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The challenge of bias-free coil combination for quantitative susceptibility mapping at ultra-high field

MAGNETIC RESONANCE IN MEDICINE 2018年第1期79卷 97-107页

作者： Bollmann, Steffen Robinson, Simon Daniel O'Brien, Kieran Vegh, Viktor Janke, Andrew Marstaller, Lars Reutens, David Barth, Markus Univ Queensland Ctr Adv Imaging Bldg 57Res Rd Brisbane Qld 4072 Australia Med Univ Vienna Dept Biomed Imaging & Image Guided Therapy High Field Magnet Resonance Ctr Vienna Austria Siemens Healthcare Pty Ltd Brisbane Qld Australia

PurposeQuantitative susceptibility mapping is a technique to estimate the magnetic property of tissue with particularly high sensitivity at ultra-high field. However, a key challenge at ultra-high field is the combination of phase data acquired using phased array receive coils. Several methods for combining phase data have been proposed, but the influence of coil combination choices on susceptibility quantitation has not been studied systematically. MethodsWe combined phase data using COMPOSER (COMbining Phase data using a Short Echo-time Reference scan) and a reference-free channel-by-channel method. We investigated the effect of the chosen combination method on susceptibility results in a group of 28 participants at 7 Tesla. ResultsOur results show that reference scans can bias susceptibility values. Although the proposed reference-free channel-by-channel method cannot remove transmit field phase, it shows comparable results to the COMPOSER method in which a high-resolution ultrashort echo-time reference scan was used. ConclusionsWe conclude that ultrashort echo-time reference scans reduce quantitation bias and remove the transmit field phase when using COMPOSER to combine phase data, and not combining the phase data before susceptibility processing avoids this bias, resulting in comparable results. Magn Reson Med 79:97-107, 2018. (c) 2017 InternationalSociety for Magnetic Resonance in Medicine.

关键词： COMPOSER 3D EPI ultra-high field QSM PETRA atlas-based segmentation

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Automatic quantification of bone marrow edema on MRI of the wrist in patients with early arthritis: A feasibility study

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MAGNETIC RESONANCE IN MEDICINE 2018年第2期79卷 1127-1134页

作者： Aizenberg, Evgeni Roex, Edgar A. H. Nieuwenhuis, Wouter P. Mangnus, Lukas van der Helm-van Mil, Annette H. M. Reijnierse, Monique Bloem, Johan L. Lelieveldt, Boudewijn P. F. Stoel, Berend C. Leiden Univ Dept Radiol Med Ctr POB 9600 NL-2300 RC Leiden Netherlands Delft Univ Technol Dept Biomech Engn Delft Netherlands Leiden Univ Dept Rheumatol Med Ctr Leiden Netherlands Delft Univ Technol Intelligent Syst Dept Delft Netherlands

PurposeTo investigate the feasibility of automatic quantification of bone marrow edema (BME) on MRI of the wrist in patients with early arthritis. MethodsFor 485 early arthritis patients (clinically confirmed arthritis of one or more joints, symptoms for less than 2 years), MR scans of the wrist were processed in three automatic stages. First, super-resolution reconstruction was applied to fuse coronal and axial scans into a single high-resolution 3D image. Next, the carpal bones were located and delineated using atlas-based segmentation. Finally, the extent of BME within each bone was quantified by identifying image intensity values characteristic of BME by fuzzy clustering and measuring the fraction of voxels with these characteristic intensities within each bone. Correlation with visual BME scores was assessed through Pearson correlation coefficient. ResultsPearson correlation between quantitative and visual BME scores across 485 patients was r=0.83, P<0.001. ConclusionsQuantitative measurement of BME on MRI of the wrist has the potential to provide a feasible alternative to visual scoring. Complete automation requires automatic detection and compensation of acquisition artifacts. Magn Reson Med 79:1127-1134, 2018. (c) 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

关键词： rheumatoid arthritis bone marrow edema inflammation atlas-based segmentation superresolution reconstruction

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Combining a Patch-based Approach with a Non-rigid Registration-based Label Fusion Method for the Hippocampal segmentation in Alzheimer's Disease

