ, beating heart in canines to provide insight into the capabilities of LSS. Spectra were gathered in vivo from four adult canine models using a catheterized LSS probe [3,4]. 10 samples of 200 spectra were gathered from the atrium, ventricle, vena cava, and blood. Data analysis was performed in Matlab r2021a. The spectra were calibrated, normalized, and averaged across each set of 200 spectra. principal component analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) were used to reduce dimensionality [5]. ANOVA, with a Tukey-Kramer post hoc test and a significance level of 0.05, was used to identify differences between the first principal components of the different tissue regions [6]. The same approach was used with the first UMAP index. The results of the PCA and UMAP are shown in figure 1. The first two principal components explain 66.3% and 21.9% of the variance respectively. ANOVA using the first principal component yielded differences (p<0.05) between all groups except between the atrium and ventricle (p=0.86). The UMAP shows more visible separation between tissues, and ANOVA yielded every group to be statistically different using the first index (p<0.05). These results demonstrate the ability of the spectra to differentiate between tissue. The relative similarity of spectra from the atrium and ventricle was expected because both regions consist of muscle cells, which vary structurally from the blood and vena cava. We demonstrate the feasibility of using LSS in vivo to non-destructively characterize tissue regions in the heart. UMAP identified tissue-specific clustering in the first index, indicating that key information about tissue properties lie in the spectra. This study informed future studies for our LSS system. Further work using supervised learning approaches and heterogenous tissue samples will provide more insight into the capabilities of LSS for tissue discrimination [4,5]. LSS’ ability to differentiate tissue based on composition, both ex and in vivo, facilitates translation for medical applications. Our studies suggest that LSS has promise as a means of diagnosing and characterizing tissues in the heart, thereby improving our ability to quickly identify and effectively treat diseases.
Acknowledgements: This work was supported by the grant NIH-R01 HL135077.
References: [1] Wang TD. Clin. Gastroenterol. Hepatol. 2004. 9: 744-753. [2] Zhang L. Nat. Biomed. Eng. 2017. 1: 18. [3] Knighton NJ. Sensors. 2021, 18: 6033. [4] Knighton NJ. J Biomed Opt, 2021. 11: 1-15. [5] McInnes L. J. Open Source Softw., 2018. 3:861 [6] Yu MML. J. Forensic Sci. 2012. 57: 70-74.




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