Multiple-echo steady-state (MESS): Extending DESS for joint T2 mapping and chemical-shift corrected water-fat separation

PURPOSE: To extend the double echo steady-state (DESS) sequence to enable chemical-shift corrected water-fat separation. METHODS: This study proposes multiple-echo steady-state (MESS), a sequence that modifies the readouts of the DESS sequence to acquire two echoes each with bipolar readout gradients with higher readout bandwidth. This enables water-fat separation and eliminates the need for water-selective excitation that is often used in combination with DESS, without increasing scan time. An iterative fitting approach was used to perform joint chemical-shift corrected water-fat separation and T2 estimation on all four MESS echoes simultaneously. MESS and water-selective DESS images were acquired for five volunteers, and were compared qualitatively as well as quantitatively on cartilage T2 and thickness measurements. Signal-to-noise ratio (SNR) and T2 quantification were evaluated numerically using pseudo-replications of the acquisition. RESULTS: The water-fat separation provided by MESS was robust and with quality comparable to water-selective DESS. MESS T2 estimation was similar to DESS, albeit with slightly higher variability. Noise analysis showed that SNR in MESS was comparable to DESS on average, but did exhibit local variations caused by uncertainty in the water-fat separation. CONCLUSION: In the same acquisition time as DESS, MESS provides water-fat separation with comparable SNR in the reconstructed water and fat images. By providing additional image contrasts in addition to the water-selective DESS images, MESS provides a promising alternative to DESS

Automatic generation of subject-specific finite element models of the spine from magnetic resonance images

The generation of subject-specific finite element models of the spine is generally a time-consuming process based on computed tomography (CT) images, where scanning exposes subjects to harmful radiation. In this study, a method is presented for the automatic generation of spine finite element models using images from a single magnetic resonance (MR) sequence. The thoracic and lumbar spine of eight adult volunteers was imaged using a 3D multi-echogradient-echo sagittal MR sequence. A deep-learning method was used to generate synthetic CT images from the MR images. A pre-trained deeplearning network was used for the automatic segmentation of vertebrae from the synthetic CT images. Another deep-learning network was trained for the automatic segmentation of intervertebral discs from the MR images. The automatic segmentations were validated against manual segmentations for two subjects, one with scoliosis, and another with a spine implant. A template mesh of the spine was registered to the segmentations in three steps using a Bayesian coherent point drift algorithm. First, rigid registration was applied on the complete spine. Second, non-rigid registration was used for the individual discs and vertebrae. Third, the complete spine was non-rigidly registered to the individually registered discs and vertebrae. Comparison of the automatic and manual segmentations led to dice-scores of 0.93–0.96 for all vertebrae and discs. The lowest dice-score was in the disc at the height of the implant where artifacts led to under-segmentation. The mean distance between the morphed meshes and the segmentations was below 1 mm. In conclusion, the presented method can be used to automatically generate accurate subject-specific spine models

The three-dimensional coupling mechanism in scoliosis and its consequences for correction

Introduction: In idiopathic scoliosis, the anterior spinal column has rotated away from the midline and has become longer through unloading and expansion of the intervertebral discs. Theoretically, extension of the spine in the sagittal plane should provide room for this longer anterior spinal column, allowing it to swing back towards the midline in the coronal and axial plane, thus reducing both the Cobb angle and the apical vertebral rotation. Methods: In this prospective experimental study, ten patients with primary thoracic adolescent idiopathic scoliosis (AIS) underwent MRI (BoneMRI and cVISTA sequences) in supine as well as in an extended position by placing a broad bolster, supporting both hemi-thoraces, under the scoliotic apex. Differences in T4–T12 kyphosis angle, coronal Cobb angle, vertebral rotation, as well as shape of the intervertebral disc and shape and position of the nucleus pulposus, were analysed and compared between the two positions. Results: Extension reduced T4–T12 thoracic kyphosis by 10° (p < 0.001), the coronal Cobb angle decreased by 9° (p < 0.001) and vertebral rotation by 4° (p = 0.036). The coronal wedge shape of the disc significantly normalized and the wedged and lateralized nucleus pulposus partially reduced to a more symmetrical position. Conclusion: Simple extension of the scoliotic spine leads to a reduction of the deformity in the coronal and axial plane. The shape of the disc normalizes and the eccentric nucleus pulposus partially moves back to the midline

