Abstract: Background: Central sensitization and dysregulation of peripheral substance P and neurokinin-1 receptor (NK-1R) signaling are associated with chronic abdominal pain in inflammatory bowel disease (IBD) and irritable bowel syndrome (IBS). Although positron emission tomography (PET) has demonstrated that patients with injury-related chronic pain have diminished NK-1R availability in the brain, it is unknown whether these deficits are present in IBD and IBS patients, who have etiologically distinct forms of non-injury-related chronic pain. Aims: To determine if patients with IBD or IBS exhibit deficits in brain expression of NK-1Rs relative to healthy controls (HCs), the extent to which expression patterns differ across patient populations, and if these patterns differentially relate to clinical parameters. Methods: PET with [18F]SPA-RQ was used to measure NK-1R availability by quantifying binding potential (BP) in the three groups. Exploratory correlation analyses were performed to detect associations between NK-1R BP and physical symptoms. Results: Compared to HCs, IBD patients had NK-1R BP deficits across a widespread network of cortical and subcortical regions. IBS patients had similar, but less pronounced deficits. BP in a subset of these regions was robustly related to discrete clinical parameters in each patient population. Conclusions: Widespread deficits in NK-1R BP occur in IBD, and to a lesser extent IBS; however, discrete clinical parameters relate to NK-1R BP in each patient population. This suggests that potential pharmacological interventions that target NK-1R signaling may be most effective for treating distinct symptoms in IBD and IBS.
Abstract: Purpose: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a radiological tool for the detection and discrimination of breast lesions. The aim of this study is to evaluate a computer-aided diagnosis (CAD) system for discriminating malignant from benign breast lesions at DCE-MRI by the combined use of morphological, kinetic, and spatiotemporal lesion features.Methods: Fifty-four malignant and 19 benign breast lesions in 51 patients were retrospectively evaluated. Images were acquired at two centers at 1.5 T. Mass-like lesions were automatically segmented after image normalization and elastic coregistration of contrast-enhanced frames. For each lesion, a set of 28 3D features were extracted: ten morphological (related to shape, margins, and internal enhancement distribution); nine kinetic (computed from signal-to-time curves); and nine spatiotemporal (related to the variation of the signal between adjacent frames). A support vector machine (SVM) was trained with feature subsets selected by a genetic search. Best subsets were composed of the most frequent features selected by majority rule. The performance was measured by receiver operator characteristics analysis with a stratified tenfold cross-validation and bootstrap method for confidence intervals.Results: SVM training by the three separated classes of features resulted in an area under the curve (AUC) of 0.90 $\pm$ 0.04 (mean $\pm$ standard deviation), 0.87 $\pm$ 0.06, and 0.86 $\pm$ 0.06 for morphological, kinetic, and spatiotemporal feature, respectively. Combined training with all 28 features resulted in AUC of 0.96 $\pm$ 0.02 obtained with a selected feature subset composed by two morphological, one kinetic, and two spatiotemporal features.Conclusions: Quantitative combination of morphological, kinetic, and spatiotemporal features is feasible and provides a higher discriminating power than using the three different classes of features separately.
Abstract: PURPOSE: To describe and test a new fully automatic lesion detection system for breast DCE-MRI. MATERIALS AND METHODS: Studies were collected from two institutions adopting different DCE-MRI sequences, one with and the other one without fat-saturation. The detection pipeline consists of (i) breast segmentation, to identify breast size and location; (ii) registration, to correct for patient movements; (iii) lesion detection, to extract contrast-enhanced regions using a new normalization technique based on the contrast-uptake of mammary vessels; (iv) false positive (FP) reduction, to exclude contrast-enhanced regions other than lesions. Detection rate (number of system-detected malignant and benign lesions over the total number of lesions) and sensitivity (system-detected malignant lesions over the total number of malignant lesions) were assessed. The number of FPs was also assessed. RESULTS: Forty-eight studies with 12 benign and 53 malignant lesions were evaluated. Median lesion diameter was 6 mm (range, 5-15 mm) for benign and 26 mm (range, 5-75 mm) for malignant lesions. Detection rate was 58/65 (89%; 95% confidence interval [CI] 79%-95%) and sensitivity was 52/53 (98%; 95% CI 90%-99%). Mammary median FPs per breast was 4 (1st-3rd quartiles 3-7.25). CONCLUSION: The system showed promising results on MR datasets obtained from different scanners producing fat-sat or non-fat-sat images with variable temporal and spatial resolution and could potentially be used for early diagnosis and staging of breast cancer to reduce reading time and to improve lesion detection. Further evaluation is needed before it may be used in clinical practice.
