In this paper we describe a novel integrated photonic system that can be applied for optical imaging of intracranial brain tumor (Glioblastoma) angiogenesis in small animal model. A non-invasive multi-modality approach based on near infrared spectroscopy (NIRS) technique namely: Steady State Diffuse Optical Spectroscopy (DOS) along with Magnetic Resonance Imaging (MRI) technique is applied for monitoring the concentration of oxyhemoglobin, deoxyhemoglobin and water within tumor region and for studying the vascular status of tumor and the physiological changes that occur during brain tumor angiogenesis.
Proc. SPIE. 5746, Medical Imaging 2005: Physiology, Function, and Structure from Medical Images
KEYWORDS: Information fusion, Visualization, Data storage, Magnetic resonance imaging, Diffusion, Neuroimaging, Functional magnetic resonance imaging, Picture Archiving and Communication System, Diffusion tensor imaging, Brain
MRI Neuroimaging provides a rich source of image content including structural (MRI, Diffusion DTI), functional (fMRI, Perfusion ASL), and metabolic (MRS) information. Today MRI capabilities allow to acquire these imaging techniques in one session in most cases. In order to be of diagnostic value, the immense and diverse data needs to be (i) automatically post-processed to extract the relevant information, e.g. 3D brain maps from 4D fMRI, and to be (ii) fused and visualized to correlate the voxel-based findings. The purpose of this study is to demonstrate the feasibility of automatic relevant information retrieval and fusion of MRI, fMRI, DTI, ASL, and MRS data of a pediatric population into a single semantic data representation. By using advanced imaging, we may able to detect a larger spectrum of abnormalities in the neonatal brain. Each imaging application, provides unique information about the physiology (fMRI, ASL), the anatomy (DTI), and the biochemistry (MRS) of the newborn brain in relation to normal development and brain injury. By being able to integrate this technology, we will be able to combine biochemical, physiologic and anatomic information which can provide unique insight about not only the normal development of the brain, but also injury of the neonatal brain.
We have investigated the feasibility of obtaining high-quality Diffusion Tensor Magnetic Resonance Imaging (DTI) data in newborn humans. We show that the use of an MR-compatible incubator with customized RF headcoils can provide diffusion tensor maps of sufficient quality for quantitative DTI measurements and 3D fiber tracking. We have also investigated the effect of performing affine co-registration on the diffusion-weighted images, as is conventionally believed to be necessary to correct for eddy current distortion effects. We have found that co-registration indeed successfully eliminates the well-known bright band of high anisotropy that forms in the peripheral brain regions, and that such co-registration also reduces smaller interior regions of artifactually high diffusion anisotropy. In addition, we have investigated whether non-affine distortions exist in the diffusion-weighted images, as might be expected due to the existence of large susceptibility gradients. The results of performing 2nd order mutual information polynomial registration of the diffusion-weighted images to the non-diffusion-weighted (b=0) image in each slice show that subtle differences between affine and 2nd order co-registration do exist, which suggests that care must be taken when interpreting FA values in cortical brain regions. Finally, we present results of 3D white matter fiber tracking in the newborn brain. To preserve the full information content of the DTI data, we used simple Euler integration without noise filtering or fiber crossing detection. Our results show that the directionality of the major white matter pathways can be visualized in newborns.
Functional Magnetic Resonance Imaging (fMRI) provides the location and regional extent of a task correlated activation in the brain. Recently we have demonstrated, that fMRI of passive sensory tasks (visual, auditory, motor) can be successfully used to map cortical activation in the newborn brain. However the interpretation of the functional response in the immature brain is difficult, as the blood oxygen level dependent (BOLD) physiological signal and location of the activation is quite different compared to adult fMRI responses of similar tasks. We expect, that the major reason for these differences are primarily caused by the immature myelination of the white matter tracts at this age. Diffusion tensor imaging (DTI) can be used to measure the white matter tract development in the newborn brain. The purpose of this paper is to report how to obtain and to combine fMRI and DTI data processing to enhance functional brain mapping in newborns. We obtained simultaneous fMRI and DTI data of 18 newborns, post-conceptional age (gestational age at study) between 34-week and 52-week, which were referred for clinical indicated MRI. 16 out of 18 subjects have been successfully investigated with combined fMRI and DTI and functional activation could be obtained. Fiber tracking was successfully in the visual and auditory cortex, but proofed difficult in the motor-cortex. The additional tract information supported the functional findings and the interpretation in the immature brain. The novel functional imaging in newborn is challenging because of the yet unknown physiological response and location of activation in the newborn brain. Therefore one need additional evidence that the functional findings are valid in the context of structural development. The maturation of myelination is an essential information to compare and to interpret fMRI in newborns. We conclude that the proposed method of combined fMRI and DTI, derived from adult neuroimaging, will be most relevant to understand the physiological response and thus the neurodevelopment of the newborn brain.
We have developed and evaluated a novel image-matching method for medical images. This method allows the radiologist to search through - in a matter of seconds - large medical databases containing thousands of patients. To illustrate the usefulness of this method in a clinical setting, we have employed this method as a diagnostic support tool for pediatric brain diseases. To this aim, we have assembled a database containing Magnetic Resonance (MR) brain images of 2500 patients between ages 0 and 18 with known brain lesions. As the images are added to the database, they are registered to a global coordinate system. In addition, regions of interests (ROI) are labeled, and sophisticated image processing techniques are used to extract image parameters from the ROIs and from the entire MR image. To perform a clinically realistic search through this database, we have established a training testbed at Childrens Hospital Los Angeles for acquiring MR images from our PACS server of patients with unknown lesions. We have matched these images with the images in the pediatric brain MR database containing known lesions using our image-matching method. An expert pediatric neuroradiologist evaluated the search results. We found that in most cases, our image-matching method is able to retrieve images with relevant diagnostic content, making it highly attractive as a diagnostic support tool.