Dynamic Contrast Enhanced MRI (DCE-MRI) is today one of the most popular methods for tumor assessment. Several pharmacokinetic models have been proposed to analyze DCE-MRI. Most of them depend on an accurate arterial input function (AIF). We propose an automatic and versatile method to determine the AIF. The method has two stages, detection and segmentation, incorporating knowledge about artery...

Prostate cancer is one of the commonest cancers in the world. Dynamic contrast enhanced MRI (DCE-MRI) provides an opportunity for non-invasive diagnosis, staging, and treatment monitoring. Quantitative analysis of DCE-MRI relies on determination of an accurate arterial input function (AIF). Although several methods for automated AIF detection have been proposed in literature, none are optimized...

Recent studies have shown that dynamic contrast-enhanced pulmonary MR imaging (DCE-pMRI) is a very appropriate imaging technique for the clinical assessment of lung diseases. For the quantitative analysis of pulmonary blood flow (PBF), pulmonary blood volume (PBV), and mean transit time (MTT) an arterial input function (AIF) of the contrast agent entering the lung is required. The AIF is usuall...

Introduction: A major challenge in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is the need to provide a measured estimate of the arterial input function (AIF) in order to perform pharmacokinetic modeling analysis of the resulting data. Measurement of the AIF requires the presence of a major artery in the imaging field-of-view, a significantly faster sampling rate than for tis...

An automated method for determination of arterial input function (AIF) for rapid determination of cerebral perfusion with dynamic susceptibility contrast magnetic resonance (MR) imaging was derived. In 100 patients, the automated method was used to create images of relative blood flow, relative cerebral blood volume, and mean transit time. In 20 patients, the voxel chosen with the automated AIF...

Full quantitative analysis of brain PET data requires knowledge of the arterial input function into the brain. Such data are normally acquired by arterial sampling with corrections for delay and dispersion to account for the distant sampling site. Several attempts have been made to extract an image-derived input function (IDIF) directly from the internal carotid arteries that supply the brain a...

BACKGROUND The present study determines the feasibility of generating an average arterial input function (Avg-AIF) from a limited population of patients with neck nodal metastases to be used for pharmacokinetic modeling of dynamic contrast-enhanced MRI (DCE-MRI) data in clinical trials of larger populations. METHODS Twenty patients (mean age 50 years [range 27-77 years]) with neck nodal metas...

gadolinium images by deconvolution given the arterial input function, AIF(t), and tissue concentration, C(t),: C(t)=CBF٠[AIF(t)⊗R(t)], where R(t) is the tissue residue function. Often AIF is found by manually inspecting tracer concentration maps which is very time consuming and operator dependent. In our study we compare 5 methods of AIF automatic selection, including a novel one. The performan...

L. J. Bains, J. H. Naish, and D. L. Buckley Imaging Science and Biomedical Engineering, School of Cancer and Imaging Sciences, University of Manchester, Manchester, Greater Manchester, United Kingdom, University of Manchester Biomedical Imaging Institute, University of Manchester, Manchester, Greater Manchester, United Kingdom, Division of Medical Physics, University of Leeds, Leeds, United Kin...

Background and Purpose—Perfusion-weighted imaging maps are used to identify critical hypoperfusion in acute stroke. However, quantification of perfusion may depend on the choice of the arterial input function (AIF). Using quantitative positron emission tomography we evaluated the influence of the AIF location on maps of absolute and relative perfusion-weighted imaging to detect penumbral flow (...