Enhancing Knee MR Image Clarity through Image Domain Super-Resolution Reconstruction.

This study introduces a hybrid analytical super-resolution (SR) pipeline aimed at enhancing the resolution of medical magnetic resonance imaging (MRI) scans. The primary objective is to overcome the limitations of clinical MRI resolution without the need for additional expensive hardware. The proposed pipeline involves three key steps: pre-processing to re-slice and register the image stacks; SR reconstruction to combine information from three orthogonal image stacks to generate a high-resolution image stack; and post-processing using an artefact reduction convolutional neural network (ARCNN) to reduce the block artefacts introduced during SR reconstruction. The workflow was validated on a dataset of six knee MRIs obtained at high resolution using various sequences. Quantitative analysis of the method revealed promising results, showing an average mean error of 1.40 ± 2.22% in voxel intensities between the SR denoised images and the original high-resolution images. Qualitatively, the method improved out-of-plane resolution while preserving in-plane image quality. The hybrid SR pipeline also displayed robustness across different MRI sequences, demonstrating potential for clinical application in orthopaedics and beyond. Although computationally intensive, this method offers a viable alternative to costly hardware upgrades and holds promise for improving diagnostic accuracy and generating more anatomically accurate models of the human body.

Cartilage thickness and bone shape variations as a function of sex, height, body mass, and age in young adult knees.

The functional relationship between bone and cartilage is modulated by mechanical factors. Scarce data exist on the relationship between bone shape and the spatial distribution of cartilage thickness. The aim of the study was to characterise the coupled variation in knee bone morphology and cartilage thickness distributions in knees with healthy cartilage and investigate this relationship as a function of sex, height, body mass, and age. MR images of 51 knees from young adults (28.4 ± 4.1 years) were obtained from a previous study and used to train a statistical shape model of the femur, tibia, and patella and their cartilages. Five multiple linear regression models were fitted to characterise morphology as a function of sex, height, body mass, and age. A logistic regression classifier was fitted to characterise morphological differences between males and females, and tenfold cross-validation was performed to evaluate the models' performance. Our results showed that cartilage thickness and its distribution were coupled to bone morphology. The first five shape modes captured over 90% of the variance and described coupled changes to the bone and spatial distribution of cartilage thickness. Mode 1 (size) was correlated to sex (p < 0.001) and height (p < 0.0001). Mode 2 (aspect ratio) was also correlated to sex (p = 0.006) and height (p = 0.017). Mode 4 (condylar depth) was correlated to sex only (p = 0.024). A logistic regression model trained on modes 1, 2, and 4 could classify sex with an accuracy of 92.2% (95% CI [81.1%, 97.8%]). No other modes were influenced by sex, height, body mass, or age. This study demonstrated the coupled relationship between bone and cartilage, showing that cartilage is thicker with increased bone size, diaphysis size, and decreased femoral skew. Our results show that sex and height influence bone shape and the spatial distribution of cartilage thickness in a healthy young adult population, but body mass and age do not.