An automatic MRI/SPECT registration algorithm using image intensity and anatomical feature as matching characters: application on the evaluation of Parkinson's disease

Automatic Contrast Enhancement of Brain MR Images Using Hierarchical Correlation Histogram Analysis

Parkinson’s disease is a progressive neurodegenerative disorder that has a higher probability of occurrence in middle-aged and older adults than in the young. With the use of a computer-aided diagnosis (CAD) system, abnormal cell regions can be identified, and this identification can help medical personnel to evaluate the chance of disease. This study proposes a hierarchical correlation histogram analysis based on the grayscale distribution degree of pixel intensity by constructing a correlation histogram, that can improves the adaptive contrast enhancement for specific objects. The proposed method produces significant results during contrast enhancement preprocessing and facilitates subsequent CAD processes, thereby reducing recognition time and improving accuracy. The experimental results show that the proposed method is superior to existing methods by using two estimation image quantitative methods of PSNR and average gradient values. Furthermore, the edge information pertaining to specific cells can effectively increase the accuracy of the results.

A new strategy to improve coregistration of SPECT and MR images in patients with high grade glioma

This paper proposes a new strategy to optimize the coregistration of Technetium-99m Sestamibi SPECT and MRI data in case of patients with high grade glioma. It consists in a personalized approach which selects, for each data set, the best registration method among several ones. To achieve this selection, a quantitative dedicated evaluation criterion based on the average intensities within specific anatomical structures corresponding to physiological areas of uptake of Sestamibi was defined. The strategy was applied to sixty-two data sets using nine registration methods based on mutual information and chamfer distance registration approaches, with different settings. It was implemented within the Anatomist/Brainvisa environment, using its basic registration functions. The visual evaluation by experts indicated that this strategy provides 60% good quality registrations, and 26% intermediate quality ones. Compared to the single use of the best global registration method, the number of registrations of good quality was multiplied by 1.4 when using the data specific strategy.

Multiscaled combination of MR and SPECT images in neuroimaging: A simplex method based variable-weight fusion

Single-photon emission computed tomography (SPECT) images alone are difficult to understand in diagnosis, since anatomical structures are absent from the data. Studies on combination attempt to locate functional changes of the SPECT image by the magnetic resonance (MR) image. Due to the low similarity between original images, fused results are always darkened, obscured or loss some crucial anatomical structures. This paper has solved these problems by the variable-weight matrix which is estimated by minimizing the cost function using the simplex method. Under the generalized intensity-hue-saturation (GIHS) framework, the multiscaled analysis is presented for a better detail preservation. Besides, interactive approaches are discussed for the gradual variation between original images and the control of detail performance. The similarity assessment evaluates several different methods on a normal brain atlas. Two clips show the interactive property of the proposed method, while two medical cases demonstrate its clinical values. We conclude that the proposed method is superior to traditional methods, when considering the definition and the information capacity of fused results.

SPECT imaging evaluation in movement disorders: far beyond visual assessment

Single photon emission computed tomography (SPECT) imaging with (123)I-FP-CIT is of great value in differentiating patients suffering from Parkinson's disease (PD) from those suffering from essential tremor (ET). Moreover, SPECT with (123)I-IBZM can differentiate PD from Parkinson's "plus" syndromes. Diagnosis is still mainly based on experienced observers' visual assessment of the resulting images while many quantitative methods have been developed in order to assist diagnosis since the early days of neuroimaging. The aim of this work is to attempt to categorize, briefly present and comment on a number of semi-quantification methods used in nuclear medicine neuroimaging. Various arithmetic indices have been introduced with region of interest (ROI) manual drawing methods giving their place to automated procedures, while advancing computer technology has allowed automated image registration, fusion and segmentation to bring quantification closer to the final diagnosis based on the whole of the patient's examinations results, clinical condition and response to therapy. The search for absolute quantification has passed through neuroreceptor quantification models, which are invasive methods that involve tracer kinetic modelling and arterial blood sampling, a practice that is not commonly used in a clinical environment. On the other hand, semi-quantification methods relying on computers and dedicated software try to elicit numerical information out of SPECT images. The application of semi-quantification methods aims at separating the different patient categories solving the main problem of finding the uptake in the structures of interest. The semi-quantification methods which were studied fall roughly into three categories, which are described as classic methods, advanced automated methods and pixel-based statistical analysis methods. All these methods can be further divided into various subcategories. The plethora of the existing semi-quantitative methods reinforces the feeling that visual assessment is still the base of image interpretation and that the unambiguous numerical results that will allow the absolute differentiation between the known diseases have not been standardized yet. Switching to a commonly agreed-ideally PC-based-automated software that may take raw or mildly processed data (checked for consistency and maybe corrected for attenuation and/or scatter and septal penetration) as input, work with basic operator's inference and produce validated numerical results that will support the diagnosis is in our view the aim towards which efforts should be directed. After all, semi-quantification can improve sensitivity, strengthen diagnosis, aid patient's follow-up and assess the response to therapy. Objective diagnosis, altered diagnosis in marginal cases and a common approach to multicentre trials are other benefits and future applications of semi-quantification.