Bone-Selective Magnetic Resonance Imaging as a Nonradiative Alternative to Computed Tomography for Cranial Vault Imaging: Concordance and Implementation of an Automated Segmentation Pipeline for Timely Image Processing

Abstract

BACKGROUND: Computed tomography (CT) is the clinical gold standard for high-resolution 3-dimensional (3D) visualization of cortical bone structures. However, CT ionizing radiation exposure is associated with the development of malignancy. Bone-selective magnetic resonance imaging (MRI) and bone-selective image reconstruction provide a radiation-free imaging modality with diagnostic and surgical planning uses. This technique, though applicable to many realms of plastic and reconstructive surgery, is of specific interest to craniofacial surgeons whose patients often require multiple pre- and postoperative CT scans, enduring a higher cumulative risk of malignancy. As it stands, the implementation of bone-selective MRI in clinical practice is prevented by a paucity of CT and bone-selective MRI concordance data and the time and labor intensive process required to produce bone-selective MR-based 3D skull segmentations. The manual segmentation process takes about 1.5 hours of time per MRI. Our study evaluates both the accuracy of a novel bone-selective MRI technique (dual-radiofrequency pulse, dual-echo, 3D ultrashort echo time) and the utility of a segmentation pipeline. OBJECTIVES: Part 1. Evaluate the concordance between MR-based and CT-based 3D skull renderings Part 2. Describe and evaluate a novel multiatlas segmentation pipeline DESIGN/METHODS: Part 1: A cadaver skull and the skulls of 5 healthy adult volunteers were scanned with bone-selective MR and thin-slice CT. Semi-automatic bone segmentation (1.5 hours/scan) was performed creating 3D renderings of the skulls. Mimics software was used to measure 8 anatomic distances from the 3D renderings. Lin’s Concordance Correlation test was applied to assess agreement between MR and CT-based 3D renderings. Part 2. CT and bone-selective MR images were acquired from 16 additional healthy adult volunteers, yielding 21 MR/CT pairs. The CT images were segmented using a semi-automated method to generate “ground truth” labels for the MR images. An automated multiatlas segmentation pipeline was then used to segment the 3D MR images using a 2-step process consisting of a training and segmentation. The training step develops an “atlas package,” which represents the varying anatomy from different subjects. The segmentation step uses the atlas package to generate segmentations for new subjects using several image registration steps. RESULTS: MR-based measurements differed from CT-based measurements by mean percent difference ranging from 2.3% to 5.0%. Lin’s Concordance Correlation ranged from 0.998 to 1.000. The segmentation pipeline took 10 minutes per segmentation with an average symmetric surface distance of 0.96 ± 0.15 mm between the manual reference segmentation and the corresponding automated segmentations. CONCLUSIONS: This study demonstrates high concordance between the gold standard (thin-slice CT) and our novel imaging modality as well as an 89% reduction in segmentation time. This technique is highly applicable to craniofacial surgery as well as cases involving extremity surgery, musculoskeletal trauma, and bone tumors. It additionally allows acquision of data of both soft and hard tissue structures from a single imaging modality with no radiation exposure. The demonstrated reduced segmentation time would allow bone-selective MRI to be used in clinical practice without a delay in treatment. We plan to investigate the accuracy of this technique as a tool for craniosynostosis diagnosis as well as in craniofacial virtual surgical planning.