scholarly journals Fast Nonsupervised 3D Registration of PET and MR Images of the Brain

1994 ◽  
Vol 14 (5) ◽  
pp. 749-762 ◽  
Author(s):  
Jean-François Mangin ◽  
Vincent Frouin ◽  
Isabelle Bloch ◽  
Bernard Bendriem ◽  
Jaime Lopez-Krahe
Keyword(s):  
Three Dimensional ◽  
Magnetic Resonance Images ◽  
Surface Matching ◽  
3D Registration ◽  
Matching Algorithm ◽  
Distance Map ◽  
Brain Surface ◽  
Discrete Surfaces ◽  
Positron Emission ◽  
The Brain

We propose a fully nonsupervised methodology dedicated to the fast registration of positron emission tomography (PET) and magnetic resonance images of the brain. First, discrete representations of the surfaces of interest (head or brain surface) are automatically extracted from both images. Then, a shape-independent surface-matching algorithm gives a rigid body transformation, which allows the transfer of information between both modalities. A three-dimensional (3D) extension of the chamfer-matching principle makes up the core of this surface-matching algorithm. The optimal transformation is inferred from the minimization of a quadratic generalized distance between discrete surfaces, taking into account between-modality differences in the localization of the segmented surfaces. The minimization process is efficiently performed via the precomputation of a 3D distance map. Validation studies using a dedicated brain-shaped phantom have shown that the maximum registration error was of the order of the PET pixel size (2 mm) for the wide variety of tested configurations. The software is routinely used today in a clinical context by the physicians of the Service Hospitalier Frédéric Joliot (>150 registrations performed). The entire registration process requires ∼5 min on a conventional workstation.

Three-dimensional magnetic resonance images of the brain: application to neurosurgical planning

1990 ◽  
Vol 72 (3) ◽  
pp. 433-440 ◽  
Author(s):  
Xiaoping Hu ◽  
Kim K. Tan ◽  
David N. Levin ◽  
Simranjit Galhotra ◽  
John F. Mullan ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Three Dimensional ◽  
Preoperative Assessment ◽  
Magnetic Resonance Images ◽  
Interactive Software ◽  
Cross Sectional ◽  
Content Type ◽  
Brain Surface ◽  
Neurosurgical Planning ◽  
The Brain

✓ Data from single 10-minute magnetic resonance scans were used to create three-dimensional (3-D) views of the surfaces of the brain and skin of 12 patients. In each case, these views were used to make a preoperative assessment of the relationship of lesions to brain surface structures associated with movement, sensation, hearing, and speech. Interactive software was written so that the user could “slice” through the 3-D computer model and inspect cross-sectional images at any level. A surgery simulation program was written so that surgeons were able to “rehearse” craniotomies on 3-D computer models before performing the actual operations. In each case, the qualitative accuracy of the 3-D views was confirmed by intraoperative inspection of the brain surface and by intraoperative electrophysiological mapping, when available.

scholarly journals A new method for reconstructing brain morphology: applying the brain-neurocranial spatial relationship in an extant lungfish to a fossil endocast

2016 ◽  
Vol 3 (7) ◽  
pp. 160307 ◽  
Author(s):  
Alice M. Clement ◽  
Robin Strand ◽  
Johan Nysjö ◽  
John A. Long ◽  
Per E. Ahlberg
Keyword(s):  
Three Dimensional ◽  
Spatial Relationship ◽  
Software Tool ◽  
Morphological Data ◽  
Brain Morphology ◽  
Cranial Cavity ◽  
Distance Map ◽  
Computed Tomographic ◽  
Phylogenetic Implications ◽  
The Brain

