scholarly journals Optical Co-registration of MRI and On-scalp MEG

2018 ◽  
Author(s):  
Rasmus Zetter ◽  
Joonas Iivanainen ◽  
Lauri Parkkonen
Keyword(s):  
Structured Light ◽  
Brain Anatomy ◽  
Surface Mesh ◽  
Mr Image ◽  
Registration Method ◽  
Median Nerve Stimulation ◽  
Somatosensory Evoked Fields ◽  
Head Surface ◽  
Anatomical Image ◽  
The Difference

Objective: To estimate the neural current distribution underlying magnetoencephalographic (MEG) signals and to link such estimates to brain anatomy, MEG data have to be co-registered with an anatomical image, typically an MR image. Optically-pumped magnetometers (OPMs) enable the construction of on-scalp MEG systems providing higher sensitivity and spatial resolution than conventional SQUID-based MEG systems. Here, we present a co-registration method that can be applied to on-scalp MEG systems, regardless of the number of channels. Methods: We apply a structured-light 3D scanner to create a surface mesh of the subject's head and the sensor array, which we fit to the MR image. To assess accuracy, we quantified the reproducibility of the surface mesh and localized current dipoles embedded in a phantom. Finally, we measured somatosensory evoked fields (SEF) to median nerve stimulation and compared their source estimates with those obtained with a SQUID-based MEG system. Results: The structured-light scanner reproduced the head surface with < 1 mm error. Phantom dipoles were localized with a mean error of 2.14 mm. The difference in SEF dipole positions between OPMs and SQUIDs were 5.0, 0.9, and 1.6 mm for N20m, P35m and P60m response peaks, respectively. Conclusion: The developed co-registration is inexpensive, fast and can easily be applied to on-scalp MEG. It is also more convenient to use than traditional co-registration methods while also being more accurate. Significance: We developed and validated a co-registration method that can be applied to on-scalp MEG systems. The method enables accurate source estimation with these novel MEG systems.

scholarly journals A High-Precision Registration Technology Based on Bundle Adjustment in Structured Light Scanning System

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Jianying Yuan ◽  
Qiong Wang ◽  
Xiaoliang Jiang ◽  
Bailin Li
Keyword(s):  
High Precision ◽  
Large Scale ◽  
Structured Light ◽  
Bundle Adjustment ◽  
Scanning System ◽  
Control Points ◽  
Registration Method ◽  
Global Control ◽  
3D Data ◽  
Structured Light Scanning

The multiview 3D data registration precision will decrease with the increasing number of registrations when measuring a large scale object using structured light scanning. In this paper, we propose a high-precision registration method based on multiple view geometry theory in order to solve this problem. First, a multiview network is constructed during the scanning process. The bundle adjustment method from digital close range photogrammetry is used to optimize the multiview network to obtain high-precision global control points. After that, the 3D data under each local coordinate of each scan are registered with the global control points. The method overcomes the error accumulation in the traditional registration process and reduces the time consumption of the following 3D data global optimization. The multiview 3D scan registration precision and efficiency are increased. Experiments verify the effectiveness of the proposed algorithm.

A robust automatic registration method for hand-held structured light 3D scanner

2014 ◽  
Author(s):  
Guomin Zhan ◽  
Mengqi Wu ◽  
Kai Zhong ◽  
Zhongwei Li ◽  
Yusheng Shi
Keyword(s):  
Structured Light ◽  
Automatic Registration ◽  
3D Scanner ◽  
Registration Method

Age-related changes in intracranial compartment volumes in normal adults assessed by magnetic resonance imaging

1996 ◽  
Vol 84 (6) ◽  
pp. 982-991 ◽  
Author(s):  
Mitsunori Matsumae ◽  
Ron Kikinis ◽  
István A. Mórocz ◽  
Antonio V. Lorenzo ◽  
Tamás Sándor ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Brain Volume ◽  
Intracranial Volume ◽  
Mr Image ◽  
Content Type ◽  
Age Related ◽  
The Difference ◽  
Normal Adults ◽  
Fine Print ◽  
Age Related Changes

✓ Magnetic resonance (MR) image—based computerized segmentation was used to measure various intracranial compartments in 49 normal volunteers ranging in age from 24 to 80 years to determine age-related changes in brain, ventricular, and extraventricular cerebrospinal fluid (CSF) volumes. The total intracranial volume (sum of brain, ventricular, and extraventricular CSF) averaged 1469 ± 102 cm3 in men and 1289 ± 111 cm3 in women. The difference was attributable primarily to brain volume, which accounted for 88.6% of the respective intracranial volumes in both sexes, but was significantly larger in men (1302 ± 112 cm3) than in women (1143 ± 105 cm3). In both, the cranial CSF volume averaged 11.4%. Total intracranial volume did not change with age, although the normalized brain volume of both men and women began to decrease after the age of 40 years. This decrease was best reflected by expansion of the extraventricular CSF volume which, after the age of 50 years, was more marked in men than in women. The volume of the cranial CSF, as determined by MR image-based computerized segmentation, is considerably larger than traditionally accepted and resides mostly extraventricularly. Expansion of CSF volume with age provides a good index of brain shrinkage although evolving changes and growth of the head with age tend to confound the results.

