Cortical bone vessel identification and quantification on contrast-enhanced MR images

Abstract

Background: Cortical bone porosity is a major determinant of bone strength. Despite the biomechanical importance of cortical bone porosity, the biological drivers of cortical porosity are unknown. The content of cortical pore space can indicate pore expansion mechanisms; both of the primary components of pore space, vessels and adipocytes, have been implicated in pore expansion. Dynamic contrast-enhanced MRI (DCE-MRI) is widely used in vessel detection in cardiovascular studies, but has not been applied to visualize vessels within cortical bone. In this study, we have developed a multimodal DCE-MRI and high resolution peripheral QCT (HR-pQCT) acquisition and image processing pipeline to detect vessel-filled cortical bone pores. Methods: For this in vivo human study, 19 volunteers (10 males and 9 females; mean age =63±5) were recruited. Both distal and ultra-distal regions of the non-dominant tibia were imaged by HR-pQCT (82 µm nominal resolution) for bone structure segmentation and by 3T DCE-MRI (Gadavist; 9 min scan time; temporal resolution =30 sec; voxel size 230×230×500 µm 3) for vessel visualization. The DCE-MRI was registered to the HR-pQCT volume and the voxels within the MRI cortical bone region were extracted. Features of the DCE data were calculated and voxels were categorized by a 2-stage hierarchical kmeans clustering algorithm to determine which voxels represent vessels. Vessel volume fraction (volume ratio of vessels to cortical bone), vessel density (average vessel count per cortical bone volume), and average vessel volume (mean volume of vessels) were calculated to quantify the status of vessel-filled pores in cortical bone. To examine spatial resolution and perform validation, a virtual phantom with 5 channel sizes and an applied pseudo enhancement curve was processed through the proposed image processing pipeline. Overlap volume ratio and Dice coefficient was calculated to measure the similarity between the detected vessel map and ground truth. Results: In the human study, mean vessel volume fraction was 2.2%±1.0%, mean vessel density was 0.68±0.27 vessel/mm 3 , and mean average vessel volume was 0.032±0.012 mm 3 /vessel. Signal intensity for detected vessel voxels increased during the scan, while signal for non-vessel voxels within pores did not enhance. In the validation phantom, channels with diameter 250 µm or greater were detected successfully, with volume ratio equal to 1 and Dice coefficient above 0.6. Both statistics decreased dramatically for channel sizes less than 250 µm. Conclusions: We have a developed a multi-modal image acquisition and processing pipeline that successfully detects vessels within cortical bone pores. The performance of this technique degrades for vessel diameters below the in-plane spatial resolution of the DCE-MRI acquisition. This approach can be applied to investigate the biological systems associated with cortical pore expansion.

Meta-analysis of Diabetes Mellitus-Associated Differences in Bone Structure Assessed by High-Resolution Peripheral Quantitative Computed Tomography

“…Cortical porosity has a considerable influence on bone strength and is essential in fracture initiation and propagation [ 14 ]. Magnetic resonance imaging has linked increased cortical porosity to alterations of the tissue that occupies the pore space [ 43 ], including blood vessels. Accordingly, microvascular function has been linked to cortical porosity in patients with T2DM [ 44 •].…”

MR-based techniques for intracortical vessel visualization and characterization: understanding the impact of microvascular disease on skeletal health

“…Despite resolution limitations [42] intracortical vessels can be partly visualized in vivo in humans using clinical 3T MRI. A first feasibility study utilized DCE-MRI on a clinical 3T scanner to image intracortical vessels in the distal tibia of 19 volunteers (10 men, 9 women, mean age 63 ± 5 years) with low bone mass (T-scores -2.5 to -1.0) [33]. Using a vessel detection algorithm this study found a mean vessel volume of 0.032 mm 3 and a mean vessel density of 0.68 per mm 3 .…”

“…2). Therefore, a multimodal imaging protocol was developed using a cortical pore mask derived from high-resolution peripheral quantitative CT to analyze vessels and perfusion in the cortical pore space using DCE-MRI [33]. The MRI protocol utilized a spoiled gradient echo sequences with circular cartesian undersampling reconstruction to increase temporal resolution to 30 s. This imaging technique was able to resolve vessels with a diameter larger than 200 mm in a virtual phantom study.…”

Intra-skeletal vascular density in a bipedal hopping macropod with implications for analyses of rib histology

“…2, 3, 4; e.g. Shea et al 2002;Wu et al 2019). Bone tissue vascular canal density characterizes bone physiology and growth.…”

“…Because we were interested in mostly regional (sub-periosteal or outer cortical segment of bone) blood supply, we used a point count technique to investigate vessel 'occurrence' from which we estimated density per mm 2 (e.g. Prisby 2017; Shea et al 2002;Wu et al 2019;Viboolvorakul et al 2009). We targeted vessels that were longitudinal (i.e.…”