Lungfish first appeared in the geological record over 410 million years ago and are the closest living group of fish to the tetrapods. Palaeoneurological investigations into the group show that unlike numerous other fishes—but more similar to those in tetrapods—lungfish appear to have had a close fit between the brain and the cranial cavity that housed it. As such, researchers can use the endocast of fossil taxa (an internal cast of the cranial cavity) both as a source of morphological data but also to aid in developing functional and phylogenetic implications about the group. Using fossil endocast data from a three-dimensional-preserved Late Devonian lungfish from the Gogo Formation, Rhinodipterus , and the brain-neurocranial relationship in the extant Australian lungfish, Neoceratodus , we herein present the first virtually reconstructed brain of a fossil lungfish. Computed tomographic data and a newly developed ‘brain-warping’ method are used in conjunction with our own distance map software tool to both analyse and present the data. The brain reconstruction is adequate, but we envisage that its accuracy and wider application in other taxonomic groups will grow with increasing availability of tomographic datasets.

scholarly journals The Metabolic Centroid Method for PET Brain Image Analysis

1989 ◽  
Vol 9 (3) ◽  
pp. 388-397 ◽  
Author(s):  
A. V. Levy ◽  
J. D. Brodie ◽  
JJ. A. G. Russell ◽  
N. D. Volkow ◽  
E. Laska ◽  
...  
Keyword(s):  
Random Noise ◽  
Three Dimensional ◽  
Normal Subjects ◽  
Subcortical Structures ◽  
Centroid Method ◽  
Whole Brain ◽  
Positron Emission ◽  
The Mean ◽  
Brain Image Analysis ◽  
The Brain

The method of centroids is an approach to the analysis of three-dimensional whole-brain positron emission tomography (PET) metabolic images. It utilizes the brain's geometric centroid and metabolic centroid so as to objectively characterize the central tendency of the distribution of metabolic activity in the brain. The method characterizes the three-dimensional PET metabolic image in terms of four parameters: the coordinates of the metabolic centroid and the mean metabolic rate of the whole brain. These parameters are not sensitive to spatially uniform random noise or to the position of the subject's head within a uniform PET camera field of view. The method has been applied to 40 normal subjects, 22 schizophrenics who were treated with neuroleptics, and 20 schizophrenics who were neuroleptic-free. The mean metabolic centroid of the normal subjects was found to be superior to the mean geometric centroid of the brain. The mean metabolic centroid of chronic schizophrenics is lower and more posterior to the mean geometric centroid than is that of normals. This difference is greater in medicated than in unmedicated schizophrenics. The posterior and downward displacement of the mean metabolic centroid is consistent with the concepts of hypofrontality, hyperactivity of subcortical structures, and neuroleptic effect in schizophrenics.

scholarly journals Anatomy and three-dimensional reconstructions of the brain of the white whale (Delphinapterus leucas) from magnetic resonance images

2001 ◽  
Vol 262 (4) ◽  
pp. 429-439 ◽  
Author(s):  
Lori Marino ◽  
Timothy L. Murphy ◽  
Amy L. Deweerd ◽  
John A. Morris ◽  
Archibald J. Fobbs ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Three Dimensional ◽  
Magnetic Resonance Images ◽  
Delphinapterus Leucas ◽  
White Whale ◽  
The Brain

Video-Assisted Navigation for Adjustment of Image-Guidance Accuracy to Slight Brain Shift

2015 ◽  
Vol 11 (4) ◽  
pp. 504-511 ◽  
Author(s):  
Sven R Kantelhardt ◽  
Angelika Gutenberg ◽  
Axel Neulen ◽  
Naureen Keric ◽  
Mirjam Renovanz ◽  
...  
Keyword(s):  
Surface Relief ◽  
Image Guidance ◽  
Magnetic Resonance Images ◽  
Guidance System ◽  
Brain Shift ◽  
Operating Microscope ◽  
Registration Accuracy ◽  
Brain Surface ◽  
Standard Operating ◽  
The Brain

