Region of interest

About: Region of interest is a research topic. Over the lifetime, 7895 publications have been published within this topic receiving 104479 citations. The topic is also known as: ROI.

Multiwavelength three-dimensional near-infrared tomography of the breast: initial simulation, phantom, and clinical results

Abstract: Three-dimensional (3D), multiwavelength near-infrared tomography has the potential to provide new physiological information about biological tissue function and pathological transformation. Fast and reliable measurements of multiwavelength data from multiple planes over a region of interest, together with adequate model-based nonlinear image reconstruction, form the major components of successful estimation of internal optical properties of the region. These images can then be used to examine the concentration of chromophores such as hemoglobin, deoxyhemoglobin, water, and lipids that in turn can serve to identify and characterize abnormalities located deep within the domain. We introduce and discuss a 3D modeling method and image reconstruction algorithm that is currently in place. Reconstructed images of optical properties are presented from simulated data, measured phantoms, and clinical data acquired from a breast cancer patient. It is shown that, with a relatively fast 3D inversion algorithm, useful images of optical absorption and scatter can be calculated with good separation and localization in all cases. It is also shown that, by use of the calculated optical absorption over a range of wavelengths, the oxygen saturation distribution of a tissue under investigation can be deduced from oxygenated and deoxygenated hemoglobin maps. With this method the reconstructed tumor from the breast cancer patient was found to have a higher oxy-deoxy hemoglobin concentration and also a higher oxygen saturation level than the background, indicating a ductal carcinoma that corresponds well to histology findings.

Analysis of left ventricular function from multiple gated acquisition cardiac blood pool imaging. Comparison to contrast angiography.

Abstract: Global ventricular function was evaluated by both multiple gated cardiac blood pool scans (MUGA) and contrast ventriculograms in a group of 17 patients with suspected coronary artery disease. The contrast ventriculograms were analyzed frame by frame to generate a volume versus time curve for each patient, while the tracer data were analyzed by two methods: 1) the standard method, in which the left ventricle is identified on the end-diastolic frame and the background corrected activity under the region of interest obtained from the entire cardiac cycle, and displayed as a time versus activity curve; and 2) by a semi-automatic method in which the computer applies a threshold detection program to define the ventricular borders, and activity in the chamber at each point in the cardiac cycle is defined after background correction. The tracer data in each patient were analyzed independently by four observers. The tracer data correlated with the contrast data on a point by point basis r = 0.87 for the standard method, and 0.93 for the semi-automatic technique. An F test of variance revealed the semi-automatic method superior to the standard approach (P less than 0.05).

MAPS : A set of software tools for analysis and visualization of 3D X-ray fluorescence data sets

Abstract: X-ray fluorescence (XRF) imaging of biological cells typically involves the creation (and subsequent analysis) of 3-dimensional (3D) data sets, where at each scan position the full spectrum is recorded. This allows one to later process the data in a variety of different approaches, e.g., by spectral region of interest (ROI E ) summation with or without background subtraction, principal component analysis, or fitting. Additionally, it is possible to sum up the per pixel spectra over selected spatial ROIs so as to improve the photon statistics in such a spectrum. A software package is presented here that implements these functions to provide a toolset for the analysis of 3D XRF data sets.

Quantification of FDG PET studies using standardised uptake values in multi-centre trials: effects of image reconstruction, resolution and ROI definition parameters

Abstract: Standardised uptake values (SUVs) depend on acquisition, reconstruction and region of interest (ROI) parameters. SUV quantification in multi-centre trials therefore requires standardisation of acquisition and analysis protocols. However, standardisation is difficult owing to the use of different scanners, image reconstruction and data analysis software. In this study we evaluated whether SUVs, obtained at three different institutes, may be directly compared after calibration and correction for inter-institute differences. First, an anthropomorphic thorax phantom containing variously sized spheres and activities, simulating tumours, was scanned and processed in each institute to evaluate differences in scanner calibration. Secondly, effects of image reconstruction and ROI method on recovery coefficients were studied. Next, SUVs were derived for tumours in 23 subjects. Of these 23 patients, four and ten were scanned in two institutes on an HR+ PET scanner and nine were scanned in one institute on an ECAT EXACT PET scanner. All phantom and clinical data were reconstructed using iterative reconstruction with various iterations, with both measured (MAC) and segmented attenuation correction (SAC) and at various image resolutions. Activity concentrations (AC) or SUVs were derived using various ROI isocontours. Phantom data revealed differences in SUV quantification of up to 30%. After application-specific calibration, recovery coefficients obtained in each institute were equal to within 15%. Varying the ROI isocontour value resulted in a predictable change in SUV (or AC) for both phantom and clinical data. Variation of image resolution resulted in a predictable change in SUV quantification for large spheres/tumours (>5 cc) only. For smaller tumours (<2 cc), differences of up to 40% were found between high (7 mm) and low (10 mm) resolution images. Similar differences occurred when data were reconstructed with a small number of iterations. Finally, no significant differences between MAC and SAC reconstructed data were observed, except for tumours near the diaphragm. Standardisation of acquisition, reconstruction and ROI methods is preferred for SUV quantification in multi-centre trials. Small unavoidable differences in methodology can be accommodated by performing a phantom study to assess inter-institute correction factors.