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NEUROINFORMATICS 2017年第2期15卷 165-183页

作者： Platero, Carlos Carmen Tobar, M. Tech Univ Madrid Hlth Sci Technol Grp Ronda Valencia 3 Madrid 28012 Spain

We provide and evaluate an open-source software solution for automatically hippocampal segmentation from T1-weighted (T1w) magnetic resonance imaging (MRI). The method is applied for measuring the hippocampal volume, which allows discriminate between patients with Alzheimer's disease (AD) or mild cognitive impairment (MCI) and elderly controls (NC). The method is based on a fast patch-based label fusion method, whose selected patches and their weights are calculated from a combination of similarity measures between patches using intensity-based distances and labeling-based distances. These combined similarity measures produces better selection of the patches, and their weights are more robust. The algorithm is trained with the Harmonized Hippocampal Protocol (HarP). The proposal is compared with FreeSurfer and other label fusion methods. To evaluate the performance and the robustness of the proposed label fusion method, we employ two databases of T1w MRI of human brains. For AD vs NC, we obtain a high degree of accuracy, approximately 90 %. For MCI vs NC, we obtain accuracies around 75 %. The average time for the hippocampal segmentation from a T1w MRI is less than 17 minutes.

关键词： atlas-based segmentation Image registration Patch-based label fusion Hippocampal segmentation Magnetic resonance imaging

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atlas-based segmentation for globus pallidus internus targeting on low-resolution MRI

Atlas-based segmentation for globus pallidus internus target...

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33rd Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBS)

作者： Iacono, Maria I. Makris, Nikos Mainardi, Luca Gale, John van der Kouwe, Andre Mareyam, Azma Polimeni, Jonathan R. Wald, Lawrence L. Fischl, Bruce Eskandar, Emad N. Bonmassar, Giorgio MGH Dept Radiol AA Martinos Ctr Biomed Imaging Charlestown MA USA Politecn Milan Biomed Engn Dept Milan Italy Cleveland Clin Ctr Neurol Restorat Dept Nerosci Cleveland OH USA Massachusetts Gen Hosp Neurosurg Boston MA USA

ISBN: (纸本)9781424441228

In this paper we report a method to automatically segment the internal part of globus pallidus (GPi) on the preoperative low-resolution magnetic resonance images (MRIs) of patients affected by Parkinson's disease. Herein we used an ultra-high resolution human brain dataset as electronic atlas of reference on which we segmented the GPi. First, we registered the ultra-high resolution dataset on the low-resolution dataset using a landmarks-based rigid registration. Then an affine and a non-rigid surface-based registration guided by the structures that surround the target was applied in order to propagate the labels of the GPi on the low-resolution un-segmented dataset and to accurately outline the target. The mapping of the atlas on the low-resolution MRI provided a highly accurate anatomical detail that can be useful for localizing the target.

关键词： Accuracy atlas-based segmentation Image resolution Image segmentation Magnetic resonance imaging Manuals Parkinson disease Spline Three dimensional displays biomedical MRI brain brain diseases globus pallidus internus image registration image resolution i

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Active deformation fields: Dense deformation field estimation for atlas-based segmentation using the active contour framework

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MEDICAL IMAGE ANALYSIS 2011年第6期15卷 787-800页

作者： Gorthi, Subrahmanyam Duay, Valerie Bresson, Xavier Cuadra, Meritxell Bach Castro, F. Javier Sanchez Pollo, Claudio Allal, Abdelkarim S. Thiran, Jean-Philippe Ecole Polytech Fed Lausanne Signal Proc Lab LTSS CH-1015 Lausanne Switzerland Univ Appl Sci Western Switzerland HES SO Geneve Inst Bioengn Geneva Switzerland Univ Calif Los Angeles Dept Math Los Angeles CA 90024 USA Philips Healthcare Medisys Res Lab Suresnes France CHU Vaudois Dept Neurosurg CH-1011 Lausanne Switzerland Hop Fribourgeois Serv Radiooncol Fribourg Switzerland