Abstract BACKGROUND Information supplied by an image-guidance system can be superimposed on the operating microscope oculars or on a screen, generating augmented reality. Recently, the outline of a patient's head and skull, injected in the oculars of a standard operating microscope, has been used to check the registration accuracy of image guidance. OBJECTIVE To propose the use of the brain surface relief and superficial vessels for real-time intraoperative visualization and image-guidance accuracy and for intraoperative adjustment for brain shift. METHODS A commercially available image-guidance system and a standard operating microscope were used. Segmentation of the brain surface and cortical blood vessel relief was performed manually on preoperative computed tomography and magnetic resonance images. The overlay of segmented digital and real operating-microscope images was used to monitor image-guidance accuracy. Adjustment for brain shift was performed by manually matching digital images on real structures. RESULTS Experimental manipulation on a phantom proved that the brain surface relief could be used to restore accuracy if the primary registration shifted. Afterward, the technique was used to assist during surgery of 5 consecutive patients with 7 deep-seated brain tumors. The brain surface relief could be successfully used to monitor registration accuracy after craniotomy and during the whole procedure. If a certain degree of brain shift occurred after craniotomy, the accuracy could be restored in all cases, and corticotomies were correctly centered in all cases. CONCLUSION The proposed method was easy to perform and augmented image-guidance accuracy when operating on small deep-seated lesions.

scholarly journals Anatomy and three-dimensional reconstructions of the brain of a bottlenose dolphin (Tursiops truncatus) from magnetic resonance images

2001 ◽  
Vol 264 (4) ◽  
pp. 397-414 ◽  
Author(s):  
Lori Marino ◽  
Keith D. Sudheimer ◽  
Timothy L. Murphy ◽  
Kristina K. Davis ◽  
D. Ann Pabst ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Bottlenose Dolphin ◽  
Tursiops Truncatus ◽  
Three Dimensional ◽  
Magnetic Resonance Images ◽  
The Brain

scholarly journals A Review of Publicly Available Automatic Brain Segmentation Methodologies, Machine Learning Models, Recent Advancements, and Their Comparison

2021 ◽  
pp. 097275312199017
Author(s):  
Mahender Kumar Singh ◽  
Krishna Kumar Singh
Keyword(s):  
Machine Learning ◽  
Automatic Segmentation ◽  
Three Dimensional ◽  
Brain Structures ◽  
Magnetic Resonance Images ◽  
Learning Models ◽  
Convolutional Network ◽  
Research Perspective ◽  
The Brain ◽  
Machine Learning Models

Background: The noninvasive study of the structure and functions of the brain using neuroimaging techniques is increasingly being used for its clinical and research perspective. The morphological and volumetric changes in several regions and structures of brains are associated with the prognosis of neurological disorders such as Alzheimer’s disease, epilepsy, schizophrenia, etc. and the early identification of such changes can have huge clinical significance. The accurate segmentation of three-dimensional brain magnetic resonance images into tissue types (i.e., grey matter, white matter, cerebrospinal fluid) and brain structures, thus, has huge importance as they can act as early biomarkers. The manual segmentation though considered the “gold standard” is time-consuming, subjective, and not suitable for bigger neuroimaging studies. Several automatic segmentation tools and algorithms have been developed over the years; the machine learning models particularly those using deep convolutional neural network (CNN) architecture are increasingly being applied to improve the accuracy of automatic methods. Purpose: The purpose of the study is to understand the current and emerging state of automatic segmentation tools, their comparison, machine learning models, their reliability, and shortcomings with an intent to focus on the development of improved methods and algorithms. Methods: The study focuses on the review of publicly available neuroimaging tools, their comparison, and emerging machine learning models particularly those based on CNN architecture developed and published during the last five years. Conclusion: Several software tools developed by various research groups and made publicly available for automatic segmentation of the brain show variability in their results in several comparison studies and have not attained the level of reliability required for clinical studies. The machine learning models particularly three dimensional fully convolutional network models can provide a robust and efficient alternative with relation to publicly available tools but perform poorly on unseen datasets. The challenges related to training, computation cost, reproducibility, and validation across distinct scanning modalities for machine learning models need to be addressed.