Holmium Nanoparticles: Preparation and In Vitro Characterization of a New Device for Radioablation of Solid Malignancies

# The Author(s) 2010. This article is published with open access at Springerlink.com Purpose The present study introduces the preparation and in vitro characterization of a nanoparticle device comprising holmium acetylacetonate for radioablation of unresectable solid malignancies. Methods HoAcAc nanoparticles were prepared by dissolving holmium acetylacetonate in chloroform, followed by emulsification in an aqueous solution of a surfactant and evaporation of W. Bult: R. Varkevisser: P. R. Luijten: A. D. van het Schip

Automatic Assessment of Lower-Limb Alignment from Computed Tomography

BACKGROUND: Preoperative planning of lower-limb realignment surgical procedures necessitates the quantification of alignment parameters by using landmarks placed on medical scans. Conventionally, alignment measurements are performed on 2-dimensional (2D) standing radiographs. To enable fast and accurate 3-dimensional (3D) planning of orthopaedic surgery, automatic calculation of the lower-limb alignment from 3D bone models is required. The goal of this study was to develop, validate, and apply a method that automatically quantifies the parameters defining lower-limb alignment from computed tomographic (CT) scans. METHODS: CT scans of the lower extremities of 50 subjects were both manually and automatically segmented. Thirty-two manual landmarks were positioned twice on the bone segmentations to assess intraobserver reliability in a subset of 20 subjects. The landmarks were also positioned automatically using a shape-fitting algorithm. The landmarks were then used to calculate 25 angles describing the lower-limb alignment for all 50 subjects. RESULTS: The mean absolute difference (and standard deviation) between repeat measurements using the manual method was 2.01 ± 1.64 mm for the landmark positions and 1.05° ± 1.48° for the landmark angles, whereas the mean absolute difference between the manual and fully automatic methods was 2.17 ± 1.37 mm for the landmark positions and 1.10° ± 1.16° for the landmark angles. The manual method required approximately 60 minutes of manual interaction, compared with 12 minutes of computation time for the fully automatic method. The intraclass correlation coefficient showed good to excellent reliability between the manual and automatic assessments for 23 of 25 angles, and the same was true for the intraobserver reliability in the manual method. The mean for the 50 subjects was within the expected range for 18 of the 25 automatically calculated angles. CONCLUSIONS: We developed a method that automatically calculated a comprehensive range of 25 measurements that defined lower-limb alignment in considerably less time, and with differences relative to the manual method that were comparable to the differences between repeated manual assessments. This method could thus be used as an efficient alternative to manual assessment of alignment. LEVEL OF EVIDENCE: Diagnostic Level III . See Instructions for Authors for a complete description of levels of evidence

Automatic generation of subject-specific finite element models of the spine from magnetic resonance images

The generation of subject-specific finite element models of the spine is generally a time-consuming process based on computed tomography (CT) images, where scanning exposes subjects to harmful radiation. In this study, a method is presented for the automatic generation of spine finite element models using images from a single magnetic resonance (MR) sequence. The thoracic and lumbar spine of eight adult volunteers was imaged using a 3D multi-echo-gradient-echo sagittal MR sequence. A deep-learning method was used to generate synthetic CT images from the MR images. A pre-trained deep-learning network was used for the automatic segmentation of vertebrae from the synthetic CT images. Another deep-learning network was trained for the automatic segmentation of intervertebral discs from the MR images. The automatic segmentations were validated against manual segmentations for two subjects, one with scoliosis, and another with a spine implant. A template mesh of the spine was registered to the segmentations in three steps using a Bayesian coherent point drift algorithm. First, rigid registration was applied on the complete spine. Second, non-rigid registration was used for the individual discs and vertebrae. Third, the complete spine was non-rigidly registered to the individually registered discs and vertebrae. Comparison of the automatic and manual segmentations led to dice-scores of 0.93–0.96 for all vertebrae and discs. The lowest dice-score was in the disc at the height of the implant where artifacts led to under-segmentation. The mean distance between the morphed meshes and the segmentations was below 1 mm. In conclusion, the presented method can be used to automatically generate accurate subject-specific spine models

