Biomedical subjects

Fernando Calamante

Publications and source records attributed to Fernando Calamante.

7 recordsLinked to original sources

PubMed2005

Sickle cell disease: ischemia and seizures.

Although the prevalence of seizures in children with sickle cell disease (SCD) is 10 times that of the general population, there are few prospectively collected data on mechanism. With transcranial Doppler and magnetic resonance imaging (MRI) and angiography, we evaluated 76 patients with sickle cell disease, 29 asymptomatic and 47 with neurological complications (seizures, stroke, transient ischemic attack, learning difficulty, headaches, or abnormal transcranial Doppler), who also underwent bolus-tracking perfusion MRI. The six patients with recent seizures also had electroencephalography. Group comparisons (seizure, nonseizure, and asymptomatic) indicated that abnormal transcranial Doppler was more common in the seizure (4/6; 67%) and nonseizure (26/41; 63%) groups than in the asymptomatic (10/29; 34%) group (chi2; p = 0.045), but abnormal structural MRI (chi2; p = 0.7) or magnetic resonance angiography (chi2; p = 0.2) were not. Relative decreased cerebral perfusion was found in all seizure patients and in 16 of 32 of the remaining patients with successful perfusion MRI (p = 0.03). In the seizure patients, the perfusion abnormalities in five were ipsilateral to electroencephalographic abnormalities; one had normal electroencephalogram results. These findings suggest that vasculopathy and focal hypoperfusion may be factors in the development of sickle cell disease-associated seizures.

Adolescent↗

PubMed2004

Defining a local arterial input function for perfusion MRI using independent component analysis.

Quantification of cerebral blood flow (CBF) using dynamic-susceptibility contrast MRI relies on the deconvolution of the arterial input function (AIF), which is commonly estimated from the signal changes in a major artery. However, it has been shown that the presence of bolus delay/dispersion between the artery and the tissue of interest can be a significant source of error. These effects could be minimized if a local AIF were used, although the measurement of a local AIF can be problematic. This work describes a new methodology to define a local AIF using independent component analysis (ICA). The methodology was tested on data from patients with various cerebrovascular abnormalities and compared to the conventional approach of using a global AIF. The new methodology produced higher CBF and shorter mean transit time values (compared to the global AIF case) in areas with distorted AIFs, suggesting that the effects of delay/dispersion are minimized. The minimization of these effects using the calculated local AIF should lead to a more accurate quantification of CBF, which can have important implications for diagnosis and management of patients with cerebral ischemia.

Blood Volume↗

PubMed2004

Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution.

Diffusion-weighted magnetic resonance imaging can provide information related to the arrangement of white matter fibers. The diffusion tensor is the model most commonly used to derive the orientation of the fibers within a voxel. However, this model has been shown to fail in regions containing several fiber populations with distinct orientations. A number of alternative models have been suggested, such as multiple tensor fitting, q-space, and Q-ball imaging. However, each of these has inherent limitations. In this study, we propose a novel method for estimating the fiber orientation distribution directly from high angular resolution diffusion-weighted MR data without the need for prior assumptions regarding the number of fiber populations present. We assume that all white matter fiber bundles in the brain share identical diffusion characteristics, thus implicitly assigning any differences in diffusion anisotropy to partial volume effects. The diffusion-weighted signal attenuation measured over the surface of a sphere can then be expressed as the convolution over the sphere of a response function (the diffusion-weighted attenuation profile for a typical fiber bundle) with the fiber orientation density function (ODF). The fiber ODF (the distribution of fiber orientations within the voxel) can therefore be obtained using spherical deconvolution. The properties of the technique are demonstrated using simulations and on data acquired from a volunteer using a standard 1.5-T clinical scanner. The technique can recover the fiber ODF in regions of multiple fiber crossing and holds promise for applications such as tractography.

Algorithms↗

PubMed2003

Quantification of bolus-tracking MRI: Improved characterization of the tissue residue function using Tikhonov regularization.

