Effects of Tissue Depth and Density on Detecting Congenital Defects Using Light Imaging

Tissue depth and density science fair 1-minute video introduction!

For my final science fair project, I wanted to create something meaningful that connected to my interest in ultrasound imaging and maternal and neonatal health. I have been accepted to Dalhousie University in the Bachelor of Science program, which I plan to use as a pathway into the Bachelor of Health Sciences to study Diagnostic Medical Ultrasound Technology.

Earlier this year, I wrote a research paper on pregnancy complications, including preeclampsia, ectopic pregnancies, and congenital defects. This work made me realize how important early and accurate detection is, especially in cases where delayed diagnosis can lead to serious, life-threatening risks and complications.

To further explore this, I investigated how tissue depth and density affect imaging clarity using light imaging. I simulated how imaging signals weaken as they travel through deeper, denser tissue, making abnormalities such as congenital defects more difficult to detect.

This project could benefit healthcare professionals and patients by improving understanding of the challenges involved in ultrasound imaging. Although the models use light rather than sound waves, they simulate the principle of wave attenuation, where signals weaken as they travel through deeper and denser tissue.

Table 1: Gelatin-to-water ratios used for each model.

I began by conducting background research to understand congenital defects, ultrasound and light imaging, and how imaging signals weaken as they travel through tissues of different depths and densities. I also researched the Doppler effect, which is used in ultrasound imaging to measure blood flow and wave frequency. In this project, light imaging was used as a model to represent how imaging signals behave, rather than as a direct medical imaging technique.

I designed models to represent human tissue using clear gelatin in a transparent container, with beads representing congenital defects. To control bead depth, I built the models in layers, pouring 1 cm of gelatin at a time and allowing each layer to partially set before adding the next, until a depth of 5 cm was reached. The first model tested depth, while the second included an additional bead placed beside the original to represent a nearby internal structure and test how it affected visibility. The third and fourth models used different gelatin-to-water ratios to simulate varying tissue densities (shown in Table 1).

To test visibility, I simulated light imaging by shining a flashlight directly above each model to represent how imaging signals travel through tissue, and used an iPhone camera to capture images for analysis. I then analyzed these images to observe how clearly each bead could be seen at different depths, densities, and arrangements. Data was collected by rating visibility on a scale from 1 (least visible) to 10 (most visible), based on how clearly the bead could be distinguished from the surrounding material.

Controlled variables included gelatin type, container, bead size, and light source, while the independent variables were bead depth, arrangement, and tissue density.

Figure one: shows the rating of visibility (scale from 1-10) in comparison to the depth (cm) of the bead being measured.

This experiment examined four models that each tested tissue depth, density, and surrounding structures through simulated light imaging. The gelatin models simulated imaging by allowing light to pass through layered material to represent how signals travel through human tissue.

The results showed a pattern: as depth increased, visibility decreased across all models. Rates of visibility consistently declined as depth increased, revealing a strong connection between depth and imaging clarity. This trend was consistent across all models, with visibility ratings decreasing at each increase in depth. Although all structures were slightly visible, those that were closer to the surface were much clearer and easier to identify, whereas those that were deeper appeared weaker due to shadowing and reduced clarity.

When observing the density models, the visibility of the two noticeably differed. The denser model was more yellow in colour and generated a much thicker material, making the deeper beads harder to clearly see. In comparison, the less dense model was almost entirely clear and liquid-like, enabling clearer visibility of all of the beads. This suggested that denser materials can affect imaging results, as signals must travel further to reach and return from deeper structures.

The model that entailed two beads at each depth demonstrated that nearby structures can also impact the clarity of an image. Though the images that I produced were reasonably clear, when dealing with deeper tissues the additional bead could make it difficult to distinguish the defect from the shadow.

Overall, the results show that depth, density, and surrounding structures all contribute to imaging clarity. These patterns indicate that visibility decreases as depth and density increase, especially when additional structures are present.

The first Image shows how imaging signals are reflected, scattered, or absorbed as they travel through tissues of different densities (low impedance is less dense, while high impedance is denser). As depth and density increase, less signal returns, making structures harder to clearly detect.

Based on the results of this experiment, it can be concluded that imaging clarity can be significantly impacted by tissue depth, density, and surrounding structures. As depth increases, defects become more challenging to identify, especially if the material is dense or near other internal structures.

These findings are important in real-world medical situations, particularly in maternal and neonatal healthcare, where prenatal imaging is used to monitor fetal development and detect potential complications. If congenital defects are located within thicker, denser tissue, they may be more difficult to clearly identify due to limitations in how signals travel.

Through this project, I learned that even in a model, depth and density can make it more difficult to clearly see internal structures. Overall, these findings highlight the importance of continued advancements in imaging technology. Improvements that allow clearer imaging in both light and ultrasound imaging through deeper and denser tissue could support earlier and more accurate detection of abnormalities, which may help improve medical decision-making, such as diagnosis and treatment planning.

In the future, I could expand this project by using different imaging techniques and increasing the number of trials to improve accuracy. To extend this project, I could also explore how different shapes and types of internal structures affect visibility. In addition, I would continue researching ultrasound and light imaging, their strengths and limitations, and how my results relate to real medical imaging.

I would like to thank my family for supporting me throughout this project and helping provide the materials needed to complete my experiment. I would also like to thank my teacher, Brandon Mackinnion, for offering guidance, feedback, and endless encouragement throughout this project, which helped improve my overall project and understanding. In addition, I am very appreciative of the Strait Region Science Fair members, Andrew Clarey and Shannon MacLennan, who supported my work and encouraged me throughout this process. Everyone's support has played such an important role in helping me succeed, thank you!

Research References:

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https://www.nibib.nih.gov/science-education/science-topics/optical-imaging?utm_source

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Pregnancy Archive. (2024, February 26). The Importance of Ultrasound during Pregnancy – What Every Expectant Parent Needs to Know! Retrieved March 19, 2026, from https://pregnancyarchive.com/blog/the-importance-of-ultrasound-during-pregnancy-what-every-expectant-parent-needs-to-know

Photo References:

Cherry Country Hospital and Clinic. (n.d.). Doctor performing a prenatal ultrasound. [Illustration]. Cherry Country Hospital and Clinic.

https://ssl.adam.com/content.aspx?productid=117&isarticlelink=false&pid=1&gid=003778&site=cherrycountyhospital.adam.com&login=CHER8093

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https://www.gastroclinix.com/blog/cancer/cancer-diagnosis/di-en/ultrasound/

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https://www.gastroclinix.com/blog/cancer/cancer-diagnosis/di-en/ultrasound/

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https://www.stroke-manual.com/quiz-physical-principles-of-ultrasound/

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https://www.sor.org/news/cpd/cpd-shorts-governance-for-sonographers

Kaiser Permanente. (n.d.). Labeled diagram of a fetus intrauterine. [Diagram]. Kaiser Permanente.

https://healthy.kaiserpermanente.org/health-wellness/health-encyclopedia/he.learning-about-fetal-ultrasound-results.abq4471

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https://www.researchgate.net/figure/Components-of-different-diagnostic-imaging-techniques_fig1_382692672