Fiducial Versus Nonfiducial Neuronavigation Registration Assessment and Considerations of Accuracy

2008 ◽  
Vol 62 (suppl_1) ◽  
pp. ONS201-ONS208 ◽  
Author(s):  
Wolfgang K. Pfisterer ◽  
Stephen Papadopoulos ◽  
Denise A. Drumm ◽  
Kris Smith ◽  
Mark C. Preul
Keyword(s):  
Surgical Navigation ◽  
Measurement Techniques ◽  
Guidance System ◽  
Frameless Stereotaxy ◽  
Target Tissue ◽  
Anatomic Landmarks ◽  
Registration Method ◽  
The Mean ◽  
The Difference ◽  
Rigid Set

Abstract Objective: For frameless stereotaxy, users can choose between anatomic landmarks (ALs) or surface fiducial markers (FMs) for their match points during registration to define an alignment of the head in the physical and radiographic image space. In this study, we sought to determine the concordance among a point-merged FM registration, a point-merged AL registration, and a combined point-merged anatomic/surface-merged (SM) registration, i.e., to determine the accuracy of registration techniques with and without FMs by examining the extent of agreement between the system-generated predicted value and physical measured values. Methods: We examined 30 volunteers treated with gamma knife surgery. The frameless stereotactic image-guidance system called the StealthStation (Medtronic Surgical Navigation Technologies, Louisville, CO) was used. Nine FMs were placed on the patient's head and four were placed on a Leksell frame rod-box, which acted as a rigid set to determine the difference in error. For each registration form, we recorded the generated measurement (GM) and the physical measurement (PM) to each of the four checkpoint FMs. Bland and Altman plot difference analyses were used to compare measurement techniques. Correlations and descriptive analyses were completed. Results: The mean of values for GMs were 1.14 mm for FM, 2.3 mm for AL, and 0.96 mm for SM registrations. The mean errors of the checkpoints were 3.49 mm for FM, 3.96 mm for AL, and 3.33 mm for SM registrations. The correlation between GMs and PMs indicated a linear relationship for all three methods. AL registration demonstrated the greatest mean difference, followed by FM registration; SM registration had the smallest difference between GMs and PMs. Differences in the anatomic registration methods, including SM registration, compared with FM registration were within a mean ± 1.96 (standard deviation) according to the Bland and Altman analysis. Conclusion: For our sample of 30 patients, all three registration methods provided comparable distances to the target tissue for surgical procedures. Users may safely choose anatomic registration as a less costly and more time-efficient registration method for frameless stereotaxy.

scholarly journals PERBEDAAN INFORMASI CITRA ANATOMI SEKUEN DIFFUSION WEIGHTED IMAGING (DWI) ANTARA PENGGUNAAN PROPELLER DENGAN TANPA PROPELLER PADA PEMERIKSAAN MRI BRAIN DENGAN KASUS STROKE

2020 ◽  
Vol 6 (1) ◽  
pp. 36-43
Author(s):  
Yeti Kartikasari ◽  
M. Irwan Kartili ◽  
Dwi Rochmayanti ◽  
Nindya Aprilia
Keyword(s):  
Diffusion Weighted Imaging ◽  
Quantitative Research ◽  
Wilcoxon Test ◽  
Image Information ◽  
Mri Brain ◽  
Diffusion Weighted ◽  
Significant Difference ◽  
Anatomical Image ◽  
The Difference

Background: Stroke is a brain disease where an acute nerve function is occurred due to the cerebral vascular disorders. To establish a diagnosis the stroke, it can be identified by employing the Diffusion Weighted Imaging (DWI) sequence in the MRI examination. Artifacts still exist on the MRI image which in turn reduce the resolution when using the DWI sequence. Adding the PROPELLER data acquisition method in the DWI sequence possibly improves the quality of brain images. The purpose of this study is to know the difference on the quality of anatomical image information between the DWI sequences with PROPELLER and without PROPELLER methods, and to determine adequate anatomical image appearance that created in amongst of the two methods, specifically for the stroke disease.Methods: this research is quantitative research with experimental approach. This study was conducted using MRI 1.5 T at Bethesda Hospital Yogyakarta. Data were 16 images from 8 patients using DWI sequences using PROPELLER without PROPELLER on MRI Brain examination with stroke. The results of the image were evaluated on 7 criteria: cortex cerebri, basal ganglia, thalamus, pons, cerebellum, stroke (infarction) and artifacts using questionnaires given to 3 respondents. Data analysis was done by Wilcoxon test to know the difference of anatomical image information on DWI sequence using PROPELLER without PROPELLER and to know better anatomical image information from both sequences seen from mean rank value.Results: The results shown, there is a significant difference on the quality of anatomical image information and the artifacts between the use of DWI sequence with and without PROPELLER methods ( 0.05). Based on the mean rank results, the DWI PROPELLER sequence has a higher mean rank value 4.50 compared to the DWI sequence without PROPELLER 0.00.Conclusions: The DWI PROPELLER sequence has better image results compared to the DWI sequence without PROPELLER.