This paper presents a new and original variational framework for atlas-based segmentation. The proposed framework integrates both the active contour framework, and the dense deformation fields of optical flow framework. This framework is quite general and encompasses many of the state-of-the-art atlas-based segmentation methods. It also allows to perform the registration of atlas and target images based on only selected structures of interest. The versatility and potentiality of the proposed framework are demonstrated by presenting three diverse applications: In the first application, we show how the proposed framework can be used to simulate the growth of inconsistent structures like a tumor in an atlas. In the second application, we estimate the position of nonvisible brain structures based on the surrounding structures and validate the results by comparing with other methods. In the final application, we present the segmentation of lymph nodes in the Head and Neck CT images, and demonstrate how multiple registration forces can be used in this framework in an hierarchical manner. (C) 2011 Elsevier B.V. All rights reserved.

关键词： atlas-based segmentation Joint registration and segmentation model Hierarchical atlas registration Active contours Level net representation

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Evaluating and improving label fusion in atlas-based segmentation using the surface distance

Evaluating and improving label fusion in atlas-based segment...

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Conference on Medical Imaging 2011 - Image Processing

作者： Langerak, T. R. van der Heide, U. A. Kotte, A. N. T. J. Berendsen, F. F. Pluim, J. P. W. Univ Med Ctr Utrecht Image Sci Inst POB 85500 NL-3508 GA Utrecht Netherlands Univ Med Ctr Utrecht Dept Radiotherapy NL-3508 GA Utrecht Netherlands

ISBN: (纸本)9780819485045

atlas-based segmentation is an increasingly popular method of automatically computing a segmentation. In the past, results of atlas-based segmentation have been evaluated using a volume overlap measure such as the Dice or Jaccard coefficients. However, in the first part of this paper we will argue and show that volume overlap measures are insensitive to local deviations. As a result, a segmentation that is judged to be of good quality when using such a measure may have large local deviations that may be problematic in clinical practice. In this paper, two versions of the surface distance are proposed as an alternative measure to evaluate the results of atlas-based segmentation, as they give more local information and therefore allow the detection of large local deviations. In most current atlas-based segmentation methods, the results of multiple atlases are combined to a single segmentation in a process called 'label fusion'. In a label fusion process it is important that segmentations with a high quality can be distinguished from those with a low quality. In the second part of the paper we will use the surface distance as a similarity measure during label fusion. We will present a modified version of the previously proposed SIMPLE algorithm, which selects propagated atlas segmentations based on their similarity with a preliminary estimate of the ground truth segmentation. The SIMPLE algorithm previously used the Dice coefficient as a similarity measure and in this paper we demonstrate that, using the spatial distance map instead, the results of atlas-based segmentation significantly improve.

关键词： atlas-based segmentation surface distance image similarity volume overlap measure SIMPLE

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Computer-aided lateral ventricular and brain volume measurements in 3D ultrasound for assessing growth trajectories in newborns and neonates

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PHYSICS IN MEDICINE AND BIOLOGY 2018年第22期63卷 225012-225012页

作者： Boucher, Marc-Antoine Lippe, Sarah Dupont, Caroline Knoth, Inga Sophia Lopez, Gabriela Shams, Roozbeh El-Jalbout, Ramy Damphousse, Amelie Kadoury, Samuel Polytech Montreal Med Lab Montreal PQ Canada Univ Montreal Dept Psychol NED Lab Montreal PQ Canada CHU St Justine Res Ctr Montreal PQ Canada CHU St Justine Dept Radiol Montreal PQ Canada

