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Makkat S, Sourbron S, Luypaert R, Stadnik T, Dujardin M, Dewilde V, De Ridder F, de Mey J, Dicken V, Bornemann L, Kuhnigk J, Krass S, Peitgen H, Izzo F, Langenberger H, Gruenberger T, Plank C, Laengle F, Schindl M, Schima W, Harave S, Higginson A, Ward S, Wenaden A, Hay E, De Lutio di Castelguidone E, De Rosa V, Petrillo A, McAuley G, Govender P, Colville J, Lyburn I, Worsley D, Torreggiani W, Vallone P, Gallipoli A, Schueller G, Kettenbach J, Sedivy R, Lammer J, Rockall A, Goode A, Sahdev A, Sohaib S, Shepherd J, Reznek R, Babar S, Williamson R, McIndoe A, Catalano O, Nunziata A, Petrosino T, Rubulotta R, Angelini A, Castellucci P, Davies M, Miles E, Humphreys M, Clark C, Nutting C, Della Vigna P, Preda L, Orsi F, Sbanotto A, Banfi M, Bonomo G, Monfardini L, Bellomi M, Shirazi H, Loo B, Kochanski J, Choe W, Haus B, Yau J, Pinto H, Goode R, Segall G, Le Q, Capirci C, Galeotti F, Crepaldi G, Mandoliti G, Polico C, Griffin N, Paghari C, Sharma B, Gregianin M, Cracco E, Dei Rossi F, Gallan M, Nicastro F, Menaldo G, Gravili S, Rombi B, Guido A, Ntreta M, Bunkheila F, Barbieri E, McGibney C, Chan B, Pereira A, Abbas A, Paul N, Jaffray D, Vallis K, Rubello D, Piotto A, Pavan N, Rampin L, Salvatori M, Fanti S, Mariani G, Busnardo B, Pelizzo M, Chandratreya L, Cobby M, Watura R, Quint L, Kretschmer M, Chang A, Sandomenico F, Mattace Raso M, Carone G, Fiore F, Filice S, Iovino M, Siani A
Cancer imaging : the official publication of the International Cancer Imaging Society · 2005 · PMC1665321
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Pixel-by-pixel deconvolution analysis of dynamic contrast-enhanced MRI data generates regional perfusion parameters that differentiate malignant from benign breast tumors, demonstrating added value in tumor characterization.

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This study investigated the added value of quantifying perfusion parameters in human breast tumors using dynamic contrast-enhanced MRI. The researchers applied a routine MR mammography protocol followed by a second bolus injection to generate parametric maps of tumor blood flow, extracellular volume, and mean transit time through pixel-by-pixel deconvolution analysis. Preliminary results from four malignant and two benign tumors indicated that these perfusion metrics successfully differentiated tumors from surrounding tissue, with malignant lesions exhibiting higher blood flow and extracellular volume alongside lower mean transit times compared to benign ones. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Full text 1,594 characters · extracted from pmc-nxml · 4 sections · click to expand

Purpose

Previously we demonstrated the feasibility of quantifying perfusion parameters in human breast tumours using a deconvolution analysis of dynamic contrast-enhanced (DCE) MRI. Now we investigate the added value of these parameters in tumour characterisation.

Results

In the initial data of four malignant and two benign tumours, the parametric maps clearly differentiated tumours from the surrounding breast tissue. Mean TBF in the malignant tumours (38.4 ml /100 g per min) was consistent with the literature values. TBF and TEV values were higher and MTT values were lower in the malignant tumours compared to the benign ones.

Materials

The routine MR mammography protocol was first applied, which included whole breast DCE MRI (0.1 mmol /kg Gd-DTPA 10 s /dynamic) sequence. The slice in which the lesion enhanced maximally was located in the subtracted DCE MR images. A second bolus of 0.1 mmol /kg Gd-DTPA was injected and a dynamic single slice Turboflash acquisition (600 dynamics with a temporal resolution of 0.3 s) was performed at that slice position. The signals were first converted to tracer concentrations, which was then deconvolved pixel-by-pixel, with an arterial input function in the aorta. Finally, the parametric maps of tumour blood flow (TBF), tumour extracellular volume (TEV) and mean transit time (MTT) were generated. Perfusion values were compared with literature values.

Conclusion

Preliminary results suggest that a pixel-by-pixel deconvolution analysis of the DCE MRI data of breast tumours provides regional perfusion parameters, which can be a valuable tool in the tumour characterisation.

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last seen: 2026-09-27T09:11:36.575535+00:00