scholarly journals Partial volume effect (PVE) on the arterial input function (AIF) in T 1 -weighted perfusion imaging and limitations of the multiplicative rescaling approach

2009 ◽  
Vol 62 (4) ◽  
pp. 1055-1059 ◽  
Author(s):  
Adam E. Hansen ◽  
Henrik Pedersen ◽  
Egill Rostrup ◽  
Henrik B.W. Larsson
Keyword(s):  
Perfusion Imaging ◽  
Arterial Input Function ◽  
Input Function ◽  
Volume Effect ◽  
Partial Volume Effect ◽  
Partial Volume

scholarly journals Automated Determination of Arterial Input Function for Dynamic Susceptibility Contrast MRI from Regions around Arteries Using Independent Component Analysis

2016 ◽  
Vol 2016 ◽  
pp. 1-10
Author(s):  
Sharon Chen ◽  
Yu-Chang Tyan ◽  
Jui-Jen Lai ◽  
Chin-Ching Chang
Keyword(s):  
Independent Component Analysis ◽  
Contrast Agent ◽  
Arterial Input Function ◽  
Input Function ◽  
Volume Effect ◽  
Partial Volume Effect ◽  
Independent Component ◽  
Dynamic Susceptibility ◽  
Dynamic Susceptibility Contrast ◽  
Susceptibility Contrast

Purpose.Quantitative cerebral blood flow (CBF) measurement using dynamic susceptibility contrast- (DSC-) MRI requires accurate estimation of the arterial input function (AIF). The present work utilized the independent component analysis (ICA) method to determine the AIF in the regions adjacent to the middle cerebral artery (MCA) by the alleviated confounding of partial volume effect.Materials and Methods.A series of spin-echo EPI MR scans were performed in 10 normal subjects. All subjects received 0.2 mmol/kg Gd-DTPA contrast agent. AIFs were calculated by two methods:(1)the region of interest (ROI) selected manually and(2)weighted average of each component selected by ICA (weighted-ICA). The singular value decomposition (SVD) method was then employed to deconvolve the AIF from the tissue concentration time curve to obtain quantitative CBF values.Results. The CBF values calculated by the weighted-ICA method were 41.1 ± 4.9 and 22.1 ± 2.3 mL/100 g/min for cortical gray matter (GM) and deep white matter (WM) regions, respectively. The CBF values obtained based on the manual ROIs were 53.6 ± 12.0 and 27.9 ± 5.9 mL/100 g/min for the same two regions, respectively.Conclusion.The weighted-ICA method allowed semiautomatic and straightforward extraction of the ROI adjacent to MCA. Through eliminating the partial volume effect to minimum, the CBF thus determined may reflect more accurate physical characteristics of theT2⁎signal changes induced by the contrast agent.

Partial volume effect of SPECT images in PRRT with 177Lu labelled somatostatin analogues: A practical solution

2019 ◽  
Vol 57 ◽  
pp. 153-159 ◽  
Author(s):  
Domenico Finocchiaro ◽  
Salvatore Berenato ◽  
Elisa Grassi ◽  
Valentina Bertolini ◽  
Gastone Castellani ◽  
...  
Keyword(s):  
Somatostatin Analogues ◽  
Volume Effect ◽  
Partial Volume Effect ◽  
Practical Solution ◽  
Partial Volume ◽  
Spect Images

Spectrum separation resolves partial-volume effect of MRSI as demonstrated on brain tumor scans

2008 ◽  
Vol 21 (10) ◽  
pp. 1030-1042 ◽  
Author(s):  
Yuzhuo Su ◽  
Sunitha B. Thakur ◽  
Karimi Sasan ◽  
Shuyan Du ◽  
Paul Sajda ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Volume Effect ◽  
Partial Volume Effect ◽  
Partial Volume

Segmentation of the skull in MRI volumes using a deformable model and taking the partial volume effect into account

1999 ◽  
Author(s):  
Hilmi Rifai ◽  
Isabelle Bloch ◽  
Seth A. Hutchinson ◽  
Joe Wiart ◽  
Line Garnero
Keyword(s):  
Volume Effect ◽  
Partial Volume Effect ◽  
Deformable Model ◽  
Partial Volume

scholarly journals Ventricle Boundary in CT: Partial Volume Effect and Local Thresholds

2010 ◽  
Vol 2010 ◽  
pp. 1-6 ◽  
Author(s):  
Ihar Volkau ◽  
Fiftarina Puspitasari ◽  
Wieslaw L. Nowinski
Keyword(s):  
Computed Tomography ◽  
Cerebrospinal Fluid ◽  
Grey Matter ◽  
Ground Truth ◽  
Gaussian Mixture ◽  
Volume Effect ◽  
Partial Volume Effect ◽  
Partial Volume ◽  
Image Histogram ◽  
Global Threshold

We present a mathematical frame to carry out segmentation of cerebrospinal fluid (CSF) of ventricular region in computed tomography (CT) images in the presence of partial volume effect (PVE). First, the image histogram is fitted using the Gaussian mixture model (GMM). Analyzing the GMM, we find global threshold based on parameters of distributions for CSF, and for the combined white and grey matter (WGM). The parameters of distribution of PVE pixels on the boundary of ventricles are estimated by using a convolution operator. These parameters are used to calculate local thresholds for boundary pixels by the analysis of contribution of the neighbor pixels intensities into a PVE pixel. The method works even in the case of an almost unimodal histogram; it can be useful to analyze the parameters of PVE in the ground truth provided by the expert.

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