Dataset Information

Average volume reference space for large scale registration of whole-body magnetic resonance images.

ABSTRACT: BACKGROUND AND OBJECTIVES:The construction of whole-body magnetic resonance (MR) imaging atlases allows to perform statistical analysis with applications in anomaly detection, longitudinal, and correlation studies. Atlas-based methods require a common coordinate system to which all the subjects are mapped through image registration. Optimisation of the reference space is an important aspect that affects the subsequent analysis of the registered data, and having a reference space that is neutral with respect to local tissue volume is valuable in correlation studies. The purpose of this work is to generate a reference space for whole-body imaging that has zero voxel-wise average volume change when mapped to a cohort. METHODS:This work proposes an approach to register multiple whole-body images to a common template using volume changes to generate a synthetic reference space, starting with an initial reference and refining it by warping it with a deformation that brings the voxel-wise average volume change associated to the mappings of all the images in the cohort to zero. RESULTS:Experiments on fat/water separated whole-body MR images show how the method effectively generates a reference space neutral with respect to volume changes, without reducing the quality of the registration nor introducing artefacts in the anatomy, while providing better alignment when compared to an implicit reference groupwise approach. CONCLUSIONS:The proposed method allows to quickly generate a reference space neutral with respect to local volume changes, that retains the registration quality of a sharp template, and that can be used for statistical analysis of voxel-wise correlations in large datasets of whole-body image data.

SUBMITTER: Pilia M

PROVIDER: S-EPMC6772040 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Average volume reference space for large scale registration of whole-body magnetic resonance images.

Pilia Martino M Kullberg Joel J Ahlström Håkan H Malmberg Filip F Ekström Simon S Strand Robin R

PloS one 20191001 10

<h4>Background and objectives</h4>The construction of whole-body magnetic resonance (MR) imaging atlases allows to perform statistical analysis with applications in anomaly detection, longitudinal, and correlation studies. Atlas-based methods require a common coordinate system to which all the subjects are mapped through image registration. Optimisation of the reference space is an important aspect that affects the subsequent analysis of the registered data, and having a reference space that is ...[more]

PMID: 31574093

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Project description:PURPOSE:Magnetic resonance imaging (MRI) provides noninvasive evaluation of patient's anatomy without using ionizing radiation. Due to this and the high soft-tissue contrast, MRI use has increased and has potential for use in longitudinal studies where changes in patients' anatomy or tumors at different time points are compared. Deformable image registration can be useful for these studies. Here, we describe two datasets that can be used to evaluate the registration accuracy of systems for MR images, as it cannot be assumed to be the same as that measured on CT images. ACQUISITION AND VALIDATION METHODS:Two sets of images were created to test registration accuracy. (a) A porcine phantom was created by placing ten 0.35-mm gold markers into porcine meat. The porcine phantom was immobilized in a plastic container with movable dividers. T1-weighted, T2-weighted, and CT images were acquired with the porcine phantom compressed in four different ways. The markers were not visible on the MR images, due to the selected voxel size, so they did not interfere with the measured registration accuracy, while the markers were visible on the CT images and were used to identify the known deformation between positions. (b) Synthetic images were created using 28 head and neck squamous cell carcinoma patients who had MR scans pre-, mid-, and postradiotherapy treatment. An inter- and intrapatient variation model was created using these patient scans. Four synthetic pretreatment images were created using the interpatient model, and four synthetic post-treatment images were created for each of the pretreatment images using the intrapatient model. DATA FORMAT AND USAGE NOTES:The T1-weighted, T2-weighted, and CT scans of the porcine phantom in the four positions are provided. Four T1-weighted synthetic pretreatment images each with four synthetic post-treatment images, and four T2-weighted synthetic pretreatment images each with four synthetic post-treatment images are provided. Additionally, the applied deformation vector fields to generate the synthetic post-treatment images are provided. The data are available on TCIA under the collection MRI-DIR. POTENTIAL APPLICATIONS:The proposed database provides two sets of images (one acquired and one computer generated) for use in evaluating deformable image registration accuracy. T1- and T2-weighted images are available for each technique as the different image contrast in the two types of images may impact the registration accuracy.

Project description:Transcranial brain stimulation (TBS) has been established as a method for modulating and mapping the function of the human brain, and as a potential treatment tool in several brain disorders. Typically, the stimulation is applied using a one-size-fits-all approach with predetermined locations for the electrodes, in electric stimulation (TES), or the coil, in magnetic stimulation (TMS), which disregards anatomical variability between individuals. However, the induced electric field distribution in the head largely depends on anatomical features implying the need for individually tailored stimulation protocols for focal dosing. This requires detailed models of the individual head anatomy, combined with electric field simulations, to find an optimal stimulation protocol for a given cortical target. Considering the anatomical and functional complexity of different brain disorders and pathologies, it is crucial to account for the anatomical variability in order to translate TBS from a research tool into a viable option for treatment. In this article we present a new method, called CHARM, for automated segmentation of fifteen different head tissues from magnetic resonance (MR) scans. The new method compares favorably to two freely available software tools on a five-tissue segmentation task, while obtaining reasonable segmentation accuracy over all fifteen tissues. The method automatically adapts to variability in the input scans and can thus be directly applied to clinical or research scans acquired with different scanners, sequences or settings. We show that an increase in automated segmentation accuracy results in a lower relative error in electric field simulations when compared to anatomical head models constructed from reference segmentations. However, also the improved segmentations and, by implication, the electric field simulations are affected by systematic artifacts in the input MR scans. As long as the artifacts are unaccounted for, this can lead to local simulation differences up to 30% of the peak field strength on reference simulations. Finally, we exemplarily demonstrate the effect of including all fifteen tissue classes in the field simulations against the standard approach of using only five tissue classes and show that for specific stimulation configurations the local differences can reach 10% of the peak field strength.