For newborns and neonates, ultrasound (US) is the most common imaging modality used for examinations due to its accessibility and ease of use. However, precise volume measurements remain limited in 2D, while MRI in newborns is typically avoided because of immobilization issues which may require sedation. The objective of this study is to assess and validate the lateral ventricular and total brain volumes obtained with an automatic segmentation method using cerebral transfontanelle 3D US. Infants aged between 2 and 8.5 months old were recruited, with both MRI and 3D US acquired on the same day was used to validate ventricular and brain volume measurements in comparison to MRI. Lateral ventricles were segmented on both the US (manually and with a proposed automatic fusion-based approach) and MRI, while brain volumes were estimated with an automatic segmentation method. Volumetric 3D US measurements were then evaluated with respect to age distribution. For the comparison between MRI and 3D US, strong inter-class correlations (ICC) were found for the ventricle volumes (manual: 5.9% +/- 2.5% difference (ICC = 0.99);automatic: 6.0% +/- 2.6% difference (ICC = 0.98)), as well as the total brain size, with a 3.0% +/- 1.3% difference (ICC = 0.98). There was no statistically significant difference based on t-test andf-test for the lateral ventricles volume (t-test: p = 0.542) and (f-test: p = 0.738) and for the total brain volume (t-test: p = 0.412) and (f-test: p = 0.685) between MRI and 3D US. This study demonstrates that 3D US can be used to automatically assess lateral ventricular and total brain volumes with no significant difference to the MRI acquisitions. The highest correlations were obtained for infants under 8 months when the fontanelle is open.

关键词： 3D ultrasound lateral ventricles atlas-based segmentation brain volume infants ventricular-brain volume ratio

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Evaluation of segmentation methods on head and neck CT: Auto-segmentation challenge 2015

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MEDICAL PHYSICS 2017年第5期44卷 2020-2036页

作者： Raudaschl, Patrik F. Zaffino, Paolo Sharp, Gregory C. Spadea, Maria Francesca Chen, Antong Dawant, Benoit M. Albrecht, Thomas Gass, Tobias Langguth, Christoph Luthi, Marcel Jung, Florian Knapp, Oliver Wesarg, Stefan Mannion-Haworth, Richard Bowes, Mike Ashman, Annaliese Guillard, Gwenael Brett, Alan Vincent, Graham Orbes-Arteaga, Mauricio Cardenas-Pena, David Castellanos-Dominguez, German Aghdasi, Nava Li, Yangming Berens, Angelique Moe, Kris Hannaford, Blake Schubert, Rainer Fritscher, Karl D. UMIT Inst Biomed Image Anal Dept Biomed Comp Sci & Mechatron A-6060 Hall In Tirol Tyrol Austria Magna Graecia Univ Catanzaro Dept Expt & Clin Med I-88100 Catanzaro Italy Harvard Med Sch Massachusetts Gen Hosp Dept Radiat Oncol Boston MA 02114 USA Merck & Co Inc West Point PA 19422 USA Vanderbilt Univ Dept Elect Engn & Comp Sci 221 Kirkland Hall Nashville TN 37235 USA Varian Med Syst CH-5404 Baden Switzerland Univ Basel CH-4001 Basel Switzerland Fraunhofer D-64283 Darmstadt Germany Imorph Ltd Kilburn HouseManchester Sci Pk Manchester M15 6SE Lancs England Univ Nacl Colombia Signal Proc & Recognit Grp Bogota Colombia Univ Washington Seattle WA 98105 USA

Purpose: Automated delineation of structures and organs is a key step in medical imaging. However, due to the large number and diversity of structures and the large variety of segmentation algorithms, a consensus is lacking as to which automated segmentation method works best for certain applications. segmentation challenges are a good approach for unbiased evaluation and comparison of segmentation algorithms. Methods: In this work, we describe and present the results of the Head and Neck Auto-segmentation Challenge 2015, a satellite event at the Medical Image Computing and Computer Assisted Interventions (MICCAI) 2015 conference. Six teams participated in a challenge to segment nine structures in the head and neck region of CT images: brainstem, mandible, chiasm, bilateral optic nerves, bilateral parotid glands, and bilateral submandibular glands. Results: This paper presents the quantitative results of this challenge using multiple established error metrics and a well-defined ranking system. The strengths and weaknesses of the different auto-segmentation approaches are analyzed and discussed. Conclusions: The Head and Neck Auto-segmentation Challenge 2015 was a good opportunity to assess the current state-of-the-art in segmentation of organs at risk for radiotherapy treatment. Participating teams had the possibility to compare their approaches to other methods under unbiased and standardized circumstances. The results demonstrate a clear tendency toward more general purpose and fewer structure-specific segmentation algorithms. (C) 2017 American Association of Physicists in Medicine

