Use Of Magnetic Resonance Imaging Measures Research Articles

Toward the use of MRI measurements of bound and pore water in fracture risk assessment.

The current clinical assessment of fracture risk lacks information about the inherent quality of a person's bone tissue. Working toward an imaging-based approach to quantify both a bone tissue quality marker (tissue hydration as water bound to the matrix) and a bone microstructure marker (porosity as water in pores), we hypothesized that the concentrations of bound water (Cbw) are lower and concentrations of pore water (Cpw) are higher in patients with osteoporosis (OP) than in age- and sex-matched adults without the disease. Using recent developments in ultrashort echo time (UTE) magnetic resonance imaging (MRI), maps of Cbw and Cpw were acquired from the uninjured distal third radius (Study 1) of 20 patients who experienced a fragility fracture of the distal radius (Fx) and 20 healthy controls (Non-Fx) and from the tibia mid-diaphysis (Study 2) of 30 women with clinical OP (low T-scores) and 15 women without OP (normal T-scores). In Study 1, Cbw was significantly lower (p=0.0018) and Cpw was higher (p=0.0022) in the Fx than in the Non-Fx group. In forward stepwise, logistic regression models using Bayesian Information Criterion for selecting the best set of predictors (from imaging parameters, age, BMI, and DXA scanner type), the area-under-the-receiver operator characteristics-curve (AUC with 95% confidence intervals) was 0.73 (0.56, 0.86) for hip aBMD (best predictors without MRI) and 0.86 (0.70, 0.95) for the combination of Cbw and Cpw (best predictors overall). In Study 2, Cbw was significantly lower (p=0.0005) in women with OP (23.8±4.3 1H mol/L) than in women without OP (29.9±6.4 1H mol/L); Cpw was significantly higher by estimate of 2.9 1H mol/L (p=0.0298) with clinical OP, but only when accounting for the type of UTE-MRI scan with 3D providing higher values than 2D (p<0.0001). Lastly, Cbw, but not Cpw, was sensitive to bone forming osteoporosis medications over 12-months. UTE-MRI-derived measurements of bound and pore water concentrations are potential, aBMD-independent predictors of fracture risk.

Diagnostic Value of Ultrasound in Children with Discoid Lateral Meniscus Using Either an Intracavitary Convex Array Probe or a Linear Array Probe

This prospective study aimed to assess the usefulness of an intracavitary convex array probe (ICAP) in visualizing the lateral meniscus (LM) and improving the diagnostic utility of ultrasound (US) when diagnosing or screening for discoid lateral meniscus (DLM) in children. We included 105 knees (66 patients) that had symptomatic or asymptomatic DLM. We extracted and retrospectively reviewed data regarding patient demographics, medical records, magnetic resonance imaging (MRI), ultrasonographic features and arthroscopic findings. The inner edge of the LM visualized using an ICAP was significantly clearer than that visualized using a linear array probe, and the difference was significant (p < 0.01). The edges were better visualized in patients aged <8 y than in those aged >8 y, and the difference was significant (p < 0.001). The average widths of the LM body using an ICAP and MRI were 19.85 ± 3.63 and 24.46 ± 4.94 mm, respectively, and the wider the meniscal width, the greater was the deviation between the US and MRI measurements, which were positively correlated (r = 0.612, p < 0.001). With the use of MRI measurements and an ICAP, meniscal widths in poorly visualized LMs were greater than those in clearly visualized LMs, but this difference was not significant (p = 0.161). US scans using an ICAP and MRI were highly consistent in assessing the shape of the menisci (κ = 0.849, p < 0.001). US scan using an ICAP is a non-invasive, convenient and low-cost modality for diagnosing or screening for DLM in the pediatric population, especially in children aged <8 y.

Segmentation of the Proximal Femur from MR Images using Deep Convolutional Neural Networks

Magnetic resonance imaging (MRI) has been proposed as a complimentary method to measure bone quality and assess fracture risk. However, manual segmentation of MR images of bone is time-consuming, limiting the use of MRI measurements in the clinical practice. The purpose of this paper is to present an automatic proximal femur segmentation method that is based on deep convolutional neural networks (CNNs). This study had institutional review board approval and written informed consent was obtained from all subjects. A dataset of volumetric structural MR images of the proximal femur from 86 subjects were manually-segmented by an expert. We performed experiments by training two different CNN architectures with multiple number of initial feature maps, layers and dilation rates, and tested their segmentation performance against the gold standard of manual segmentations using four-fold cross-validation. Automatic segmentation of the proximal femur using CNNs achieved a high dice similarity score of 0.95 ± 0.02 with precision = 0.95 ± 0.02, and recall = 0.95 ± 0.03. The high segmentation accuracy provided by CNNs has the potential to help bring the use of structural MRI measurements of bone quality into clinical practice for management of osteoporosis.