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Purpose: To develop and validate a method for performing inter-station intensity standardization in multispectral whole-body MR data., Methods: Different approaches for mapping the intensity of each acquired image stack into the reference intensity space were developed and validated. The registration strategies included: "direct" registration to the reference station (Strategy 1), "progressive" registration to the neighboring stations without (Strategy 2), and with (Strategy 3) using information from the overlap regions of the neighboring stations. For Strategy 3, two regularized modifications were proposed and validated. All methods were tested on two multispectral whole-body MR data sets: a multiple myeloma patients data set (48 subjects) and a whole-body MR angiography data set (33 subjects)., Results: For both data sets, all strategies showed significant improvement of intensity homogeneity with respect to vast majority of the validation measures (P < 0.005). Strategy 1 exhibited the best performance, closely followed by Strategy 2. Strategy 3 and its modifications were performing worse, in majority of the cases significantly (P < 0.05)., Conclusions: We propose several strategies for performing inter-station intensity standardization in multispectral whole-body MR data. All the strategies were successfully applied to two types of whole-body MR data, and the "direct" registration strategy was concluded to perform the best. Magn Reson Med 77:422-433, 2017. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine., (© 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc.)

In this work, we present a fully automated algorithm for extraction of the 3D arterial tree and labelling the tree segments from whole-body magnetic resonance angiography (WB-MRA) sequences. The algorithm developed consists of two core parts (i) 3D volume reconstruction from different stations with simultaneous correction of different types of intensity inhomogeneity, and (ii) Extraction of the arterial tree and subsequent labelling of the pruned extracted tree. Extraction of the arterial tree is performed using the probability map of the "contrast" class, which is obtained as one of the results of the inhomogeneity correction scheme. We demonstrate that such approach is more robust than using the difference between the pre- and post-contrast channels traditionally used for this purpose. Labelling the extracted tree is performed by using a combination of graph-based and atlas-based approaches. Validation of our method with respect to the extracted tree was performed on the arterial tree subdivided into 32 segments, 82.4% of which were completely detected, 11.7% partially detected, and 5.9% were missed on a cohort of 35 subjects. With respect to automated labelling accuracy of the 32 segments, various registration strategies were investigated on a training set consisting of 10 scans. Further analysis on the test set consisting of 25 data sets indicates that 69% of the vessel centerline tree in the head and neck region, 80% in the thorax and abdomen region, and 84% in the legs was accurately labelled to the correct vessel segment. These results indicate clinical potential of our approach in enabling fully automated and accurate analysis of the entire arterial tree. This is the first study that not only automatically extracts the WB-MRA arterial tree, but also labels the vessel tree segments., (Copyright © 2015. Published by Elsevier B.V.)

Purpose: To show the effect, efficiency, and image quality improvements achievable by Dual Refocusing Echo Acquisition Mode (DREAM)-based B1+ shimming in whole-body magnetic resonance imaging (MRI) at 3T using the example of water/fat imaging., Materials and Methods: 3D multistation, dual-echo mDixon gradient echo imaging was performed in 10 healthy subjects on a clinical 3T dual-transmit MRI system using station-to-station adapted B1+ shimming based on fast DREAM B1+ mapping. Whole-body data were obtained using conventional quadrature excitation and station-by-station adapted DREAM-based B1+ shimmed excitation, along with the corresponding B1+ maps for both excitation modes to assess image quality and radiofrequency (RF) performance., Results: Station-dependent DREAM-based B1+ shimming showed significantly improved image quality in the stations covering the upper legs, pelvis, and upper body region for all subjects (P < 0.02). This finding is supported by corresponding B1+ maps showing an improved B1+ homogeneity and a more precise flip angle in the DREAM-based B1+ shimmed excitation (P < 0.01). Furthermore, the very short dual-channel DREAM B1+ mapping times of less than 2 seconds facilitate quick B1+ shimming., Conclusion: Station-dependent DREAM-based B1+ shimming improved RF performance and image quality and is therefore a promising technique for whole-body multistation imaging applications., (© 2014 Wiley Periodicals, Inc.)

Purpose: Whole-body MRI is seeing increasing use in the study and diagnosis of disease progression. In this, a central task is the visual assessment of the progressive changes that occur between two whole-body MRI datasets, taken at baseline and follow-up. Current radiological workflow for this consists in manual search of each organ of interest on both scans, usually on multiple data channels, for further visual comparison. Large size of datasets, significant posture differences, and changes in patient anatomy turn manual matching in an extremely labor-intensive task that requires from radiologists high concentration for long period of time. This strongly limits the productivity and increases risk of underdiagnosis., Materials and Methods: We present a novel approach to the comparative visual analysis of whole-body MRI follow-up data. Our method is based on interactive derivation of locally rigid transforms from a pre-computed whole-body deformable registration. Using this approach, baseline and follow-up slices can be interactively matched with a single mouse click in the anatomical region of interest. In addition to the synchronized side-by-side baseline and matched follow-up slices, we have integrated four techniques to further facilitate the visual comparison of the two datasets: the "deformation sphere", the color fusion view, the magic lens, and a set of uncertainty iso-contours around the current region of interest., Results: We have applied our method to the study of cancerous bone lesions over time in patients with Kahler's disease. During these studies, the radiologist carefully visually examines a large number of anatomical sites for changes. Our interactive locally rigid matching approach was found helpful in localization of cancerous lesions and visual assessment of changes between different scans. Furthermore, each of the features integrated in our software was separately evaluated by the experts., Conclusion: We demonstrated how our method significantly facilitates examination of whole-body MR datasets in follow-up studies by enabling the rapid interactive matching of regions of interest and by the explicit visualization of change.

Whole-body MR receives increasing interest as potential alternative to many conventional diagnostic methods. Typical whole-body MR scans contain multiple data channels and are acquired in a multistation manner. Quantification of such data typically requires correction of two types of artefacts: different intensity scaling on each acquired image stack, and intensity inhomogeneity (bias) within each stack. In this work, we present an all-in-one method that is able to correct for both mentioned types of acquisition artefacts. The most important properties of our method are: 1) All the processing is performed jointly on all available data channels, which is necessary for preserving the relation between them, and 2) It allows easy incorporation of additional knowledge for estimation of the bias field. Performed validation on two types of whole-body MR data confirmed superior performance of our approach in comparison with state-of-the-art bias removal methods.