A Positron Emission Tomography Study of Visual and Mental Spatial Exploration

1995 ◽  
Vol 7 (4) ◽  
pp. 433-445 ◽  
Author(s):  
E. Mellet ◽  
N. Tzourio ◽  
M. Denis ◽  
B. Mazoyer
Keyword(s):  
Positron Emission Tomography ◽  
Three Dimensional ◽  
Occipital Cortex ◽  
Magnetic Resonance Images ◽  
Visual Exploration ◽  
Emission Tomography ◽  
Total Darkness ◽  
Positron Emission ◽  
The Right ◽  
No Activation

We measured normalized regional cerebral blood flow (NrCBF) using positron emission tomography (PET) and oxygen-15-labeled water in eight young right-handed healthy volunteers selected as high-imagers. during 2 runs of 3 different conditions: 1, rest in total darkness 2; visual exploration of a map 3; mental exploration of the same map in total darkness. NrCBF images were aligned with individual magnetic resonance images (MRI), and NrCBF variations between pairs of measurements (N = 15) were computed in regions of interest having anatomical boundaries that were defined using a three-dimensional (3-D) reconstruction of each subject MRI. During visual exploration, we found bilateral activations of primary visual areas, superior and inferior occipital gyri, fusiform and lingual gyri, cuneus and precuneus, bilateral superior parietal, and angular gyri. The right lateral premotor area was also activated during this task while superior temporal gyri and Broca's area were deactivated. By contrast, mental exploration activated the right superior occipital cortex, the supplementary motor area, and the cerebellar vermis. No activation was observed in the primary visual area. These results argue for a specific participation of the superior occipital cortex in the generation and maintenance of visual mental images.

Functional topography of the low postcentral area

2002 ◽  
Vol 97 (2) ◽  
pp. 388-395 ◽  
Author(s):  
Warren Boling ◽  
David C. Reutens ◽  
André Olivier
Keyword(s):  
Three Dimensional ◽  
Central Area ◽  
Magnetic Resonance Images ◽  
Sensory Function ◽  
Lower Face ◽  
Content Type ◽  
Sensory Area ◽  
Positron Emission ◽  
Postcentral Gyrus ◽  
Fine Print

Object. The goal of this study was to establish a reliable method for identification of face and tongue sensory function in the lower central area. Methods. All positron emission tomography (PET) clinical activation studies performed over a 3-year period at the Montreal Neurological Institute and Hospital were evaluated by coregistering the PET images with three-dimensional reconstructions of magnetic resonance images obtained in the same patients. In addition to stereotactic coordinates and measurements based on distance from the sylvian fissure, gyral and sulcal landmarks were analyzed to determine their reliability in localizing the sensory areas of the tongue and lower face. The convolutional anatomy of the central area is an important guide to the identification of function. The sensory area of the tongue is recognized as a triangular region at the base of the postcentral gyrus; the sensory area of the lower face resides in the narrowed portion of the postcentral gyrus, immediately above the tongue area. Conclusions. Cortical landmarks such as the substrata of tongue and face sensory impressions are more reliable guides than stereotactic coordinates or measurements for localizing function.

FULLY AUTOMATED PEELING TECHNIQUE FOR T1-WEIGHTED, HIGH-QUALITY MR HEAD SCANS

2004 ◽  
Vol 04 (02) ◽  
pp. 141-156 ◽  
Author(s):  
RAVINDA G. N. MEEGAMA ◽  
JAGATH C. RAJAPAKSE
Keyword(s):  
White Matter ◽  
Healthy Subjects ◽  
Three Dimensional ◽  
Magnetic Resonance Images ◽  
Mr Images ◽  
3D Segmentation ◽  
High Quality ◽  
Anatomical Structures ◽  
Present Technique ◽  
The Brain

In order to conduct many non-intrusive clinical studies of the human brain, an accurate model that is capable of extracting the brain matter from magnetic resonance images (MRI) is required. We present a fully automated two-stage procedure to extract the brain matter accurately from a database of T1-weighted, high-quality MRI of healthy subjects. The procedure is initiated using a three dimensional (3D) segmentation process to separate the brain from other anatomical structures. The extracted brain is then subjected to an adaptive filter to remove cerebro-spinal fluid that fills sulcal cavities. The experiments clearly demonstrate the capability of the present technique in accurately peeling the brain. The accuracy of the results is tested using relative gray and white matter concentrations of both simulated and real MR images.

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