Abstract: OBJECTIVE: To establish whether muscle blood flow (MBF) measurements with O-water positron emission tomography could reliably identify patients with critical limb ischemia and detect and quantify a distal deficit in skeletal MBF in these cases. DESIGN: O-water positron emission tomography scans were performed at rest or during unloaded ankle plantar and dorsiflexion exercise of the diseased leg in 17 subjects with leg ischemia or on a randomly selected leg of 18 age-matched healthy control subjects. TcPO2 was evaluated with Novametrix monitors and perfusion of skin topically heated to 44 degrees C and adjacent nonheated areas with a Moor Instruments laser Doppler imaging scanner. RESULTS: The enhancement of MBF induced by exercise was significantly lower in ischemic than in normal legs, and the sensitivity and specificity of this phenomenon were similar to those of laser Doppler imaging or TcPO2 in identifying ischemia subjects. In addition, the exercise MBF deficit was predominant at the distal-leg levels, indicating the ability of the technique to help determine the correct level of amputation. CONCLUSIONS: Skeletal MBF of legs with severe ischemia can be detected accurately with O-water positron emission tomography and could add valuable information about viability of skeletal muscle in the residual limb when deciding the level of an amputation.
Abstract: OBJECTIVE: The aim of this study was to compare the computed tomographic colonography (CTC) image quality and patient acceptance of three iodine-based faecal tagging bowel preparations in 60 patients undergoing the following regimens: a 2-day regimen of meal-time administration of iodine and phospho-soda (GFPH); a 2-day regimen of meal-time mild laxative, followed by iodine administered 2 h before CTC (SD); and a 2-day regimen of meal-time administration of iodine (GF). METHODS: Two independent radiologists assessed tagging quality; quantitative measures included the tagged stool density, and computer-aided detection (CAD) false-positive rate. RESULTS: The GFPH and SD regimens provided better subjective quality than GF (p < 0.001). The latter regimen resulted in a higher proportion of insufficiently tagged segments: the measured average stool density was less than 200 HU in 10.7% in all segments vs 3.6% for SD and <0.5% for GFPH, respectively. Insufficient tagging occurred mostly in the ascending colon and the caecum. The CAD false-positive rate increased following the trend: GFPH < SD < GF (p = 0.00012). GFPH was worse tolerated than SD (p < 0.05). CONCLUSIONS: Considering preparation quality alone, GFPH was the best regimen, but SD provided the best balance between bowel preparation quality and patient acceptability.
Abstract: An automatic method for the segmentation of the colonic wall is proposed for abdominal computed tomography (CT) of the cleansed and air-inflated colon. This multistage approach uses an adaptive 3D region-growing algorithm, with a self-adjusting growing condition depending on local variations of the intensity at the air-tissue boundary. The method was evaluated using retrospectively collected CT scans based on visual segmentation of the colon by expert radiologists. This evaluation showed that the procedure identifies 97% of the colon segments, representing 99.8% of the colon surface, and accurately replicates the anatomical profile of the colonic wall. The parameter settings and performance of the method are relatively independent of the scanner and acquisition conditions. The method is intended for application to the computer-aided detection of polyps in CT colonography.