scholarly journals 200. The difference of MR image between spin Echo and field echo sequence

1993 ◽  
Vol 49 (2) ◽  
pp. 320
Author(s):  
Tsukasa Doi ◽  
Yoshimasa Mizuno ◽  
Seiichi Masuda ◽  
Fumio Uenoyama
Keyword(s):  
Spin Echo ◽  
Mr Image ◽  
The Difference

Points Registration for Roadside LiDAR Sensors

2019 ◽  
Vol 2673 (9) ◽  
pp. 627-639 ◽  
Author(s):  
Jianqing Wu ◽  
Hao Xu ◽  
Wei Liu
Keyword(s):  
Ground Surface ◽  
Cost Effective ◽  
Point Clouds ◽  
Iterative Closest Point ◽  
Connected Vehicle ◽  
Point Selection ◽  
Registration Method ◽  
Data Registration ◽  
The Difference ◽  
Iterative Closest Point Algorithm

Roadside LiDAR deployment provides a solution to obtain the real-time high-resolution micro traffic data of unconnected road users for the connected-vehicle road network. Single roadside LiDAR sensor has a lot of limitations considering the scant coverage and the difficulty of handling object occlusion issue. Multiple roadside LiDAR sensors can provide a larger coverage and eliminate the object occlusion issue. To combine different LiDAR sensors, it is necessary to integrate the point clouds into the same coordinate system. The existing points registration methods serving mapping scans or autonomous sensing systems could not be directly used for roadside LiDAR sensors considering the different feature of point clouds and the spare points in the cost-effective roadside LiDAR sensors. This paper developed an approach for roadside LiDAR points registration. The developed points-aggregation-based partial iterative closest point algorithm (PA-PICP) is a semi-automatic points registration method, which contains two major parts: XY data registration and Z adjustment. A semi-automatic key point selection method was introduced. The partial iterative closest point was applied to minimize the difference between different LiDARs in the XY plane. The intersection of ground surface between different LiDARs was used for Z-axis adjustment. The performance of the developed procedure was evaluated with field-collected LiDAR data. The results showed the effectiveness and accuracy of data integration using PA-PICP was greatly improved compared with points registration using the traditional iterative closest point. The case studies also showed that the occlusion issue can be fixed after PA-PICP points registration.

Reconstruction and Mesh Simplification of Tooth Model

2011 ◽  
Vol 474-476 ◽  
pp. 955-960
Author(s):  
Lang Ming Zhou ◽  
Shun Yi Zheng ◽  
Yang Zhou ◽  
Qing Li
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Structured Light ◽  
Biomechanical Model ◽  
Model Analysis ◽  
Mesh Simplification ◽  
Surface Point ◽  
Surface Mesh ◽  
Regular Surface ◽  
Mesh Model

A method for reconstruction of tooth model based on rotate scanning using structured light match technology was proposed. And an algorithm of mesh simplification of the surface point of tooth model using section lines sampling with fixed angles was achieved, which can provide regular surface mesh points for finite element method (FEM). Experiments showed that the method and algorithm could generate accurate surface mesh model which can provide excellent data support for finite element biomechanical model analysis.

scholarly journals Cross-Modality Coherent Registration of Whole Mouse Brains

2021 ◽  
Author(s):  
Hanchuan Peng ◽  
Lei Qu ◽  
Yuanyuan Li ◽  
Peng Xie ◽  
Lijuan Liu ◽  
...  
Keyword(s):  
Large Scale ◽  
Single Neuron ◽  
Cell Types ◽  
Brain Anatomy ◽  
Neuron Morphology ◽  
Brain Images ◽  
Whole Brain ◽  
Preparation Methods ◽  
Registration Method ◽  
3D Images

Abstract Recent whole brain mapping projects are collecting large-scale 3D images using powerful and informative modalities, such as STPT, fMOST, VISoR, or MRI. Registration of these multi-dimensional whole-brain images onto a standard atlas is essential for characterizing neuron types and constructing brain wiring diagrams. However, cross-modality image registration is challenging due to intrinsic variations of brain anatomy and artifacts resulted from different sample preparation methods and imaging modalities. We introduced a cross-modality registration method, called mBrainAligner, which uses coherent landmark mapping as well as deep neural networks to align whole mouse brain images to the standard Allen Common Coordinate Framework atlas. We also built a single cell resolution atlas using the fMOST modality, and used our method to generate whole brain map of 3D full single neuron morphology and neuron cell types.

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