Wireless vs. Traditional Ultrasound Assessed Knee Cartilage Outcomes Utilizing Automated Gain and Normalization Techniques

Arjun Parmar; Corey D Grozier; Robert Dima; Jessica E Tolzman; Ilker Hacihaliloglu; Kenneth L Cameron; Ryan Fajardo; Matthew S Harkey

arXiv · 2405.12172

Wireless vs. Traditional Ultrasound Assessed Knee Cartilage Outcomes Utilizing Automated Gain and Normalization Techniques

Arjun Parmar·Corey D Grozier·Robert Dima·Jessica E Tolzman·Ilker Hacihaliloglu·Kenneth L Cameron·Ryan Fajardo·Matthew S Harkey

View original source ↗Read source document ↗Export metadata ↓

Abstract

Advancements in wireless ultrasound technology allow for point of care cartilage imaging, yet validation against traditional ultrasound units remains to be established for knee cartilage outcomes. Therefore, the purpose of our study was to establish the agreement of articular cartilage thickness and echo-intensity measures between traditional and wireless ultrasound units utilizing automatic-gain and normalization techniques. We used traditional and wireless ultrasound to assess the femoral cartilage via transverse suprapatellar scans with the knee in maximum flexion in 71 female NCAA Division I athletes (age: 20.0$\pm$1.3 years, height: 171.7$\pm$8.7 cm, mass: 69.4$\pm$11.0 kg). Wireless ultrasound images (auto-gain and standard gain) were compared to traditional ultrasound images (standard gain) before and after normalization. Ultrasound image pixel values were algebraically scaled to normalize imaging parameter differences between units. Mean thickness and echo-intensity of the global and sub-regions of interest were measured for unnormalized and normalized images. Intraclass correlation coefficients ($ICC_{2,k}$) for absolute agreement, standard error of the measurement, and minimum detectable difference were calculated between the traditional and wireless ultrasound units across both gain parameters and normalization. Cartilage thickness demonstrated good to excellent agreement for all regions ($ICC_{2,k} = 0.83 {\text -} 0.95$) regardless of gain and normalization. However, mean echo-intensity demonstrated poor to moderate agreement in all regions regardless of gain and normalization ($ICC_{2,k} = 0.23 {\text -} 0.68 $). While there was a high level of agreement between units when assessing cartilage thickness, further research in ultrasound beam forming may lead to improvements in agreement of cartilage echo-intensity between ultrasound units.

Explore related subjects

eess.IV

Keep this discovery

BibTeX RIS

Arjun Parmar, Corey D Grozier, Robert Dima, Jessica E Tolzman, Ilker Hacihaliloglu, Kenneth L Cameron, Ryan Fajardo, Matthew S Harkey. 2024-05-20. Wireless vs. Traditional Ultrasound Assessed Knee Cartilage Outcomes Utilizing Automated Gain and Normalization Techniques. https://arxiv.org/abs/2405.12172

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Exponential Pixelating Integral transform with dual fractal features for enhanced chest X-ray abnormality detection

The heightened prevalence of respiratory disorders, particularly exacerbated by a significant upswing in fatalities due to the novel coronavirus, underscores the critical need for early detection and timely intervention. This imperative is paramount, possessing the potential to profoundly impact and safeguard numerous lives. Medically, chest radiography stands out as an essential and economically viable medical imaging approach for diagnosing and assessing the severity of diverse Respiratory Disorders. However, their detection in Chest X-Rays is a cumbersome task even for well-trained radiologists owing to low contrast issues, overlapping of the tissue structures, subjective variability, and the presence of noise. To address these issues, a novel analytical model termed Exponential Pixelating Integral is introduced for the automatic detection of infections in Chest X-Rays in this work. Initially, the presented Exponential Pixelating Integral enhances the pixel intensities to overcome the low-contrast issues that are then polar-transformed followed by their representation using the locally invariant Mandelbrot and Julia fractal geometries for effective distinction of structural features. The collated features labeled Exponential Pixelating Integral with dually characterized fractal features are then classified by the non-parametric multivariate adaptive regression splines to establish an ensemble model between each pair of classes for effective diagnosis of diverse diseases. Rigorous analysis of the proposed classification framework on large medical benchmarked datasets showcases its superiority over its peers by registering a higher classification accuracy and F1 scores ranging from 98.46 to 99.45% and 96.53-98.10% respectively, making it a precise and interpretable automated system for diagnosing respiratory disorders.

eess.IV↗

arXiv2026

Myocardial Strain Drift Correction in Deep Learning Based Ultrasound Tracking

Myocardial strain from echocardiography is a key biomarker for cardiac function. Recent deep learning methods show strong performance for myocardial motion tracking but often lack physiological constraints, leading to temporal drift across the cardiac cycle. Consequently, tracked points may not return to their relative initial positions at the end of each cardiac cycle, producing inaccurate strain estimates and even divergence in some cases. We propose a deep learning framework that compensates for drift during myocardial tracking. We extend a state-of-the-art echocardiographic tracking method (TAS-Net) with persistent memory tokens that share information across sliding windows over full cardiac cycles. A teacher-student fine-tuning strategy on real echocardiographic data then enforces physiologically consistent cyclic motion while preserving tracking accuracy. Experiments show reduced global and regional strain drift, improved agreement with clinical references, and better test-retest reproducibility, supporting more reliable myocardial strain estimation in clinical practice.

eess.IV↗

arXiv2026

Morphological Decoupling-Based Skeletal Classification for Clinical Assessment of Malocclusion

Malocclusion skeletal grading is a fundamental task in orthodontics, critical for diagnosis and treatment planning. Traditionally, cone-beam computed tomography (CBCT) is used for visual measurement, and the reconstructed lateral cephalograms are handed over to expert dentists for diagnosis. However, manual review is time-consuming, labor-intensive, and subject to inter-operator variability. Therefore, an automatic CBCT-based system is needed for reliable malocclusion skeletal grading. In this case, we develop TeethGNN, a novel graph-based framework designed to combine CBCT image features with morphological information for accurate and efficient malocclusion grading. TeethGNN utilizes a decoupled learnable decoder to directly predict key morphological indicators from CBCT images, eliminating the need for manual measurements. These morphological features are then fused with image features using a graph neural network (GNN), which effectively models the relationships between the modalities. To further enhance robustness and calibration, we introduce a collaborative calibration strategy. This strategy combines multi-scale graph adversarial perturbation for explicit calibration and nonlinear topological graph calibration for implicit confidence adjustment. Extensive experiments and ablation studies on our collected clinical dataset demonstrate that our malocclusion measurement system achieves 77.08\% in accuracy and 89.61\% in AUC, outperforming the compared state-of-the-art methods. These results validate the effectiveness of graph-based multimodal fusion and collaborative calibration in improving malocclusion grading performance. Our system shows strong potential for advancing computer-aided orthodontic diagnosis, providing an accurate and reliable solution for vision-based clinical measurement and diagnosis.

eess.IV↗