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1.
J Magn Reson Imaging ; 33(6): 1422-9, 2011 Jun.
Article in English | MEDLINE | ID: mdl-21591012

ABSTRACT

PURPOSE: To compare automated segmentation of the quadratus lumborum (QL) based on statistical shape modeling (SSM) with conventional manual processing of magnetic resonance (MR) images for segmentation of this paraspinal muscle. MATERIALS AND METHODS: The automated SSM scheme for QL segmentation was developed using an MR database of 7 mm axial images of the lumbar region from 20 subjects (cricket fast bowlers and athletic controls). Specifically, a hierarchical 3D-SSM scheme for segmentation of the QL, and surrounding psoas major (PS) and erector spinae+multifidus (ES+MT) musculature, was implemented after image preprocessing (bias field correction, partial volume interpolation) followed by image registration procedures to develop average and probabilistic MR atlases for initializing and constraining the SSM segmentation of the QL. The automated and manual QL segmentations were compared using spatial overlap and average surface distance metrics. RESULTS: The spatial overlap between the automated SSM and manual segmentations had a median Dice similarity metric of 0.87 (mean = 0.86, SD = 0.08) and mean average surface distance of 1.26 mm (SD = 0.61) and 1.32 mm (SD = 0.60) for the right and left QL muscles, respectively. CONCLUSION: The current SSM scheme represents a promising approach for future automated morphometric analyses of the QL and other paraspinal muscles from MR images.


Subject(s)
Magnetic Resonance Imaging/methods , Muscle, Skeletal/pathology , Adolescent , Adult , Algorithms , Athletes , Automation , Humans , Image Processing, Computer-Assisted/methods , Male , Models, Statistical , Pattern Recognition, Automated/methods , Probability , Reproducibility of Results
2.
Neuroimage ; 34(4): 1506-18, 2007 Feb 15.
Article in English | MEDLINE | ID: mdl-17207638

ABSTRACT

The registration of functional brain data to common stereotaxic brain space facilitates data sharing and integration across different subjects, studies, and even imaging modalities. Thus, we previously described a method for the probabilistic registration of functional near-infrared spectroscopy (fNIRS) data onto Montreal Neurological Institute (MNI) coordinate space that can be used even when magnetic resonance images of the subjects are not available. This method, however, requires the careful measurement of scalp landmarks and fNIRS optode positions using a 3D-digitizer. Here we present a novel registration method, based on simulations in place of physical measurements for optode positioning. First, we constructed a holder deformation algorithm and examined its validity by comparing virtual and actual deformation of holders on spherical phantoms and real head surfaces. The discrepancies were negligible. Next, we registered virtual holders on synthetic heads and brains that represent size and shape variations among the population. The registered positions were normalized to MNI space. By repeating this process across synthetic heads and brains, we statistically estimated the most probable MNI coordinate values, and clarified errors, which were in the order of several millimeters across the scalp, associated with this estimation. In essence, the current method allowed the spatial registration of completely stand-alone fNIRS data onto MNI space without the use of supplementary measurements. This method will not only provide a practical solution to the spatial registration issues in fNIRS studies, but will also enhance cross-modal communications within the neuroimaging community.


Subject(s)
Brain/anatomy & histology , Spectroscopy, Near-Infrared/methods , Adult , Algorithms , Female , Humans , Imaging, Three-Dimensional , Male , Reference Values , Sensitivity and Specificity , User-Computer Interface
3.
Neuroimage ; 34(4): 1600-11, 2007 Feb 15.
Article in English | MEDLINE | ID: mdl-17207640

ABSTRACT

With the advent of multi-channel EEG hardware systems and the concurrent development of topographic and tomographic signal source localization methods, the international 10/20 system, a standard system for electrode positioning with 21 electrodes, was extended to higher density electrode settings such as 10/10 and 10/5 systems, allowing more than 300 electrode positions. However, their effectiveness as relative head-surface-based positioning systems has not been examined. We previously developed a virtual 10/20 measurement algorithm that can analyze any structural MR head and brain image. Extending this method to the virtual 10/10 and 10/5 measurement algorithms, we analyzed the MR images of 17 healthy subjects. The acquired scalp positions of the 10/10 and 10/5 systems were normalized to the Montreal Neurological Institute (MNI) stereotactic coordinates and their spatial variability was assessed. We described and examined the effects of spatial variability due to the selection of positioning systems and landmark placement strategies. As long as a detailed rule for a particular system was provided, it yielded precise landmark positions on the scalp. Moreover, we evaluated the effective spatial resolution of 329 scalp landmark positions of the 10/5 system for multi-subject studies. As long as a detailed rule for landmark setting was provided, 241 scalp positions could be set effectively when there was no overlapping of two neighboring positions. Importantly, 10/10 positions could be well separated on a scalp without overlapping. This study presents a referential framework for establishing the effective spatial resolutions of 10/20, 10/10, and 10/5 systems as relative head-surface-based positioning systems.


