Východiská: Dynamické, kontrastnou látkou sýtené MRI (DCE MRI) dokáže reflektovať zmeny vo vaskularite tkaniva, v permeabilite cievnych stien ale aj v difúzii v rámci extracelulárneho priestoru. Cieľom tejto štúdie bolo overiť aplikovateľnosť DCE MRI pri odlíšení benígnych a malígnych lézií prsníka. Pacienti a metódy: Z databázy bolo náhodne vybraných päť pacientov s malígnou a päť s benígnou léziou prsníka. Všetci pacienti podstúpili meranie v 3T MR skeneri vykonané pomocou prsníkovej cievky. Série T1-vážených MRI boli získané za použitia intravenózne aplikovanej kontrastnej látky. Následne bolo zmeraných 17 post kontrastných sérií snímok v priebehu 13 sekúnd. Všetky DCE MRI dáta boli vyhodnocované pomocou grafického balíka JIM. Pozorovali sme zmeny intenzity signálu počas doby akvizície – krivky dynamického sýtenia tkaniva kontrastnou látkou. Záver: Skúmali sme časti kriviek s najväčším nárastom intenzity signálu v rámci časového rámca. Pre ďalšie porovnanie sme použili hodnoty najväčších nárastov intenzity signálu medzi časovými intervalmi. Analýza týchto výsledkov viedla k pozorovaniu, že rozhranie medzi benígnymi a malígnymi léziami má relatívnu hodnotu 100. Navyše sme potvrdili významný rozdiel medzi uvedenými typmi lézií. Kľúčové slová: karcinóm prsníka – zobrazovanie magnetickou rezonanciou – kontrastná látka Táto štúdia bola podporená projektom MZSR, kód: 2012/31-UKMA-8 a projektom „Zvýšenie možností kariérneho rastu vo výskume a vývoji v oblasti lekárskych vied“, ITMS kód: 26110230067, spolufinancovanými zo zdrojov EÚ a Európskeho sociálneho fondu. Autoři deklarují, že v souvislosti s předmětem studie nemají žádné komerční zájmy. Redakční rada potvrzuje, že rukopis práce splnil ICMJE kritéria pro publikace zasílané do biomedicínských časopisů. Obdržané: 12. 9. 2014 Prijaté: 20. 10. 2014
Background: Dynamic contrast enhanced MRI (DCE MRI) is able to reflect changes in vascularity, vessel permeability and extracellular diffusion space of tissues. The goal of this study was to investigate the use of DCE MRI to differentiate benign and malignant breast lesions. Patients and Methods: From a database, five patients with malignant and five patients with benign lesions were randomly chosen. All patients underwent measurement in a 3T MR scanner using a breast coil. A series of T1-weighted MRI were performed using an intravenously delivered contrast agent. Then, 17 post‑contrast sets were acquired within a timeframe of 13 seconds. All DCE MRI data were evaluated using the JIM image analysis package. We observed changes in signal intensity over the acquisition time – curves of dynamic contrast enhancement. Conclusion: We investigated parts of the curves with the largest increase in signal intensity during the timeframe. For further comparison, we used values of the highest signal intensity increases between the timeframes. Analysis of these results led to the proposal that the threshold between benign and malignant lesion had a relative value of 100. Furthermore, there was a significant difference between these two types of lesions.
PURPOSE: One of the main obstacles for reliable quantitative dynamic contrast-enhanced (DCE) MRI is the need for accurate knowledge of the arterial input function (AIF). This is a special challenge for preclinical small animal applications where it is very difficult to measure the AIF without partial volume and flow artifacts. Furthermore, using advanced pharmacokinetic models (allowing estimation of blood flow and permeability-surface area product in addition to the classical perfusion parameters) poses stricter requirements on the accuracy and precision of AIF estimation. This paper addresses small animal DCE-MRI with advanced pharmacokinetic models and presents a method for estimation of the AIF based on blind deconvolution. METHODS: A parametric AIF model designed for small animal physiology and use of advanced pharmacokinetic models is proposed. The parameters of the AIF are estimated using multichannel blind deconvolution. RESULTS: Evaluation on simulated data show that for realistic signal to noise ratios blind deconvolution AIF estimation leads to comparable results as the use of the true AIF. Evaluation on real data based on DCE-MRI with two contrast agents of different molecular weights showed a consistence with the known effects of the molecular weight. CONCLUSION: Multi-channel blind deconvolution using the proposed AIF model specific for small animal DCE-MRI provides reliable perfusion parameter estimates under realistic signal to noise conditions.