Project description:BackgroundMagnetic resonance imaging (MRI) is the gold standard for evaluating body composition. However, the reference ranges have not been established.MethodsThree lean tissue and seven adipose tissue parameters based on MRI data from the UK Biobank were used in this study. Participants with European ancestry and data on at least one parameter were screened. Age- and sex-specific percentile curves were generated using the lambda-mu-sigma method. Three levels of reference ranges were provided, which were equivalent to the mean ± 1 standard deviation (SD), 2 SDs and 2.5 SDs.ResultsThe final analysis set for each parameter ranged from 4842 to 14 148 participants (53.4%-56.6% women) with a median age of 61. For lean tissue parameters, compared with those at age 45, the median total lean tissue volume and total thigh fat-free muscle volume at age 70 were 2.83 and 1.73 L, and 3.02 and 1.51 L lower in men and women, respectively. The median weight-to-muscle ratios at age 45 were 0.51 and 0.83 kg/L lower compared with those at age 70 in men and women, respectively. Adipose tissue parameters showed inconsistent differences. In men, the median muscle fat infiltration, visceral adipose tissue (VAT) volume, total abdominal adipose tissue index and abdominal fat ratio were 1.48%, 0.32 L, 0.08 L/m2 and 0.4 higher, and the median abdominal subcutaneous adipose tissue (ASAT) volume and total adipose tissue volume were 0.47 and 0.41 L lower, respectively, at age 70 than at age 45. The median total trunk fat volume was approximately 9.53 L at all ages. In women, the median muscle fat infiltration and VAT volume were 1.68% and 0.76 L higher, respectively, at age 70 than at age 45. The median ASAT volume, total adipose tissue volume, total trunk fat volume, total abdominal adipose tissue index and abdominal fat ratio were 0.35 L, 0.78 L, 1.12 L, 0.49 L/m2 and 0.06 higher, respectively, at age 60 than at age 45. The medians of the former three parameters were 0.33 L, 0.14 L and 0.20 L lower, at age 70 than at age 60. The medians of the latter two parameters were approximately 3.64 L/m2 and 0.55 at ages between 60 and 70.ConclusionsWe have established reference ranges for MRI-measured body composition parameters in a large community-dwelling population. These findings provide a more accurate assessment of abnormal adipose and muscle conditions.

Project description:Magnetic resonance imaging (MRI) at ultra-high fields (UHF), such as 7 T, provides an enhanced signal-to-noise ratio and has led to unprecedented high-resolution anatomic images and brain activation maps. Although a variety of radio frequency (RF) coil architectures have been developed for imaging at UHF conditions, they usually are specialized for small volumes of interests (VoI). So far, whole-body coil resonators are not available for commercial UHF human whole-body MRI systems. The goal of the present study was the development and validation of a transmit and receive system for large VoIs that operates at a 7 T human whole-body MRI system. A Metamaterial Ring Antenna System (MRAS) consisting of several ring antennas was developed, since it allows for the imaging of extended VoIs. Furthermore, the MRAS not only requires lower intensities of the irradiated RF energy, but also provides a more confined and focused injection of excitation energy on selected body parts. The MRAS consisted of several antennas with 50 cm inner diameter, 10 cm width and 0.5 cm depth. The position of the rings was freely adjustable. Conformal resonant right-/left-handed metamaterial was used for each ring antenna with two quadrature feeding ports for RF power. The system was successfully implemented and demonstrated with both a silicone oil and a water-NaCl-isopropanol phantom as well as in vivo by acquiring whole-body images of a crab-eating macaque. The potential for future neuroimaging applications was demonstrated by the acquired high-resolution anatomic images of the macaque's head. Phantom and in vivo measurements of crab-eating macaques provided high-resolution images with large VoIs up to 40 cm in xy-direction and 45 cm in z-direction. The results of this work demonstrate the feasibility of the MRAS system for UHF MRI as proof of principle. The MRAS shows a substantial potential for MR imaging of larger volumes at 7 T UHF. This new technique may provide new diagnostic potential in spatially extended pathologies such as searching for spread-out tumor metastases or monitoring systemic inflammatory processes.

Dataset Information

Average volume reference space for large scale registration of whole-body magnetic resonance images.

Publications

Average volume reference space for large scale registration of whole-body magnetic resonance images.

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