Famis: A software package for functional feature extraction from biomedical multidimensional images

Frouin, F.; Bazin, J.; Paola, M. Di; Jolivet, Odile; Paola, R. Di

doi:10.1016/0895-6111(92)90121-o

Cited by 35 publications

(19 citation statements)

References 17 publications

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“…Our proposed approach to analyze the DCE-MRI data has several advantages compared with the other model-free techniques which are based on factor analysis, 8,9 ICA, 10 or extracting characteristic features, such as time to peak or maximum enhancement, from the DCE curves. 30 Foremost, for each tumor type or body site, a set of PCs used in calculation of the coefficient maps can be achieved offline and are available before a new patient is scanned.…”

Section: Discussionmentioning

confidence: 99%

“…Then, using the probability membership function obtained in the development step, we calculate the cPC-defined tumor subvolumes by Eqs. (8) or (9). Finally, a change of the subvolume from pretherapy to during or post-therapy is determined (Fig.…”

Section: E Usage Phasementioning

confidence: 99%

“…Also, computing the physiological parameters by fitting the DCE-MRI data into the PK models is a time consuming process, which is inadequate for real-time decision making support in adaptive radiation therapy, especially to handle the number of patients and the clinical workload. Hence, a modelfree approach to analyze the DCE-MRI data, e.g., the methods based on factor analysis, 8,9 independent component analysis (ICA), 10 and principal component analysis (PCA), [11][12][13][14] could potentially facilitate the development of the real-time decision-making supportive tools in diagnosis and therapy assessment. PCA has shown the potential to be a very robust and fast technique in analyzing the DCE-MRI data, [11][12][13][14][15][16] and especially in prostate [12][13][14] and breast 15,16 cancer.…”

Section: Introductionmentioning

confidence: 99%

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DCE-MRI defined subvolumes of a brain metastatic lesion by principle component analysis and fuzzy-c-means clustering for response assessment of radiation therapy

Farjam¹,

Tsien²,

Lawrence³

et al. 2013

Med. Phys.

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Purpose: To develop a pharmacokinetic modelfree framework to analyze the dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) data for assessment of response of brain metastases to radiation therapy. Methods: Twenty patients with 45 analyzable brain metastases had MRI scans prior to whole brain radiation therapy (WBRT) and at the end of the 2-week therapy. The volumetric DCE images covering the whole brain were acquired on a 3T scanner with approximately 5 s temporal resolution and a total scan time of about 3 min. DCE curves from all voxels of the 45 brain metastases were normalized and then temporally aligned. A DCE matrix that is constructed from the aligned DCE curves of all voxels of the 45 lesions obtained prior to WBRT is processed by principal component analysis to generate the principal components (PCs). Then, the projection coefficient maps prior to and at the end of WBRT are created for each lesion. Next, a pattern recognition technique, based upon fuzzy-c-means clustering, is used to delineate the tumor subvolumes relating to the value of the significant projection coefficients. The relationship between changes in different tumor subvolumes and treatment response was evaluated to differentiate responsive from stable and progressive tumors. Performance of the PCdefined tumor subvolume was also evaluated by receiver operating characteristic (ROC) analysis in prediction of nonresponsive lesions and compared with physiological-defined tumor subvolumes. Results: The projection coefficient maps of the first three PCs contain almost all response-related information in DCE curves of brain metastases. The first projection coefficient, related to the area under DCE curves, is the major component to determine response while the third one has a complimentary role. In ROC analysis, the area under curve of 0.88 ± 0.05 and 0.86 ± 0.06 were achieved for the PC-defined and physiological-defined tumor subvolume in response assessment. Conclusions: The PC-defined subvolume of a brain metastasis could predict tumor response to therapy similar to the physiological-defined one, while the former is determined more rapidly for clinical decision-making support.

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Section: Discussionmentioning

confidence: 99%

Section: E Usage Phasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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DCE-MRI defined subvolumes of a brain metastatic lesion by principle component analysis and fuzzy-c-means clustering for response assessment of radiation therapy

Farjam¹,

Tsien²,

Lawrence³

et al. 2013

Med. Phys.

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“…Les séquences corrigées avec les méthodes manuelles et automatiques sont ensuite comparées à la séquence non corrigée. Pour étudier la précision de l'atténuation estimée, une image synthé-tique regroupant les cinétiques existantes dans chaque séquence temporelle est crée par analyse factorielle (AFSIM) avec deux facteurs [3].…”

Section: éValuation De La Méthodeunclassified

“…Notre équipe a récemment développé une méthode d'estimation de l'atténuation basée sur un modèle mathématique de régularisation des cartes d'atténuation [2]. Dans ce travail, nous étudions la précision et la robustesse du modèle mathématique utilisé par le biais de l'analyse factorielle des séquences d'images médicales (AFSIM) [3] qui résume les séries temporelles en quelques images représentatives permettant une comparaison efficace des séquences avant et après correction.…”

Section: Introductionunclassified

Évaluation par AFSIM d’une méthode régularisée de correction de l’atténuation en imagerie de contraste ultrasonore

Pellot‐Barakat

Mulé

Cesare

et al. 2009

IRBM

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Using an adaptive semiautomated self‐evaluated registration technique to analyze MRI data for myocardial perfusion assessment

Delzescaux

Frouin

Cesare

et al. 2003

Magnetic Resonance Imaging

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Purpose: To validate the adaptive semiautomated self-evaluated registration technique (ASSERT) followed by factor analysis of medical image sequence (FAMIS) for analyzing myocardial perfusion using magnetic resonance imaging (MRI) images. Materials and Methods:Eleven patients having a significant stenosis of at least one coronary artery detected by coronarography were examined by thallium tomoscintigraphy and perfusion MRI (first-pass of Gd-DTPA-BMA) at rest and under pharmacologic stress. The MRI images were analyzed by ASSERT to correct for rigid motion in the acquisition plane and to reject those images that were severely deformed or acquired outside the slice plane. The images thus obtained were analyzed by FAMIS. The resulting factor images representing myocardial perfusion were read to identify the ischemic territories corresponding to left anterior descending coronary arteries and right coronary arteries.Results: ASSERT allowed automatic correction for motion and the exclusion of images that could not be registered. The ischemic territories, identified from the factor images of the myocardium, agreed with those identified by coronarography and tomoscintigraphy for 20 of the 22 territories. Conclusion:The results demonstrate the feasibility of analyzing myocardial perfusion using MRI acquisition and treating the resulting images by ASSERT and FAMIS. Extending this method to multislice examinations will enable evaluation of the perfusion of the whole myocardium.

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Famis: A software package for functional feature extraction from biomedical multidimensional images

Cited by 35 publications

References 17 publications

DCE-MRI defined subvolumes of a brain metastatic lesion by principle component analysis and fuzzy-c-means clustering for response assessment of radiation therapy

DCE-MRI defined subvolumes of a brain metastatic lesion by principle component analysis and fuzzy-c-means clustering for response assessment of radiation therapy

Évaluation par AFSIM d’une méthode régularisée de correction de l’atténuation en imagerie de contraste ultrasonore

Using an adaptive semiautomated self‐evaluated registration technique to analyze MRI data for myocardial perfusion assessment

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