关键词： atlas-based segmentation automated segmentation model-based segmentation segmentation challenge

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Breast-region segmentation in MRI using chest region atlas and SVM

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2017年第6期25卷 4575-4592页

作者： Fooladivanda, Aida Baradaran Shokouhi, Shahriar Ahmadinejad, Nasrin Iran Univ Sci & Technol Dept Elect Engn Tehran Iran Univ Tehran Med Sci Adv Diagnost & Intervent Radiol Res Ctr Tehran Iran

An important step for computerized analysis of breast magnetic resonance imaging (MRI) is segmentation of the breast region. Due to the similar signal intensity of fibroglandular tissue and the chest wall, the segmentation process is difficult for breasts with fibroglandular tissue connected to the chest wall. In order to overcome this challenge, a new framework is presented that relies on a chest region atlas. The proposed method first detects the approximated breast chest wall boundary using an intensity-based operation. A support vector machine (SVM) then determines the connectivity of fibroglandular tissue to the chest wall by the extracted features from the obtained breast chest wall boundary. Finally, the obtained breast chest wall boundary is accurately refined using the geometric shape of the chest region, which is obtained by an atlas-based segmentation method. The proposed method is validated using a dataset of 5964 breast MRI images from 126 women. The Dice similarity coefficient (DSC), total overlap (TO), false negative (FN), and false positive (FP) values are calculated to measure the similarity between automatic and manual segmentation results. Our method achieves DSC, TO, FN, and FP values of 96.46%, 96.41%, 3.59%, and 3.51%, respectively. The results prove the effectiveness of the presented algorithm for breasts with different sizes, shapes, and density patterns.

关键词： Breast magnetic resonance imaging breast segmentation support vector machine atlas-based segmentation chest region atlas

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Local SAR near deep brain stimulation (DBS) electrodes at 64 and 127 MHz: A simulation study of the effect of extracranial loops

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MAGNETIC RESONANCE IN MEDICINE 2017年第4期78卷 1558-1565页

作者： Golestanirad, Laleh Angelone, Leonardo M. Iacono, Maria Ida Katnani, Husam Wald, Lawrence L. Bonmassar, Giorgio Harvard Med Sch Massachusetts Gen Hosp Dept Radiol Athinoula A Martinos Ctr Biomed Imaging Charlestown MA USA Harvard Med Sch Boston MA USA US FDA Off Sci Div Biomed Phys Silver Spring MD USA US FDA Ctr Devices & Radiol Hlth Engn Labs Silver Spring MD USA Harvard Med Sch Massachusetts Gen Hosp Dept Neurosurg Boston MA USA

PurposeMRI may cause brain tissue around deep brain stimulation (DBS) electrodes to become excessively hot, causing lesions. The presence of extracranial loops in the DBS lead trajectory has been shown to affect the specific absorption rate (SAR) of the radiofrequency energy at the electrode tip, but experimental studies have reported controversial results. The goal of this study was to perform a systematic numerical study to provide a better understanding of the effects of extracranial loops in DBS leads on the local SAR during MRI at 64 and 127 MHz. MethodsA total of 160 numerical simulations were performed on patient-derived data, in which relevant factors including lead length and trajectory, loop location and topology, and frequency of MRI radiofrequency (RF) transmitter were assessed. ResultsOverall, the presence of extracranial loops reduced the local SAR in the tissue around the DBS tip compared with straight trajectories with the same length. SAR reduction was significantly larger at 127 MHz compared with 64 MHz. SAR reduction was significantly more sensitive to variable loop parameters (eg, topology and location) at 127 MHz compared with 64 MHz. ConclusionLead management strategies could exist that significantly reduce the risks of 3 Tesla (T) MRI for DBS patients. Magn Reson Med 78:1558-1565, 2017. (c) 2016 International Society for Magnetic Resonance in Medicine.

关键词： atlas-based segmentation medical implant RF heating finite element method (FEM) safety

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