Subject(s)
Head/anatomy & histology , Body Surface Area , Electroencephalography , Head/diagnostic imaging , Head/physiology , Humans , Image Processing, Computer-Assisted/methods , Magnetic Resonance Imaging , Posture , Reproducibility of Results , Scalp/anatomy & histology , Tomography, X-Ray Computed
4.
Appetite ; 47(2): 220-32, 2006 Sep.
Article in English | MEDLINE | ID: mdl-16797780

ABSTRACT

Sensory evaluation (SE) of food attributes involves various levels of cognitive functions, yet not much has been studied about its neural basis. Using multi-channel functional near-infrared spectroscopy (fNIRS), we examined the activation of the anterior portion of the lateral prefrontal cortex (LPFC) of 12 healthy volunteers during the SE of tea samples. The experimental task used corresponded to the early phase of the same-different test, and required subjects to attentively taste tea samples and memorize their flavors. To isolate activation associated with the cognitive functions involved in the task, we contrasted the results with those achieved by a control (Ctl) task during which subjects held familiar tea samples in their mouths without actively evaluating their flavor. We probabilistically registered the fNIRS data to the Montreal Neurological Institute standard brain space to examine the results as they correspond with other published neuroimaging studies. We found significant activation in the left LPFC and in the right inferior frontal gyrus. The activation pattern was consistent with earlier studies on encoding of other sensory stimuli, with cortical regions supposed to be involved in semantic and perceptual processing. This research makes a start on characterizing the cognitive process employed during SE from the neuroimaging perspective.


Subject(s)
Memory/physiology , Prefrontal Cortex/physiology , Recognition, Psychology , Spectroscopy, Near-Infrared/methods , Taste/physiology , Adult , Brain Mapping , Female , Functional Laterality , Humans , Male , Tea
5.
Neuroimage ; 26(4): 1184-92, 2005 Jul 15.
Article in English | MEDLINE | ID: mdl-15961052

ABSTRACT

It is important to create a link between stereotaxic coordinates and head-surface-based positioning systems in order to share data between tomographic and transcranial brain mapping studies. In our previous studies, we established the probabilistic correspondence of the international 10-20 positions to the standard stereotaxic coordinate systems and made a reference database. However, its expansion required the physical marking of the 10-20 positions and the subsequent acquisition of MR images. To avoid such tedious procedures, we developed a virtual 10-20 measurement algorithm that can be applied to re-analyze any structural MR image that covers the whole head. As in the physical 10-20 measurements, with the reference points given, the algorithm automatically determines each 10-20 position step by step. Using the virtual 10-20 measurement method, we re-analyzed the MR images of 17 healthy subjects for whom we had determined 10-20 positions by physical marking in our previous study. The acquired 10-20 positions were normalized to the Montreal Neurological Institute (MNI) stereotactic coordinates and compared with the positions previously determined by physical measurements. 10-20 positions determined using the virtual and physical methods were roughly consistent. Average standard deviations for virtual and physical methods were 7.7 mm and 9.0 mm, respectively. There was a systematic shift in the virtual method, likely due to the absence of hair interference. We corrected the shift with affine transformation. The virtual 10-20 measurement method proved to be an effective alternative to physical marking. This method will serve as an essential tool for expanding the reference database and will further strengthen the link between tomographic and transcranial brain mapping methods.


Subject(s)
Brain Mapping/methods , Image Interpretation, Computer-Assisted/standards , Magnetic Resonance Imaging/standards , Stereotaxic Techniques/standards , Adult , Algorithms , Female , Head/anatomy & histology , Humans , Male , Middle Aged , Posture , Reproducibility of Results
6.
Neuroimage ; 27(4): 842-51, 2005 Oct 01.
Article in English | MEDLINE | ID: mdl-15979346

ABSTRACT

The registration of functional brain data to the common brain space offers great advantages for inter-modal data integration and sharing. However, this is difficult to achieve in functional near-infrared spectroscopy (fNIRS) because fNIRS data are primary obtained from the head surface and lack structural information of the measured brain. Therefore, in our previous articles, we presented a method for probabilistic registration of fNIRS data to the standard Montreal Neurological Institute (MNI) template through international 10-20 system without using the subject's magnetic resonance image (MRI). In the current study, we demonstrate our method with a new statistical model to facilitate group studies and provide information on different components of variability. We adopt an analysis similar to the single-factor one-way classification analysis of variance based on random effects model to examine the variability involved in our improvised method of probabilistic registration of fNIRS data. We tested this method by registering head surface data of twelve subjects to seventeen reference MRI data sets and found that the standard deviation in probabilistic registration thus performed for given head surface points is approximately within the range of 4.7 to 7.0 mm. This means that, if the spatial registration error is within an acceptable tolerance limit, it is possible to perform multi-subject fNIRS analysis to make inference at the population level and to provide information on positional variability in the population, even when subjects' MRIs are not available. In essence, the current method enables the multi-subject fNIRS data to be presented in the MNI space with clear description of associated positional variability. Such data presentation on a common platform, will not only strengthen the validity of the population analysis of fNIRS studies, but will also facilitate both intra- and inter-modal data sharing among the neuroimaging community.


Subject(s)
Brain Mapping , Brain/anatomy & histology , Brain/physiology , Image Processing, Computer-Assisted/methods , Spectroscopy, Near-Infrared , Adult , Algorithms , Cerebral Cortex/physiology , Databases, Factual , Electromagnetic Fields , Female , Humans , Image Processing, Computer-Assisted/statistics & numerical data , Individuality , Male , Models, Statistical , Neural Pathways/anatomy & histology , Neural Pathways/physiology , Reference Standards , Skull/anatomy & histology , Tooth/anatomy & histology , Tooth/physiology
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