- MeSH
- Algorithms MeSH
- Arteries diagnostic imaging MeSH
- Pharmacokinetics MeSH
- Contrast Media pharmacokinetics MeSH
- Humans MeSH
- Magnetic Resonance Imaging * MeSH
- Mice, Inbred BALB C MeSH
- Mice MeSH
- Necrosis pathology MeSH
- Perfusion MeSH
- Computer Simulation MeSH
- Image Processing, Computer-Assisted methods MeSH
- Signal-To-Noise Ratio MeSH
- Regression Analysis MeSH
- Reproducibility of Results MeSH
- Animals MeSH
- Check Tag
- Humans MeSH
- Mice MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
PURPOSE: The Tofts and the extended Tofts models are the pharmacokinetic models commonly used in dynamic contrast-enhanced MRI (DCE-MRI) perfusion analysis, although they do not provide two important biological markers, namely, the plasma flow and the permeability-surface area product. Estimates of such markers are possible using advanced pharmacokinetic models describing the vascular distribution phase, such as the tissue homogeneity model. However, the disadvantage of the advanced models lies in biased and uncertain estimates, especially when the estimates are computed voxelwise. The goal of this work is to improve the reliability of the estimates by including information from neighboring voxels. THEORY AND METHODS: Information from the neighboring voxels is incorporated in the estimation process through spatial regularization in the form of total variation. The spatial regularization is applied on five maps of perfusion parameters estimated using the tissue homogeneity model. Since the total variation is not differentiable, two proximal techniques of convex optimization are used to solve the problem numerically. RESULTS: The proposed algorithm helps to reduce noise in the estimated perfusion-parameter maps together with improving accuracy of the estimates. These conclusions are proved using a numerical phantom. In addition, experiments on real data show improved spatial consistency and readability of perfusion maps without considerable lowering of the quality of fit. CONCLUSION: The reliability of the DCE-MRI perfusion analysis using the tissue homogeneity model can be improved by employing spatial regularization. The proposed utilization of modern optimization techniques implies only slightly higher computational costs compared to the standard approach without spatial regularization.
- MeSH
- Algorithms MeSH
- Phantoms, Imaging MeSH
- Glioblastoma diagnostic imaging MeSH
- Contrast Media pharmacology MeSH
- Rats MeSH
- Magnetic Resonance Imaging * MeSH
- Brain diagnostic imaging MeSH
- Brain Neoplasms diagnostic imaging MeSH
- Perfusion MeSH
- Permeability MeSH
- Computer Simulation MeSH
- Image Processing, Computer-Assisted MeSH
- Signal-To-Noise Ratio MeSH
- Reproducibility of Results MeSH
- Animals MeSH
- Check Tag
- Rats MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
PURPOSE: Dynamic Contrast-Enhanced (DCE) MRI with 2nd generation pharmacokinetic models provides estimates of plasma flow and permeability surface-area product in contrast to the broadly used 1st generation models (e.g. the Tofts models). However, the use of 2nd generation models requires higher frequency with which the dynamic images are acquired (around 1.5 s per image). Blind deconvolution can decrease the demands on temporal resolution as shown previously for one of the 1st generation models. Here, the temporal-resolution requirements achievable for blind deconvolution with a 2nd generation model are studied. METHODS: The 2nd generation model is formulated as the distributed-capillary adiabatic-tissue-homogeneity (DCATH) model. Blind deconvolution is based on Parker's model of the arterial input function. The accuracy and precision of the estimated arterial input functions and the perfusion parameters is evaluated on synthetic and real clinical datasets with different levels of the temporal resolution. RESULTS: The estimated arterial input functions remained unchanged from their reference high-temporal-resolution estimates (obtained with the sampling interval around 1 s) when increasing the sampling interval up to about 5 s for synthetic data and up to 3.6-4.8 s for real data. Further increasing of the sampling intervals led to systematic distortions, such as lowering and broadening of the 1st pass peak. The resulting perfusion-parameter estimation error was below 10% for the sampling intervals up to 3 s (synthetic data), in line with the real data perfusion-parameter boxplots which remained unchanged up to the sampling interval 3.6 s. CONCLUSION: We show that use of blind deconvolution decreases the demands on temporal resolution in DCE-MRI from about 1.5 s (in case of measured arterial input functions) to 3-4 s. This can be exploited in increased spatial resolution or larger organ coverage.