Abstract: Regional differences in tissue volume and perfusion in brains of individuals with mild cognitive impairment (MCI) versus normal healthy age-matched controls (NC), and the differences between MCI-AD converters and stable MCI patients were investigated. MRI and SPECT scans were performed on 13 MCI (74+6 years) and 12 NC (75+4 years). Of the MCI patients, 10 were followed for up to three years and 4 subsequently converted to Alzheimer's disease (AD). Episodic memory function was assessed using tests of delayed recall for word lists and stories. The volume reductions and hypoperfusion were mainly confined to the medial temporal lobe (MTL) of MCI patients and associated with worse scores on memory tests. Perfusion in the corpus callosum and the gray matter of frontal, lateral temporal, parietal or occipital lobe was not significantly affected in MCI. The 4 MCI-AD converters had relatively low MTL structural volume and perfusion compared to their stable peers.
Abstract: PURPOSE: To prospectively evaluate regional alterations in the apparent diffusion coefficient (ADC) of cortical gray and white matter and subcortical structures that are known to be involved in mild cognitive impairment (MCI). MATERIALS AND METHODS: Magnetic resonance (MR) imaging was performed in 13 patients with MCI (nine men, four women; mean age, 74 years +/- 6 [standard deviation]) and 13 healthy elderly control subjects (seven men, six women; mean age, 75 years +/- 4). This study was approved by the institutional review board and was HIPAA compliant. Each subject gave informed consent. ADC was measured from manually drawn regions of interest (ROIs) of the hippocampus, parahippocampal gyrus, amygdala, corpus callosum, and anterior and posterior cingulate gyrus and from automatically defined frontal, parietal, occipital, and temporal lobes by using template masking. ROIs were outlined on anatomic images then mapped onto ADC maps by using coregistration transformation matrix. A skeleton-based region competition segmentation algorithm was used for segmentation of gray and white matter. The group difference in ADC values was assessed with independent-sample t tests. Pearson correlation analysis was used to examine the correlation of ADC values with age and memory test scores. RESULTS: Higher ADCs were found in hippocampus, temporal lobe gray matter, and corpus callosum of patients with MCI compared with that of control subjects (P < .05). By pooling all subjects together, an elevated hippocampal ADC was significantly correlated with worse memory performance scores in 5-minute and 30-minute delayed word-list recall tasks (P < .05). CONCLUSION: ADCs from gray and white matter of different brain regions can be analyzed by applying an automated template-masking method in conjunction with a skeleton-based region competition segmentation algorithm.
Abstract: The crystalline orbitals of six semiconductors (Si, C, BP, AlP, SiC and BN) are localized according to
a Wannier–Boys mixed scheme recently implemented. The degree of localization, size and shape of the localized
crystalline orbitals are documented by means of various indices (centroid position, second order central
moment tensor, its eigenvalues and principal axes, Mulliken population analysis and atomic localization
indices) and through their graphical representations. Systematic trends along the series are observed, and are
correlated to the Pauling electronegativity scale and the calculated band gap.
Abstract: The crystalline orbitals of seven oxygen containing compounds with increasing degree of covalent
character Í‘MgO, MnO, ZnO, Al2 O3 , SiO2 , AlPO4 , and CaSO4 ) are localized according to a
Wannier–Boys mixed scheme recently implemented. The resulting Wannier functions are analyzed
in terms of various indices Í‘centroids positions, second-order central moment tensor, its eigenvalues
and principal axes, Mulliken population analysis, and atomic localization indicesÍ’. Systematic trends
are observed along the series.
Abstract: Automatic segmentation of the breast and axillary region is an important preprocessing step for automatic lesion detection in breast MR and dynamic contrast-enhanced-MR studies. In this paper, we present a fully automatic procedure based on the detection of the upper border of the pectoral muscle. Compared with previous methods based on thresholding, this method is more robust to noise and field inhomogeneities. The method was quantitatively evaluated on 31 cases acquired from two centers by comparing the results with a manual segmentation. Results indicate good overall agreement within the reference segmentation (overlap=0.79 $\pm$ 0.09, recall=0.95 $\pm$ 0.02, precision=0.82 $\pm$ 0.1).
