OBJECTIVE: An extension of single- and multi-channel blind deconvolution is presented to improve the estimation of the arterial input function (AIF) in quantitative dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). METHODS: The Lucy-Richardson expectation-maximization algorithm is used to obtain estimates of the AIF and the tissue residue function (TRF). In the first part of the algorithm, nonparametric estimates of the AIF and TRF are obtained. In the second part, the decaying part of the AIF is approximated by three decaying exponential functions with the same delay, giving an almost noise free semi-parametric AIF. Simultaneously, the TRF is approximated using the adiabatic approximation of the Johnson-Wilson (aaJW) pharmacokinetic model. RESULTS: In simulations and tests on real data, use of this AIF gave perfusion values close to those obtained with the corresponding previously published nonparametric AIF, and are more noise robust. CONCLUSION: When used subsequently in voxelwise perfusion analysis, these semi-parametric AIFs should give more correct perfusion analysis maps less affected by recording noise than the corresponding nonparametric AIFs, and AIFs obtained from arteries. SIGNIFICANCE: This paper presents a method to increase the noise robustness in the estimation of the perfusion parameter values in DCE-MRI.

• MeSH
• Algorithms MeSH
• Arteries pathology   MeSH
• Contrast Media chemistry   pharmacokinetics   MeSH
• Magnetic Resonance Imaging * MeSH
• Mice, Inbred C57BL MeSH
• Mice MeSH
• Perfusion MeSH
• Computer Simulation MeSH
• Image Processing, Computer-Assisted * MeSH
• Reproducibility of Results MeSH
• Image Enhancement * MeSH
• Animals MeSH
• Check Tag
• Mice MeSH
• Female MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

PURPOSE: The composite vascular transport function of a brain voxel consists of one convolutional component for the arteries, one for the capillaries and one for the veins in the voxel of interest. Here, the goal is to find each of these three convolutional components and the associated arterial input function. PHARMACOKINETIC MODELLING: The single voxel vascular transport functions for arteries, capillaries and veins were all modelled as causal exponential functions. Each observed multipass tissue contrast function was as a first approximation modelled as the resulting parametric composite vascular transport function convolved with a nonparametric and voxel specific multipass arterial input function. Subsequently, the residue function was used in the true perfusion equation to optimize the three parameters of the exponential functions. DECONVOLUTION METHODS: For each voxel, the parameters of the three exponential functions were estimated by successive iterative blind deconvolutions using versions of the Lucy-Richardson algorithm. The final multipass arterial input function was then computed by nonblind deconvolution using the Lucy-Richardson algorithm and the estimated composite vascular transport function. RESULTS: Simulations showed that the algorithm worked. The estimated mean transit time of arteries, capillaries and veins of the simulated data agreed with the known input values. For real data, the estimated capillary mean transit times agreed with known values for this parameter. The nonparametric multipass arterial input functions were used to derive the associated map of the arrival time. The arrival time map of a healthy volunteer agreed with known arterial anatomy and physiology. CONCLUSION: Clinically important new voxelwise hemodynamic information for arteries, capillaries and veins separately can be estimated using multipass tissue contrast functions and the iterative blind Lucy-Richardson deconvolution algorithm.

• MeSH
• Algorithms MeSH
• Arteries pathology   MeSH
• Capillaries * diagnostic imaging   MeSH
• Contrast Media * pharmacokinetics   MeSH
• Humans MeSH
• Magnetic Resonance Spectroscopy MeSH
• Magnetic Resonance Imaging methods   MeSH
• Brain diagnostic imaging   MeSH
• Cerebrovascular Circulation MeSH
• Perfusion Imaging MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

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

The paper deals with the impact of chosen geometric and material factors on maximal stresses in carotid atherosclerotic plaque calculated using patient-specific finite element models. These stresses are believed to be decisive for the plaque vulnerability but all applied models suffer from inaccuracy of input data, especially when obtained in vivo only. One hundred computational models based on ex vivo MRI are used to investigate the impact of wall thickness, MRI slice thickness, lipid core and fibrous tissue stiffness, and media anisotropy on the calculated peak plaque and peak cap stresses. The investigated factors are taken as continuous in the range based on published experimental results, only the impact of anisotropy is evaluated by comparison with a corresponding isotropic model. Design of Experiment concept is applied to assess the statistical significance of these investigated factors representing uncertainties in the input data of the model. The results show that consideration of realistic properties of arterial wall in the model is decisive for the stress evaluation; assignment of properties of fibrous tissue even to media and adventitia layers as done in some studies may induce up to eightfold overestimation of peak stress. The impact of MRI slice thickness may play a key role when local thin fibrous cap is present. Anisotropy of media layer is insignificant, and the stiffness of fibrous tissue and lipid core may become significant in some combinations.

