BRAIM: A computer-aided diagnosis system for neurodegenerative diseases and brain lesion monitoring from volumetric analyses.

BACKGROUND AND OBJECTIVE: This paper presents BRAIM, a computer-aided diagnosis (CAD) system to help clinicians in diagnosing and treatment monitoring of brain diseases from magnetic resonance image processing. BRAIM can be used for early diagnosis of neurodegenerative diseases such as Parkinson, Alzheimer or Multiple Sclerosis and also for brain lesion diagnosis and monitoring. METHODS: The developed CAD system includes different user-friendly tools for segmenting and determining whole brain and brain structure volumes in an easy and accurate way. Specifically, three types of measurements can be performed: (1) total volume of white, gray matter and cerebrospinal fluid; (2) brain structure volumes (volume of putamen, thalamus, hippocampus and caudate nucleus); and (3) brain lesion volumes. RESULTS: As a proof of concept, some study cases were analyzed with the presented system achieving promising results. In addition to be used to quantify treatment effectiveness in patients with brain lesions, it was demonstrated that BRAIM is able to classify a subject according to the brain volume measurements using as reference a healthy control database created for this purpose. CONCLUSIONS: The CAD system presented in this paper simplifies the daily work of clinicians and provides them with objective and quantitative volume data for prospective and retrospective analyses.

Efficient Variational Approach to Multimodal Registration of Anatomical and Functional Intra-Patient Tumorous Brain Data.

This paper addresses the functional localization of intra-patient images of the brain. Functional images of the brain (fMRI and PET) provide information about brain function and metabolism whereas anatomical images (MRI and CT) supply the localization of structures with high spatial resolution. The goal is to find the geometric correspondence between functional and anatomical images in order to complement and fuse the information provided by each imaging modality. The proposed approach is based on a variational formulation of the image registration problem in the frequency domain. It has been implemented as a C/C[Formula: see text] library which is invoked from a GUI. This interface is routinely used in the clinical setting by physicians for research purposes (Inscanner, Alicante, Spain), and may be used as well for diagnosis and surgical planning. The registration of anatomic and functional intra-patient images of the brain makes it possible to obtain a geometric correspondence which allows for the localization of the functional processes that occur in the brain. Through 18 clinical experiments, it has been demonstrated how the proposed approach outperforms popular state-of-the-art registration methods in terms of efficiency, information theory-based measures (such as mutual information) and actual registration error (distance in space of corresponding landmarks).

Metal artifact reduction in dental CT images using polar mathematical morphology.

Most dental implant planning systems use a 3D representation of the CT scan of the patient under study as it provides a more intuitive view of the human jaw. The presence of metallic objects in human jaws, such as amalgam or gold fillings, provokes several artifacts like streaking and beam hardening which makes the reconstruction process difficult. In order to reduce these artifacts, several methods have been proposed using the raw data, directly obtained from the tomographs, in different ways. However, in DICOM-based applications this information is not available, and thus the need of a new method that handles this task in the DICOM domain. The presented method performs a morphological filtering in the polar domain yielding output images less affected by artifacts (even in cases of multiple metallic objects) without causing significant smoothing of the anatomic structures, which allows a great improvement in the 3D reconstruction. The algorithm has been automated and compared to other image denoising methods with successful results.