# Estimating Lumbar Spine Foraminal Disc Measurements Using Ultrasound and X-Ray Imaging Through Advanced Image Annotation, Processing, and Mathematical Modeling During Chiropractic Traction Procedures: A Feasibility Study

## TL;DR

This study explores a new method to measure spinal changes using X-ray and ultrasound during chiropractic procedures, aiming to improve diagnostic accuracy.

## Contribution

A novel methodology combining geometric scaling and extrapolation to align spinal metrics from X-ray and ultrasound imaging is introduced.

## Key findings

- Spinal metrics like height and area increased consistently under progressive traction weights.
- Extrapolation methods successfully standardized measurements across imaging modalities and conditions.
- The approach bridges static and real-time imaging data, offering potential for enhanced clinical assessments.

## Abstract

Accurate measurement of spinal metrics is critical for diagnosing and treating spinal disorders. However, discrepancies between X-ray and ultrasound imaging data pose a challenge in standardizing clinical assessments. This study introduces a novel methodology that combines geometric scaling factors and extrapolation techniques to align spinal metrics from X-ray and ultrasound modalities. Data were collected from fifteen healthy adult volunteers (8 males, 7 females) aged from early adulthood to middle age, all without a history of low back pain, who underwent a standardized chiropractic traction protocol. X-ray imaging was performed pre-procedure, and ultrasound imaging was conducted both pre-procedure and during the procedure at the L3–L4, L4–L5, and L5–S1 levels under graded traction forces (1.8 kg, 3.6 kg, 5.4 kg, and 11.3 kg). Extrapolation methods were applied to standardize measurements across pre- and during-procedure conditions. Significant findings include consistent increases in spinal metrics, such as height and area, indicating positive elongation and flexibility under progressive weights. The integration of these methods bridges the gap between static and real-time imaging data, potentially enhancing diagnostic accuracy and leads to clinical relevance. This proof-of-concept study lays the groundwork for developing standardized spinal imaging protocols and adapting the methodology to broader imaging applications for improved patient outcomes.