Quantification of cerebral blood flow (CBF) and the tissue residue function (R) using bolus-tracking MRI requires deconvolution of the arterial input function (AIF). Currently, the most commonly used deconvolution method is singular value decomposition (SVD), which has been shown to produce accurate estimations of CBF. However, this method introduces unwanted oscillations in the time course of R, and there are situations in which the actual shape is of interest (e.g., in calculating flow heterogeneity and assessing bolus dispersion). In such cases, the conventional SVD method may no longer be suitable, and an alternative approach may be required. This work describes the implementation of Tikhonov regularization with the L-curve criterion to quantify CBF and obtain a better characterization of R. The methodology is tested on simulated and patient data, and the results are compared to those found using the conventional SVD approach. Although both methods produce similar CBF values, the deconvolved R shape obtained using SVD is dominated by oscillations and fails to characterize the shape in the presence of dispersion. On the other hand, the use of the proposed regularization method improves the characterization of the tissue residue function.

Carotid Stenosis↗

PubMed2003

Estimation of bolus dispersion effects in perfusion MRI using image-based computational fluid dynamics.

Bolus tracking magnetic resonance imaging (MRI) is a powerful technique for measuring perfusion, and is playing an increasing role in the investigation of acute stroke. However, limitations have been reported when assessing patients with steno-occlusive disease. The presence of a steno-occlusive disease in the artery may cause bolus dispersion, which has been shown to introduce significant errors in cerebral blood flow (CBF) quantification. Bolus dispersion is commonly described by a vascular transport function, but the function that properly characterizes the dispersion is unknown. A novel method to quantify bolus dispersion errors on perfusion measurements is presented. A realistic patient-specific model is constructed from anatomical and physiologic MR data, and the arterial blood flow pattern and the transport of the bolus of contrast agent are computed using finite element analysis. The methodology presented was used also to evaluate the accuracy of three simple vascular models. The methodology was tested on MR data from two normal subjects and two subjects with mild carotid artery stenosis. The estimated CBF errors were of the order of 15% to 20%. However, the presence of stenosis did not necessarily introduce larger dispersion (not only the geometrical model but also the particular physiologic conditions influence the degree of bolus dispersion). The method described will contribute to a better understanding of errors introduced by dispersion effects, to the assessment and validation of vascular models, and to the development of new methods for the correction of dispersion errors in CBF quantification.

Blood Flow Velocity↗

PubMed2003

Diffusion-weighted magnetic resonance imaging fibre tracking using a front evolution algorithm.

A novel technique is presented for estimating white matter connectivity in vivo using diffusion-weighted magnetic resonance imaging. The concept of a fibre orientation density function (ODF) is described, which characterises the uncertainty in the orientation of the underlying white matter fibres, given the set of diffusion-weighted signal intensities at the point of interest. The proposed algorithm is based on the evolution of a front from a seed region, using the information provided by the fibre ODF. Each point reached by the front is assigned an index of connectivity with the seed region. The algorithm was used to track various major white matter fibre pathways in two data sets acquired on the same healthy adult volunteer over separate occasions. Example tracks are shown to illustrate some of the properties of the algorithm, such as robustness to noise and branching capability. Finally, the dependence of the algorithm on the model used to derive the fibre ODF is discussed.

Adult↗

PubMed2002

Is quantification of bolus tracking MRI reliable without deconvolution?

Bolus tracking data obtained with paramagnetic intravascular tracers are commonly analyzed and quantified by the direct measurement of properties of the tissue concentration-time curve (e.g., time to peak (TTP)). The measurement of these "summary parameters" is used as an accessible alternative approach to the complex deconvolution procedure, and provides indirect measures of perfusion. However, summary parameters do not take into account differences in arterial input functions (AIFs) or residue functions (R(t)) between patients or studies. Simulations were performed to assess the variability of summary parameters over a realistic range of AIFs and for differing R(t), to establish whether they can be used as reliable measures of tissue perfusion status. Results showed that the value of each summary parameter investigated is highly dependent upon both the AIF and R(t). The referencing of summary parameters to their corresponding value in the AIF or in normal tissue is a method commonly used to normalize results, but this approach did not lead to any measures that were independent of both the AIF and R(t) in this study. The results presented here show that the use of summary parameters requires considerable caution, since tissue or patient types can easily be incorrectly classified due to the effect of variations in patient AIF and R(t).

Adult↗