Rayleigh to Compton ratio with monochromatic radiation from an X-ray tube

Results on the Rayleigh to Compton ratio (R/C) for elements and compounds with low atomic number (5 6 Z 6 12) are presented. These materials are difficult to identify and characterize with other radiological techniques because of their very close linear attenuation coefficients. A transportable setup for R/C measurements was assembled and tested. This comprises an X-ray tube, in which the output radiation is partially ‘‘converted’’ to monochromatic radiation emitted by a secondary target. The experimental results are compared with theory, determined through coherent and incoherent scattering cross sections

Automatic Segmentation of Cerebral Glioma in DT-MR Images by 3D Texture Analysis

Tumor cells in cerebral glioma invade surrounding tissues preferentially along white matter tracts, spreading beyond the abnormal area seen on conventional MR images. Diffusion tensor imaging can reveal larger peritumoral abnormalities in gliomas that are not apparent on MRI. Our aim was to characterize pathological vs healthy tissue in DTI datasets by 3D statistical Texture Analysis, developing an automatic segmentation technique (CAD) for cerebral glioma, especially useful in a patient follow-up during chemotherapy, and for preoperative assessment of tumor extension. Fifteen patients with glioma (9 low-grade, 6 high-grade) were selected. 3T MR-DTI consisted of a single-shot EPI sequence (b=1000 s/mm2, 32 gradient directions). Fractional anisotropy (FA), mean diffusivity (MD), p and q maps, were obtained. Manual segmentation of pathological areas was performed on each map. 3D texture analysis was applied with a sliding window approach to the segmented ROIs and to the contralateral healthy tissue, in order to identify discriminating features from the intensity and the gradient histogram, and from the cooccurrence (COM) and the run length matrix (RLM). After determining (according to their Fisher-filter score) the best features for each map, the feature-space dimensionality was reduced by Principal Component Analysis, and a neural-network classifier was trained. Glioma segmentations, performed by tissue classification, were compared with the manual ones. Six patients were employed for training, nine for testing. Classifier sensitivity, specificity and ROC curves were calculated: preliminary results were obtained for the p map (AUC = 0.96, sensitivity and specificity equal to 90%, classification error 10.0%) and FA map (AUC = 0.98, sensitivity and specificity equal to 92.6%, classification error equal to 7.3%). Test images were automatically segmented by tissue classification; manual and automatic segmentations were compared, showing good concordance. Our preliminary results show that this approach could allow objective tumor identification and quantitative measurement, with good accuracy

Diffusion Tensor Magnetic Resonance Imaging: a Semi-Automated Algorithm to Identify Damaged Brain Areas from Fractional Anisotropy Maps

Aim of this study was to analyse diffusion tensor imaging (DTI) datasets in order to identify damaged areas or disorders of the brain in a semi-automatic way. For this purpose, a software tool has been developed: it takes in input the fractional anisotropy (FA) map of a (damaged) brain and, after several steps involving the comparison between the two brain hemispheres, it gives back, as output, a binary mask with a ROI (Region of Interest) that shows the probably damaged area. In the same way, starting from the MR image without diffusion weighting (b0), we find another ROI that we compare with the one previously detected from the FA map. Then we overlay these ROIs onto both the FA map and the image without diffusion weighting, trying to quantify how well the ROIs cover the pathological tissue. This procedure was repeated on a few patients (healthy and pathological ones). The algorithm worked well, showing as a preliminary result that FA maps allow a neater detection of the pathological tissue if compared to MR images without diffusion weighting

A semi-automated DTI-based approach to evaluate structural characteristics and extension of cerebral gliomas (poster No C-2926)

Purpose: Diffusion Tensor Imaging (DTI) can identify peritumoral white-matter abnormalities in cerebral gliomas not apparent on conventional MRI, that can be referred to infiltration regions surrounding the tumor core. Our aim was to characterize pathological and healthy tissue in DTI datasets by 3D statistical texture analysis, with the purpose of developing a semi-automated detection technique (CAD) of cerebral tumors. Methods and materials: Fifteen patients with gliomas (9 low-grade, 6 high-grade) were selected. 3T MRDTI consisted of a single-shot EPI sequence (b=1000 s/mm2, 32 gradient directions). Diffusion maps were obtained (anisotropy maps: FA and q; isotropy maps: MD and p). Manual segmentation of pathological areas was performed on each map; 3D texture analysis was applied to these ROIs and to the contralateral healthy tissue, in order to identify discriminating features based on cooccurrence and “run length” matrices. Ninety features were calculated, with a sliding-window approach; the most representative ones were selected by the Fisher filter, and Principal Component Analysis was applied, followed by Neural Network training. Results: Six patients were employed for training, nine for testing. Sensitivity, specificity and ROC curves were calculated, giving satisfactory results (95% sensitivity at 88% specificity, ROC AUC 0.89). Test images were automatically segmented by tissue classification; manual and automatic segmentations were compared by the Jaccard coefficient, and were in good accord. Mapping of Principal Components was used to characterize the tumoral structure. Conclusion: This semi-automated approach looks promising for preoperative assessment of structural heterogeneity and extension of cerebral gliomas and for evaluating response to chemotherapy