- MeSH
- Algorithms MeSH
- Time Factors MeSH
- Contrast Media * pharmacokinetics MeSH
- Magnetic Resonance Imaging * methods MeSH
- Perfusion MeSH
- Publication type
- Journal Article MeSH
The present trend in dynamic contrast-enhanced MRI is to increase the number of estimated perfusion parameters using complex pharmacokinetic models. However, less attention is given to the precision analysis of the parameter estimates. In this paper, the distributed capillary adiabatic tissue homogeneity pharmacokinetic model is extended by the bolus arrival time formulated as a free continuous parameter. With the continuous formulation of all perfusion parameters, it is possible to use standard gradient-based optimization algorithms in the approximation of the tissue concentration time sequences. This new six-parameter model is investigated by comparing Monte-Carlo simulations with theoretically derived covariance matrices. The covariance-matrix approach is extended from the usual analysis of the primary perfusion parameters of the pharmacokinetic model to the analysis of the perfusion parameters derived from the primary ones. The results indicate that the precision of the estimated perfusion parameters can be described by the covariance matrix for signal-to-noise ratio higher than~20dB. The application of the new analysis model on a real DCE-MRI data set is also presented.
- MeSH
- Algorithms MeSH
- Image Interpretation, Computer-Assisted methods MeSH
- Contrast Media diagnostic use pharmacokinetics MeSH
- Humans MeSH
- Magnetic Resonance Angiography methods MeSH
- Models, Cardiovascular * MeSH
- Prostatic Neoplasms diagnosis physiopathology MeSH
- Computer Simulation MeSH
- Reproducibility of Results MeSH
- Blood Flow Velocity MeSH
- Sensitivity and Specificity MeSH
- Image Enhancement methods MeSH
- Check Tag
- Humans MeSH
- Male MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
PURPOSE: One of the main challenges in quantitative dynamic contrast-enhanced (DCE) MRI is estimation of the arterial input function (AIF). Usually, the signal from a single artery (ignoring contrast dispersion, partial volume effects and flow artifacts) or a population average of such signals (also ignoring variability between patients) is used. METHODS: Multi-channel blind deconvolution is an alternative approach avoiding most of these problems. The AIF is estimated directly from the measured tracer concentration curves in several tissues. This contribution extends the published methods of multi-channel blind deconvolution by applying a more realistic model of the impulse residue function, the distributed capillary adiabatic tissue homogeneity model (DCATH). In addition, an alternative AIF model is used and several AIF-scaling methods are tested. RESULTS: The proposed method is evaluated on synthetic data with respect to the number of tissue regions and to the signal-to-noise ratio. Evaluation on clinical data (renal cell carcinoma patients before and after the beginning of the treatment) gave consistent results. An initial evaluation on clinical data indicates more reliable and less noise sensitive perfusion parameter estimates. CONCLUSION: Blind multi-channel deconvolution using the DCATH model might be a method of choice for AIF estimation in a clinical setup.
- MeSH
- Algorithms * MeSH
- Models, Biological * MeSH
- Capillaries MeSH
- Carcinoma, Renal Cell blood supply MeSH
- Contrast Media MeSH
- Kidney blood supply MeSH
- Humans MeSH
- Magnetic Resonance Imaging methods MeSH
- Kidney Neoplasms blood supply MeSH
- Perfusion Imaging MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- MeSH
- Dynamic Contrast Enhanced Magnetic Resonance Imaging * methods MeSH
- Humans MeSH
- Carcinoma, Lobular * diagnostic imaging MeSH
- Aged MeSH
- Neoplasm Staging methods MeSH
- Check Tag
- Humans MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Case Reports MeSH
