Magnetic Resonance Imaging

Zia, Chaudhuri; Hardinge, Lady; Apt, Leonard

doi:10.1002/9781118633953

Cited by 376 publications

(67 citation statements)

References 90 publications

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“…NMR is a non-invasive and chemically specific technique that can be applied to study mass transport phenomena quantitatively in the form of molecular diffusion and flow (Mantle, 2013). The complete theory of NMR is beyond the scope of this review article and the reader is referred to several excellent books (Brown et al, 2014;Callaghan, 1993;Levitt, 2008).…”

Section: Nuclear Magnetic Resonancementioning

confidence: 99%

Characterisation of pore structures of pharmaceutical tablets: A review

Markl

Strobel

Schlossnikl

et al. 2018

International Journal of Pharmaceutics

104

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Traditionally, the development of a new solid dosage form is formulation-driven and less focus is put on the design of a specific microstructure for the drug delivery system. However, the compaction process particularly impacts the microstructure, or more precisely, the pore architecture in a pharmaceutical tablet. Besides the formulation, the pore structure is a major contributor to the overall performance of oral solid dosage forms as it directly affects the liquid uptake rate, which is the very first step of the dissolution process. In future, additive manufacturing is a potential game changer to design the inner structures and realise a tailor-made pore structure. In pharmaceutical development the pore structure is most commonly only described by the total porosity of the tablet matrix. Yet it is of great importance to consider other parameters to fully resolve the interplay between microstructure and dosage form performance. Specifically, tortuosity, connectivity, as well as pore shape, size and orientation all impact the flow paths and play an important role in describing the fluid flow in a pharmaceutical tablet. This review presents the key properties of the pore structures in solid dosage forms and it discusses how to measure these properties. In particular, the principles, advantages and limitations of helium pycnometry, mercury porosimetry, terahertz time-domain spectroscopy, nuclear magnetic resonance and X-ray computed microtomography are discussed.

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Section: Nuclear Magnetic Resonancementioning

confidence: 99%

Characterisation of pore structures of pharmaceutical tablets: A review

Markl

Strobel

Schlossnikl

et al. 2018

International Journal of Pharmaceutics

104

View full text Add to dashboard Cite

show abstract

“…In general, the more excitations are needed to complete the measurement, the more extended the acquisition time is. For more details on the nuclear physics of MRI and different sampling methods, the interested reader may refer to [2].…”

Section: Fundamentals Of Mri-based Measurementsmentioning

confidence: 99%

Towards Analyzing the Influence of Measurement Errors in Magnetic Resonance Imaging of Fluid Flows

et al. 2020

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Magnetic Resonance Imaging (MRI) provides an insight into opaque structures and does not only have a large number of applications in the field of medical examinations but also in the field of engineering. In technical applications, MRI enables a contactless measurement of the two-or threedimensional velocity field within minutes. However, various measurement methods would benefit from an acceleration of the measurement procedure. Compressed Sensing is a promising method to fit this need. A random undersampling of the sampled data points enables a significant reduction of acquisition time. As this method requires a nonlinear iterative reconstruction of unmeasured data to obtain the same data quality as for a conventional fully sampled measurement, it is essential to estimate the influence of uncertainty on the quantitative result. This paper investigates the implementation of interval arithmetic approaches with a focus on the applicability in the frame of compressed sensing techniques. These approaches are able to handle bounded uncertainty not only in the case of linear relationships between measured data and the computed outputs but also allow for solving the necessary optimality criteria for the fluid velocity reconstruction in an iterative manner under the assumption of set-valued measurement errors and bounded representations of noise.

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“…Hence some literature [18,19] used bias and L 2 bias as more accurate measure. However, Hickernell [20] pointed out that bias and L 2 bias were not suitable as general indicators and demonstrated the reasonability of using WD bias to characterize LHS and random uniform design.…”

Section: Properties Of Lhsmentioning

confidence: 99%

Regression modeling based on improved genetic algorithm

Shi

Benda

et al. 2017

Teh. vjesn.

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Original scientific paper Regression model is a well-established method in data analysis with applications in various fields. The selection of independent variables and mathematically transformed in a regression model is often a challenging problem. Recently, some scholars have used evolutionary computation to solve this problem, but the result is not effective as we desired. The crossover operation in GA is redesigned by using Latin hypercube sampling, then combining two commonly used statistical criteria (AIC, BIC) we are presenting an improved genetic algorithm based for solving statistical model selection problem. The proposed algorithm can overcome strong path-dependence and rely on experience of classical approaches. Comparison of simulation results in solving statistical model selection problem with this improved GA, traditional genetic algorithm and classical algorithm for model selection show that the new GA has superiority in solution of quality, convergence rate and other various indices. Keywords: genetic algorithm; Latin hypercube sampling; regression analysis; regression model selection Regresijsko modeliranje zasnovano na poboljšanom genetičkom algoritmuIzvorni znanstveni članak Regresijski model je dobro uhodana metoda u analizi podataka s primjenom u raznim područjima. Izbor nezavisnih varijabli i matematički transformiranih u regresijski model, često predstavlja izazovan problem. Nedavno je nekoliko znanstvenika primijenilo evolucijski proračun za rješenje tog problema, ali rezultat nije učinkovit onoliko koliko smo željeli. Ukrižena (crossover) operacija u GA redizajnirana je primjenom Latin hypercube uzorkovanja, a zatim, kombinacijom dvaju uobičajeno korištenih statističkih kriterija (AIC, BIC), dajemo poboljšani genetički algoritam za rješavanje problema izbora statističkog modela. Predloženim se algoritmom može prevladati jaka ovisnost o putanji i osloniti na iskustvo stečeno primjenom klasičnih pristupa. Usporedba rezultata simulacije u rješavanju problema odabira statističkog modela s ovim poboljšanim GA, tradicionalnog genetičkog algoritma i klasičnog algoritma za odabir modela pokazuje da je novi GA superiorniji u rješavanju kvalitete, brzine konvergencije i drugih različitih pokazatelja.

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Magnetic Resonance Imaging

Cited by 376 publications

References 90 publications

Characterisation of pore structures of pharmaceutical tablets: A review

Characterisation of pore structures of pharmaceutical tablets: A review

Towards Analyzing the Influence of Measurement Errors in Magnetic Resonance Imaging of Fluid Flows

Regression modeling based on improved genetic algorithm

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