• MeSH
• Finite Element Analysis MeSH
• Carotid Arteries diagnostic imaging   pathology   MeSH
• Plaque, Atherosclerotic diagnostic imaging   pathology   MeSH
• Biomechanical Phenomena MeSH
• Humans MeSH
• Magnetic Resonance Imaging MeSH
• Mechanical Phenomena * MeSH
• Models, Cardiovascular MeSH
• Patient-Specific Modeling * MeSH
• Check Tag
• Humans 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

S narůstajícím počtem hospitalizovaných geriatrických pacientů vyvstává mnohem častěji otázka vhodné cévního vstupu. Důležité je již vstupně zhodnotit, zda bude nutný centrální nebo periferní žilní vstup, jak dlouho bude tento vstup využíván a jaké má pacient přidružené choroby, které by mohly volbu cévního vstupu ovlivňovat. Mnohem častěji jsou nyní indikovány dlouhodobé cévní vstupy, které jsou právě u delších hospitalizací křehkých geriatrických pacientů komfortnější pro pacienty a ekonomicky výhodnější pro poskytovatele zdravotní péče. V tomto přehledovém článku předkládáme popis nejčastěji používaných cévních vstupů, které jsou vhodné v geriatrii, spolu s doporučeným algoritmem při volbě cévního vstupu dle Společnosti pro porty a katétry (dále již jen SPPK).

With the growing number of hospitalized geriatric patients, the question of appropriate vascular access arises much more often. It is important to evaluate initially whether central or peripheral venous access will be required, how long this access will be used, and what patients have associated diseases that could affect the choice of vascular access. Much more often, long-term vascular access is now indicated, which is more comfortable for patients and has more economic benefits for healthcare providers, especially in longer hospitalizations of fragile geriatric patients. In this review article, we present a description of the most commonly used vascular inputs, which are suitable in geriatrics, together with the recommended algorithm for the selection of vascular access according to the Society for Ports and Catheters (hereinafter referred to as SPPK).

• MeSH
• Vascular Access Devices * MeSH
• Catheters MeSH
• Frail Elderly MeSH
• Humans MeSH
• Aged, 80 and over MeSH
• Aged MeSH
• Check Tag
• Humans MeSH
• Aged, 80 and over MeSH
• Aged MeSH
• Publication type
• Review MeSH

• MeSH
• Physiological Phenomena MeSH
• Physiology MeSH
• Human Body MeSH

• Conspectus
• Fyziologie člověka a srovnávací fyziologie
• NML Fields
• fyziologie
• NML Publication type
• kolektivní monografie

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

PURPOSE: Ktrans$$ {K}^{\mathrm{trans}} $$ has often been proposed as a quantitative imaging biomarker for diagnosis, prognosis, and treatment response assessment for various tumors. None of the many software tools for Ktrans$$ {K}^{\mathrm{trans}} $$ quantification are standardized. The ISMRM Open Science Initiative for Perfusion Imaging-Dynamic Contrast-Enhanced (OSIPI-DCE) challenge was designed to benchmark methods to better help the efforts to standardize Ktrans$$ {K}^{\mathrm{trans}} $$ measurement. METHODS: A framework was created to evaluate Ktrans$$ {K}^{\mathrm{trans}} $$ values produced by DCE-MRI analysis pipelines to enable benchmarking. The perfusion MRI community was invited to apply their pipelines for Ktrans$$ {K}^{\mathrm{trans}} $$ quantification in glioblastoma from clinical and synthetic patients. Submissions were required to include the entrants' Ktrans$$ {K}^{\mathrm{trans}} $$ values, the applied software, and a standard operating procedure. These were evaluated using the proposed OSIPIgold$$ \mathrm{OSIP}{\mathrm{I}}_{\mathrm{gold}} $$ score defined with accuracy, repeatability, and reproducibility components. RESULTS: Across the 10 received submissions, the OSIPIgold$$ \mathrm{OSIP}{\mathrm{I}}_{\mathrm{gold}} $$ score ranged from 28% to 78% with a 59% median. The accuracy, repeatability, and reproducibility scores ranged from 0.54 to 0.92, 0.64 to 0.86, and 0.65 to 1.00, respectively (0-1 = lowest-highest). Manual arterial input function selection markedly affected the reproducibility and showed greater variability in Ktrans$$ {K}^{\mathrm{trans}} $$ analysis than automated methods. Furthermore, provision of a detailed standard operating procedure was critical for higher reproducibility. CONCLUSIONS: This study reports results from the OSIPI-DCE challenge and highlights the high inter-software variability within Ktrans$$ {K}^{\mathrm{trans}} $$ estimation, providing a framework for ongoing benchmarking against the scores presented. Through this challenge, the participating teams were ranked based on the performance of their software tools in the particular setting of this challenge. In a real-world clinical setting, many of these tools may perform differently with different benchmarking methodology.

• MeSH
• Algorithms MeSH
• Contrast Media * MeSH
• Humans MeSH
• Magnetic Resonance Imaging * methods   MeSH
• Reproducibility of Results MeSH
• Software MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH

• MeSH
• Physiological Phenomena MeSH
• Publication type
• Textbook MeSH

• Conspectus
• Fyziologie člověka a srovnávací fyziologie
• NML Fields
• fyziologie