Fun with Cancer Patients: The Affect of Cancer

Fun with Cancer Patients: Practice-Based Research and the Affect of Cancer examines Fun with Cancer Patients, a project mixing strategies from live art, intervention performance and current discourses on performance documentation, to examine the personal experience of cancer. Fun with Cancer Patients was researched and developed through a Wellcome Trust Arts Award in 2010 and will be fully realised with a group of teenagers at Birmingham Children’s Hospital in association with Fierce Festival in 2013. This chapter demonstrates the strategies Fun with Cancer Patients takes to challenge all-inclusive narratives of an individual’s cancer experience and provide current cancer patients alternative modes of engagement, particularly for those who find the tools established for cancer patients to be limiting and/or reaffirming of a policed positivity. The chapter argues that Fun with Cancer Patients opens up new spaces for expression and works to capture a patient-focused experience inside of performance creation and subsequent exhibition. Employing theory on applied arts practice by Petra Kuppers and James Thompson, this chapter will demonstrate how Fun with Cancer Patients addresses existing tensions between applied arts projects and their public dissemination as a critical performance element

Automatic Segmentation and Therapy Follow-up of Cerebral Glioma in Diffusion-Tensor Images

Gliomas are the most common primary brain tumors, with a typical infiltrative growth pattern along white matter (WM) fibers. Diffusion Tensor Imaging (DTI) is sensitive to the directional diffusion of water along WM tracts, which allows the identification of subtle peritumoral glioma infiltration that are not apparent on conventional Magnetic Resonance imaging. The aim of this study was to characterize pathological and healthy tissue in DTI datasets by statistical texture analysis, developing a Computer Assisted Detection (CAD) technique for cerebral glioma. This system, coupled to voxel-based tumor evolution analysis, could allow objective tumor identification and qualitative and quantitative measurements in the follow-up of patients during chemotherapy. In this paper, preliminary results of tumor segmentation and evolution analysis are shown

Magnetic properties of novel superparamagnetic MRI contrast agents based on colloidal nanocrystals

Novel systems based on colloidal magnetic nanocrystals (NCs), potentially useful as superparamagnetic (SP) contrast agents for magnetic resonance imaging (MRI) have been investigated. The NCs we have studied comprise organic-capped single-crystalline maghemite (\u3b3-Fe2O3) cores possessing controlled sizes and shapes. We have comparatively examined spherical and tetrapod-like NCs, the latter being branched particles possessing four arms which depart out at tetrahedral angles from a central point. The as-synthesized NCs are passivated by hydrophobic surfactant molecules and thus are fully dispersible in nonpolar media only. The NCs have been made soluble in aqueous solution by applying a procedure based on the surface intercalation and coating with an amphiphilic polymer shell. NMR relaxivities R1 and R2 were compared with ENDOREM\uae, one of the standard commercial SP-MRI contrast agent. We found that the spherical NCs exhibit R1 and R2 relaxivities slightly lower than those of ENDOREM\uae, over the whole frequency range; on the contrary, tetrapods show relaxivities about one order of magnitude lower. The physical origin of such difference in relaxivities between tetrapod- and spheres-based nanostructures is under investigation and it is possibly related to different sources of the magnetic anisotropy

A CAD system for cerebral gliomas based on 3D texture features in Diffusion Tensor MR images

Gliomas are the most common primary brain tumors. The diffuse infiltration of white matter (WM) tracts by cerebral gliomas is a major cause of their appalling prognosis: tumor cells invade, displace, and possibly destroy WM. An early diagnosis and a comprehensive evaluation of tumor extent and relationships with surrounding ana- tomical structures are crucial in determining prognosis and treatment planning. Conventional MRI sequences (e.g. T1- or T2-weighted images) have limited sensitivity and specificity in diagnosing brain tumors [1], because they do not always allow precise delineation of tumor mar- gins, or tumor differentiation from edema and/or treatment effects. In particular, contrast-enhanced MR images may underestimate lesion margins, which is critical for image-guided tumor resection, radio- therapy planning, and for assessing the response to chemotherapy. On the contrary, Diffusion Tensor Imaging (DTI) can identify peritumoral white-matter abnormalities [2], by detecting the presence of small areas with tumor-cell infiltration in WM around the edge of the gross tumor, as confirmed by image guided biopsies. In particular the tumor core is characterized by reduced anisotropy and increased isotropy, while, around this area, tumor infiltration shows increased isotropy, but normal anisotropy [3]. The aims of this study were: (a) to characterize pathological and healthy tissue in DTI datasets by 3D statistical texture analysis; (b) to develop a (semi-automated) Computer Assisted Detection (CAD) system for cerebral tumors, remotely accessed over the Internet

Semi-automated evaluation of structural characteristics and extension of cerebral gliomas using DTI-MR 3D Texture Analysis

The diffuse and infiltrative growth of cerebral gliomas is a major determinant of poor prognosis. Tumor cells invade surrounding tissues preferentially along white matter tracts, spreading beyond the abnormal area seen on conventional MR images. The detection and characterization of this microscopic infiltration in a non-invasive manner is of outstanding importance for surgical and radiation therapy planning or to assess response to chemotherapy. Diffusion tensor imaging can reveal larger peritumoral abnormalities in gliomas that are not apparent on conventional MR imaging, by detecting the presence of microscopic tumor cells infiltration in white matter around the edge of the gross tumor, as confirmed by image guided biopsies. The aims of this study are: 1) to characterize pathological and healthy tissue in DTI datasets by 3D statistical Texture Analysis, developing a semi-automated segmentation technique of cerebral tumors; 2) to correlate segmentation results with histopathological findings from specimens obtained from image-guided tumor biopsies