Abstract: Fecal tagging preparations are attracting notable interest as a way to increase patients’ compliance to virtual colonoscopy. Patient-friendly preparations, however, often result in less homogeneous tagging. Electronic cleansing algorithms should be capable of dealing with such preparations and yield good quality 2D and 3D images; moreover, successful electronic cleansing lays the basis for the application of Computer Aided Detection schemes. In this work, we present a cleansing algorithm based on an adaptive remapping procedure, which is based on a model of how partial volume affects both the air-tissue and the soft-tissue interfaces. Partial volume at the stool-soft tissue interface is characterized in terms of the local characteristics of tagged regions, in order to account for variations in tagging intensity throughout the colon. The two models are then combined in order to obtain a remapping equation relating the observed intensity to the that of the cleansed colon. The electronic cleansed datasets were then processed by a CAD scheme composed of three main steps: colon surface extraction, polyp candidate segmentation through curvature-based features, and linear classifier-based discrimination between true polyps and false alarms. Results obtained were compared with a previous version of the cleansing algorithm, in which a simpler remapping procedure was used. Performances are increased both in terms of the visual quality of the 2D cleansed images and 3D rendered volumes, and of CAD performances on a sameday FT virtual colonoscopy dataset.
Abstract: The successful application of Computer Aided Detection schemes to CT Colonography depends not only on their performances in terms of sensitivity and specificity, but also on the interaction with the radiologist, and thus ultimately on factors such as the nature of CAD prompts and the reading paradigm. Fecal tagging is emerging as a widely accepted technique for patient preparation, and patient-friendlier schemes are being proposed in an effort to increase compliance to screening programs; the interaction between CAD and FT regimens should likewise be taken into account. In this scenario, an analysis of the characteristics of CAD prompts is of paramount importance in order to guide further research, both from clinical and technical viewpoints. The CAD scheme analyzed in this paper is essentially composed of five steps: electronic cleansing, colon surface extraction, polyp candidate segmentation, pre-filtering of residual tagged stool and classification of the generated candidates in true polyps vs. false alarms. False positives were divided into six categories: untagged and tagged solid stool, haustral folds, extra-colonic candidates, ileocecal valve and cleansing artifacts. A full cathartic preparation was compared with a semi-cathartic regimen with sameday fecal tagging, which is characterized by higher patient acceptance but also higher inhomogeneity. The distribution of false positives at segmentation reflects the quality of preparation, as more inhomogeneous tagging results in a higher number of untagged solid stool and cleansing artifacts.
Abstract: Dynamic Contrast Enhanced MRI (DCE-MRI) has today a well-established role, complementary to routine imaging techniques for breast cancer diagnosis such as mammography. Despite its undoubted clinical advantages, DCE-MRI data analysis is time-consuming and Computer Aided Diagnosis (CAD) systems are required to help radiologists. Segmentation is one of the key step of every CAD image processing pipeline, but most techniques available require human interaction. We here present the preliminary results of a fully automatic lesion detection method, capable of dealing with fat suppression image acquisition sequences, which represents a challenge for image processing algorithms due to the low SNR. The method is based on four fundamental steps: registration to correct for motion artifacts; anatomical segmentation to discard anatomical structures located outside clinically interesting lesions; lesion detection to select enhanced areas and false positive reduction based on morphological and kinetic criteria. The testing set was composed by 13 cases and included 27 lesions (10 benign and 17 malignant) of diameter > 5 mm. The system achieves a per-lesion sensitivity of 93%, while yielding an acceptable number of false positives (26 on average). The results of our segmentation algorithm were verified by visual inspection, and qualitative comparison with a manual segmentation yielded encouraging results.
Abstract: DCE-MRI is a diagnostic method that can visualize neoangiogenic-induced vascular changes. Typically, the analysis of these data is time-consuming and the visualization of the quantitative information on tumor vasculature, derivable from DCE-MRI, is not easy and comfortable. In this study, we propose a method to accelerate computation and analysis of DCE-MRI data, while making easy to use the functional information obtained from model-based functional analysis.
