scholarly journals Blind Source Separation of Hemodynamics from Magnetic Resonance Perfusion Brain Images Using Independent Factor Analysis

2010 ◽  
Vol 2010 ◽  
pp. 1-9 ◽  
Author(s):  
Yen-Chun Chou ◽  
Chia-Feng Lu ◽  
Wan-Yuo Guo ◽  
Yu-Te Wu
Keyword(s):  
Factor Analysis ◽  
Magnetic Resonance ◽  
Cerebral Blood Volume ◽  
Mean Transit Time ◽  
Normal Subjects ◽  
Independent Factor ◽  
Time Curve ◽  
Magnetic Resonance Perfusion ◽  
Independent Factor Analysis ◽  
Signal Time

Perfusion magnetic resonance brain imaging induces temporal signal changes on brain tissues, manifesting distinct blood-supply patterns for the profound analysis of cerebral hemodynamics. We employed independent factor analysis to blindly separate such dynamic images into different maps, that is, artery, gray matter, white matter, vein and sinus, and choroid plexus, in conjunction with corresponding signal-time curves. The averaged signal-time curve on the segmented arterial area was further used to calculate the relative cerebral blood volume (rCBV), relative cerebral blood flow (rCBF), and mean transit time (MTT). The averaged ratios for rCBV, rCBF, and MTT between gray and white matters for normal subjects were congruent with those in the literature.

scholarly journals Clinical and Radiological Correlates of Reduced CBF Measured Using MRI

Stroke ◽  
2001 ◽  
Vol 32 (suppl_1) ◽  
pp. 317-318
Author(s):  
Vincent N Thijs ◽  
Tobias Neumann-Haefelin ◽  
Michael E Moseley ◽  
Michael P Marks ◽  
Gregory W Albers
Keyword(s):  
Symptom Onset ◽  
Cerebral Blood Volume ◽  
Arterial Input Function ◽  
Mean Transit Time ◽  
Input Function ◽  
Lesion Volume ◽  
Time Curve ◽  
Measured Concentration ◽  
Ischemic Lesion ◽  
Adc Values

11 Background and purpose Methods for determining CBF using IV bolus tracking MRI have recently become available. Reduced apparent diffusion coefficient (ADC) values of brain tissue are associated with reductions in regional cerebral blood flow (rCBF). We studied the clinical and radiological features of patients with severe reductions of rCBF on MRI and analysed the relationship between reduced rCBF and ADC. Methods We studied patients with non-lacunar acute ischemic stroke in whom PWI and DWI MRI were performed within 7 hours after symptom onset. A PWI>DWI mismatch of >20% was required. Maps of rCBF, cerebral blood volume (rCBV) and mean transit time (rMTT) were generated after deconvoluting the measured concentration-time curve with the arterial input function using singular value decomposition. The ischemic lesion was outlined on the MTT map and the region of interest (ROI) transferred to the rCBF and rCBV map. ADC-maps were calculated. ADC lesions were defined as regions with ADC values ≤ 550 μm m2/sec. We compared the characteristics of patients with ischemic lesions that had a relative CBF of <50% to the contralateral hemisphere to patients with lesions that had relative CBF of >50%. Characteristics analysed included age, time to MRI, baseline NIHSS, mean ADC, DWI lesion volume, PWI lesion volume and absolute mismatch volume. Results Fifteen patients with an initial PWI>DWI mismatch of >20% were included. Ten had lesions with rCBF of >50% (median 60%) and five patients had rCBF of <50% (median 27.7%). Patients with rCBF <50% had lower ADC values (median 431 μmm2/sec versus 506 μ mm2/sec, p=0.028), larger DWI volumes (median 75.6 cm 3 versus 8.6 cm 3 , p=0.001) and larger PWI lesions as defined by the MTT volume (median 193 cm 3 versus 69 cm 3 , p=0.028) and more severe baseline NIHSS scores (median 18 versus 9, p=0.019). The rMTT and rCBV of the lesions were similar in both groups, as were the age, the absolute mismatch volume and the time from symptom onset to MRI. Conclusion These data indicate that ischemic lesions with severe CBF reductions, measured with new MRI techniques, are associated with a lower mean ADC, larger DWI and PWI lesion volumes and a higher NIHSS score.

Dexamethasone and Enhancing Solitary Cerebral Mass Lesions: Alterations in Perfusion and Blood-tumor Barrier Kinetics Shown by Magnetic Resonance Imaging

Neurosurgery ◽  
2006 ◽  
Vol 58 (4) ◽  
pp. 640-646 ◽  
Author(s):  
Iain D. Wilkinson ◽  
David A. Jellineck ◽  
David Levy ◽  
Frederik L. Giesel ◽  
Charles A. J. Romanowski ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Blood Volume ◽  
Transit Time ◽  
Cerebral Blood Volume ◽  
Mean Transit Time ◽  
Resonance Imaging ◽  
Regional Cerebral Blood ◽  
Regional Cerebral Blood Volume ◽  
First Moment

