The impact of motion correction on lesion characterization in DCE breast MR images

2011 ◽  
Author(s):  
Martin Bergtholdt ◽  
Sven Kabus ◽  
Rafael Wiemker ◽  
Thomas Buelow
Keyword(s):  
Motion Correction ◽  
Mr Images ◽  
Breast Mr ◽  
Lesion Characterization ◽  
The Impact

scholarly journals Multigrid Nonlocal Gaussian Mixture Model for Segmentation of Brain Tissues in Magnetic Resonance Images

2016 ◽  
Vol 2016 ◽  
pp. 1-10
Author(s):  
Yunjie Chen ◽  
Tianming Zhan ◽  
Ji Zhang ◽  
Hongyuan Wang
Keyword(s):  
Magnetic Resonance ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Magnetic Resonance Images ◽  
Intensity Inhomogeneity ◽  
Mr Images ◽  
Proposed Model ◽  
Brain Mr Images ◽  
The Impact

We propose a novel segmentation method based on regional and nonlocal information to overcome the impact of image intensity inhomogeneities and noise in human brain magnetic resonance images. With the consideration of the spatial distribution of different tissues in brain images, our method does not need preestimation or precorrection procedures for intensity inhomogeneities and noise. A nonlocal information based Gaussian mixture model (NGMM) is proposed to reduce the effect of noise. To reduce the effect of intensity inhomogeneity, the multigrid nonlocal Gaussian mixture model (MNGMM) is proposed to segment brain MR images in each nonoverlapping multigrid generated by using a new multigrid generation method. Therefore the proposed model can simultaneously overcome the impact of noise and intensity inhomogeneity and automatically classify 2D and 3D MR data into tissues of white matter, gray matter, and cerebral spinal fluid. To maintain the statistical reliability and spatial continuity of the segmentation, a fusion strategy is adopted to integrate the clustering results from different grid. The experiments on synthetic and clinical brain MR images demonstrate the superior performance of the proposed model comparing with several state-of-the-art algorithms.

scholarly journals Segmentation of breast MR images based on multiresolution level set algorithm

2014 ◽  
Vol 63 (11) ◽  
pp. 118701
Author(s):  
Fan Hong ◽  
Zhu Yan-Chun ◽  
Wang Fang-Mei ◽  
Zhang Xu-Mei
Keyword(s):  
Level Set ◽  
Mr Images ◽  
Breast Mr

scholarly journals The efficacy of different preprocessing steps in reducing motion-related confounds in diffusion MRI connectomics

2020 ◽  
Author(s):  
Stuart Oldham ◽  
Aurina Arnatkevic̆iūtė ◽  
Robert E. Smith ◽  
Jeggan Tiego ◽  
Mark A. Bellgrove ◽  
...  
Keyword(s):  
Diffusion Mri ◽  
Motion Correction ◽  
Healthy Adult ◽  
Structural Connectivity ◽  
Head Motion ◽  
List Type ◽  
Edge Weighting ◽  
Quantitative Metrics ◽  
The Impact ◽  
Connectivity Strength

AbstractHead motion is a major confounding factor in neuroimaging studies. While numerous studies have investigated how motion impacts estimates of functional connectivity, the effects of motion on structural connectivity measured using diffusion MRI have not received the same level of attention, despite the fact that, like functional MRI, diffusion MRI relies on elaborate preprocessing pipelines that require multiple choices at each step. Here, we report a comprehensive analysis of how these choices influence motion-related contamination of structural connectivity estimates. Using a healthy adult sample (N = 252), we evaluated 240 different preprocessing pipelines, devised using plausible combinations of different choices related to explicit head motion correction, tractography propagation algorithms, track seeding methods, track termination constraints, quantitative metrics derived for each connectome edge, and parcellations. We found that an approach to motion correction that includes outlier replacement and within-slice volume correction led to a dramatic reduction in cross-subject correlations between head motion and structural connectivity strength, and that motion contamination is more severe when quantifying connectivity strength using mean tract fractional anisotropy rather than streamline count. We also show that the choice of preprocessing strategy can significantly influence subsequent inferences about network organization, with the location of network hubs varying considerably depending on the specific preprocessing steps applied. Our findings indicate that the impact of motion on structural connectivity can be successfully mitigated using recent motion-correction algorithms that include outlier replacement and within-slice motion correction.HighlightsWe assess how motion affects structural connectivity in 240 preprocessing pipelinesMotion contamination of structural connectivity depends on preprocessing choicesAdvanced motion correction tools reduce motion confoundsFA edge weighting is more susceptible to motion effects than streamline count

scholarly journals Magnetic resonance imaging and computed tomography of equine cheek teeth and adjacent structures: comparative study of image quality in horses in vivo, post-mortem and frozen-thawed