Deep learning-enabled MRI-only photon and proton therapy treatment planning for paediatric abdominal tumours

Purpose: To assess the feasibility of magnetic resonance imaging (MRI)-only treatment planning for photon and proton radiotherapy in children with abdominal tumours. Materials and methods: The study was conducted on 66 paediatric patients with Wilms' tumour or neuroblastoma (age 4 +/- 2 years) who underwent MR and computed tomography (CT) acquisition on the same day as part of the clinical protocol. MRI intensities were converted to CT Hounsfield units (HU) by means of a UNet-like neural network trained to generate synthetic CT (sCT) from T1- and T2-weighted MR images. The CT-to-sCT image similarity was evaluated by computing the mean error (ME), mean absolute error (MAE), peak signal-to-noise ratio (PSNR) and Dice similarity coefficient (DSC). Synthetic CT dosimetric accuracy was verified against CT-based dose distributions for volumetric-modulated arc therapy (VMAT) and intensity-modulated pencil-beam scanning (PBS). Relative dose differences (D-diff) in the internal target volume and organs-at-risk were computed and a three-dimensional gamma analysis (2 mm, 2%) was performed. Results: The average +/- standard deviation ME was -5 +/- 12 HU, MAE was 57 +/- 12 HU, PSNR was 30.3 +/- 1. 6 dB and DSC was 76 +/- 8% for bones and 92 +/- 9% for lungs. Average D-diff were 99% (range [85; 100]%) for VMAT and >96% (range [87; 100]%) for PBS. Conclusion: The deep learning-based model generated accurate sCT from planning T1w- and T2w-MR images. Most dosimetric differences were within clinically acceptable criteria for photon and proton radiotherapy, demonstrating the feasibility of an MRI-only workflow for paediatric patients with abdominal tumours. (C) 2020 The Authors. Published by Elsevier B.V

3D-printed saw guides for lower arm osteotomy, a comparison between a synthetic CT and CT-based workflow

BACKGROUND: Three-dimensional (3D)-printed saw guides are frequently used to optimize osteotomy results and are usually designed based on computed tomography (CT), despite the radiation burden, as radiation-less alternatives like magnetic resonance imaging (MRI) have inferior bone visualization capabilities. This study investigated the usability of MR-based synthetic-CT (sCT), a novel radiation-less bone visualization technique for 3D planning and design of patient-specific saw guides.METHODS: Eight human cadaveric lower arms (mean age: 78y) received MRI and CT scans as well as high-resolution micro-CT. From the MRI scans, sCT were generated using a conditional generative adversarial network. Digital 3D bone surface models based on the sCT and general CT were compared to the surface model from the micro-CT that was used as ground truth for image resolution. From both the sCT and CT digital bone models saw guides were designed and 3D-printed in nylon for one proximal and one distal bone position for each radius and ulna. Six blinded observers placed these saw guides as accurately as possible on dissected bones. The position of each guide was assessed by optical 3D-scanning of each bone with positioned saw guide and compared to the preplanning. Eight placement errors were evaluated: three translational errors (along each axis), three rotational errors (around each axis), a total translation (∆T) and a total rotation error (∆R).RESULTS: Surface models derived from micro-CT were on average smaller than sCT and CT-based models with average differences of 0.27 ± 0.30 mm for sCT and 0.24 ± 0.12 mm for CT. No statistically significant positioning differences on the bones were found between sCT- and CT-based saw guides for any axis specific translational or rotational errors nor between the ∆T (p = .284) and ∆R (p = .216). On Bland-Altman plots, the ∆T and ∆R limits of agreement (LoA) were within the inter-observer variability LoA.CONCLUSIONS: This research showed a similar error for sCT and CT digital surface models when comparing to ground truth micro-CT models. Additionally, the saw guide study showed equivalent CT- and sCT-based saw guide placement errors. Therefore, MRI-based synthetic CT is a promising radiation-less alternative to CT for the creation of patient-specific osteotomy surgical saw guides.</p