Abstract OBJECTIVE: Glucocorticoid analogues are often administered to patients with intracranial space-occupying lesions. Clinical response can be dramatic, but the neurophysiological response is not well documented. This study sought to investigate the blood-lesion barrier, blood-brain barrier, and cerebral perfusion characteristics of patients who have undergone such therapy using magnetic resonance imaging. METHODS: Seventeen patients with intracranial mass-enhancing lesions underwent magnetic resonance imaging before and after 3 days of high-dose dexamethasone therapy. Assessments of blood-lesion barrier and blood-brain barrier integrity were based on a dynamic T1-weighted exogenous contrast technique that yielded the normalized maximal change in contrast uptake (T1-uptake). Perfusion was assessed using a dynamic T2*-weighted exogenous contrast technique to yield relative regional cerebral blood volume and first-moment mean transit time. Comparisons were made in T1-uptake, regional cerebral blood volume, and first-moment mean transit time of both enhancing lesion and contralateral normal-appearing white matter (CNAWM) obtained before and after dexamethasone. RESULTS: Significant reduction in T1-uptake was observed (19% decrease, P &lt; 0.005) within enhancing pathological tissue, whereas no significant alteration was detected in CNAWM. Regional cerebral blood volume was significantly reduced in both enhancing tissue (28% decrease, P &lt; 0.005) and in CNAWM (20% decrease, P &lt; 0.001). Bolus first-moment mean transit time significantly increased (2.0 s prolongation, P &lt; 0.05) in CNAWM, whereas there was no significant change (1.4 s prolongation, P &gt; 0.05) within enhancing tissue. CONCLUSION: Glucocorticoid-analogue therapy not only affects the permeability of the blood-lesion barrier and lesion blood volume but also affects blood flow within normal-appearing contralateral parenchyma. There is a need for controls in steroid therapy in magnetic resonance imaging studies, which involve assessments of cerebrovascular function.

scholarly journals The Application of Diffusion- and Perfusion-Weighted Magnetic Resonance Imaging in the Diagnosis and Therapy of Acute Cerebral Infarction

2006 ◽  
Vol 2006 ◽  
pp. 1-11 ◽  
Author(s):  
Ying Han ◽  
Enzhong Li ◽  
Jie Tian ◽  
Jian Chen ◽  
Huifang Wang ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Diffusion Coefficient ◽  
Magnetic Resonance ◽  
Cerebral Blood Volume ◽  
Mean Transit Time ◽  
Resonance Imaging ◽  
Stroke Patients ◽  
Apparent Diffusion ◽  
Weighted Magnetic Resonance Imaging ◽  
Adc Map

Diffusion- and perfusion-weighted magnetic resonance imaging (DWI and PWI) was applied for stroke diagnose in 120 acute (<48h) ischemic stroke patients. At hyperacute (<6h) stage, it is difficult to find out the infarction zone in conventional T1 or T2 image, but it is easy in DWI, apparent diffusion coefficient (ADC) map; when at 3–6-hour stage it is also easy in PWI, cerebral blood flow (CBF) map, cerebral blood volume (CBV) map, and mean transit time (MTT) map; at acute (6–48 h) stage, DWI or PWI is more sensitive than conventional T1 or T2 image too. Combining DWI with ADC, acute and chronic infarction can be distinguished. Besides, penumbra which should be developed in meaning was used as an indication or to evaluate the therapeutic efficacy. There were two cases (<1.5h) that broke the model of penumbra because abnormity was found in DWI but not that in PWI, finally they recovered without any sequela.

Source separation in astrophysical maps using independent factor analysis

2003 ◽  
Vol 16 (3-4) ◽  
pp. 479-491 ◽  
Author(s):  
Ercan E. Kuruoğlu ◽  
Luigi Bedini ◽  
Maria T. Paratore ◽  
Emanuele Salerno ◽  
Anna Tonazzini
Keyword(s):  
Factor Analysis ◽  
Source Separation ◽  
Independent Factor ◽  
Independent Factor Analysis

scholarly journals Choosing the number of factors in independent factor analysis model

2005 ◽  
Vol 2 (2) ◽  
Author(s):  
Cinzia Viroli
Keyword(s):  
Factor Analysis ◽  
Latent Variables ◽  
Latent Variable ◽  
Information Criteria ◽  
Ratio Test ◽  
Independent Factor ◽  
Analysis Model ◽  
Variable Model ◽  
Factor Analysis Model ◽  
Independent Factor Analysis

Independent Factor Analysis (IFA) has recently been proposed in the signal processing literature as a way to model a set of observed variables through linear combinations of hidden independent ones plus a noise term. Despite the peculiarity of its origin the method can be framed within the latent variable model domain and some parallels with the ordinary factor analysis can be drawn. If no prior information on the latent structure is available a relevant issue concerns the correct specification of the model. In this work some methods to detect the number of significant latent variables are investigated. Moreover, since the method defines a probability density function for the latent variables by mixtures of gaussians, the correct number of mixture components must also be determined. This issue will be treated according to two main approaches. The first one amounts to carry out a likelihood ratio test. The other one is based on a penalized form of the likelihood, that leads to the so called information criteria. Some simulations and empirical results on real data sets are finally presented.

scholarly journals Noisy independent factor analysis model for density estimation and classification

2010 ◽  
Vol 4 (0) ◽  
pp. 707-736 ◽  
Author(s):  
Umberto Amato ◽  
Anestis Antoniadis ◽  
Alexander Samarov ◽  
Alexandre B. Tsybakov
Keyword(s):  
Factor Analysis ◽  
Density Estimation ◽  
Independent Factor ◽  
Analysis Model ◽  
Factor Analysis Model ◽  
Independent Factor Analysis
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