2019 ◽  
Vol 61 (1) ◽  
Author(s):  
Christin Röttiger ◽  
Maren Hellige ◽  
Bernhard Ohnesorge ◽  
Astrid Bienert-Zeit
Keyword(s):  
Computed Tomography ◽  
Image Quality ◽  
Post Mortem ◽  
Mr Images ◽  
Adjacent Structures ◽  
Group A ◽  
Group B ◽  
The Impact ◽  
Ct And Mri

Abstract Background The use of cadavers for radiology research methodologies involving subjective image quality evaluation of anatomical criteria is well-documented. The purpose of this method comparison study was to evaluate the image quality of dental and adjacent structures in computed tomography (CT) and high-field (3 T) magnetic resonance (MR) images in cadaveric heads, based on an objective four-point rating scale. Whilst CT is a well-established technique, MR imaging (MRI) is rarely used for equine dental diagnostics. The use of a grading system in this study allowed an objective assessment of CT and MRI advantages in portraying equine cheek teeth. As imaging is commonly performed with cadaveric or frozen and thawed heads for dental research investigations, the second objective was to quantify the impact of the specimens’ conditions (in vivo, post-mortem, frozen-thawed) on the image quality in CT and MRI. Results The CT and MR images of nine horses, focused on the maxillary premolar 08s and molar 09s, were acquired post-mortem (Group A). Three observers scored the dental and adjacent tissues. Results showed that MR sequences gave an excellent depiction of endo- and periodontal structures, whereas CT produced high-quality images of the hard tooth and bony tissues. Additional CT and MRI was performed in vivo (Group B) and frozen-thawed (Group C) in three of these nine horses to specify the condition of the best specimens for further research. Assessing the impact of the specimens’ conditions on image quality, specific soft tissues of the maxillary 08s and 09s including adjacent structures (pulps, mucosa of the maxillary sinuses, periodontal ligament, soft tissue inside the infraorbital canal) were graded in group B and C and analysed for significant differences within CT and MR modalities in comparison to group A. Results showed that MRI scores in vivo were superior to the post-mortem and frozen-thawed condition. Conclusions On comparing the imaging performance of CT and MRI, both techniques show a huge potential for application in equine dentistry. Further studies are needed to assess the clinical suitability of MRI. For further research investigations it must be considered, that the best MR image quality is provided in live horses.

Automatic motion correction for breast MR imaging.

Radiology ◽  
1996 ◽  
Vol 198 (3) ◽  
pp. 903-906 ◽  
Author(s):  
C S Zuo ◽  
A Jiang ◽  
B L Buff ◽  
T G Mahon ◽  
T Z Wong
Keyword(s):  
Mr Imaging ◽  
Motion Correction ◽  
Breast Mr

scholarly journals Different preprocessing strategies lead to different conclusions: A [11C]DASB-PET reproducibility study

2019 ◽  
Vol 40 (9) ◽  
pp. 1902-1911
Author(s):  
Martin Nørgaard ◽  
Melanie Ganz ◽  
Claus Svarer ◽  
Vibe G Frokjaer ◽  
Douglas N Greve ◽  
...  
Keyword(s):  
Motion Correction ◽  
Positive Association ◽  
Sequential Data ◽  
Double Blind ◽  
Data Set ◽  
Reproducibility Study ◽  
Positron Emission ◽  
The Impact ◽  
Data Analytic

Positron emission tomography (PET) neuroimaging provides unique possibilities to study biological processes in vivo under basal and interventional conditions. For quantification of PET data, researchers commonly apply different arrays of sequential data analytic methods (“preprocessing pipeline”), but it is often unknown how the choice of preprocessing affects the final outcome. Here, we use an available data set from a double-blind, randomized, placebo-controlled [11C]DASB-PET study as a case to evaluate how the choice of preprocessing affects the outcome of the study. We tested the impact of 384 commonly used preprocessing strategies on a previously reported positive association between the change from baseline in neocortical serotonin transporter binding determined with [11C]DASB-PET, and change in depressive symptoms, following a pharmacological sex hormone manipulation intervention in 30 women. The two preprocessing steps that were most critical for the outcome were motion correction and kinetic modeling of the dynamic PET data. We found that 36% of the applied preprocessing strategies replicated the originally reported finding ( p < 0.05). For preprocessing strategies with motion correction, the replication percentage was 72%, whereas it was 0% for strategies without motion correction. In conclusion, the choice of preprocessing strategy can have a major impact on a study outcome.

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