Quantitative transport mapping (QTM) for differentiating benign and malignant breast lesion: Comparison with traditional kinetics modeling and semi-quantitative enhancement curve characteristics.

Zhang, Qihao; Spincemaille, Pascal; Drotman, Michele; Chen, Christine; Eskreis‐Winkler, Sarah; Huang, Weiyuan; Zhou, Liangdong; Morgan, John P.; Nguyen, Thanh D.; Prince, Martin R.; Wang, Yi

doi:10.1016/j.mri.2021.10.039

Cited by 15 publications

(29 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on eqn. (11), the hybrid synchronous spectrum is always symmetric and the asynchronous spectrum is always antisymmetric relative to the diagonal. Therefore, the underlying principle of the smart error sum, introduced for series of curves, namely to increase the symmetry of the hybrid synchronous 2D correlation map and the antisymmetry of the hybrid asynchronous 2D correlation map by varying the fit parameters, cannot be employed.…”

Section: ( ) ( )mentioning

confidence: 99%

“…Potential applications of curve fitting are countless and encompass virtually all scientific disciplines. Examples include biosynthesis, 6 thermoluminescence, 7 solar energy, 8 materials science and technology, 9 agriculture, 10 cancer research, 11 kinetics, 12 thermal engineering, 13 transportation, 14 soil science, 15 remote sensing of ecosystems, 16 epidemiology, 17 power and energy engineering, 18 population growth 19 and spectroscopy, 20 to name just a few. The disagreement metrics to minimize during the fit depends on the properties of the noise and possibly on prior information on the parameters to fit.…”

Section: Mainmentioning

confidence: 99%

See 1 more Smart Citation

Correcting Systematic Errors by Hybrid 2D Correlation Loss Functions in nonlinear inverse modelling

Mayerhöfer

Noda

Pahlow

et al. 2022

Preprint

View full text Add to dashboard Cite

Recently a new family of loss functions called smart error sums has been suggested. These loss functions account for correlations within experimental data and force modeled data to obey these correlations. As a result, multiplicative systematic errors of experimental data can be revealed and corrected. The smart error sums are based on 2D correlation analysis which is a comparably recent methodology for analyzing spectroscopic data that has found broad application. In this contribution we mathematically generalize and break down this methodology and the smart error sums to uncover the mathematic roots and simplify it to craft a general tool beyond spectroscopic modelling. This reduction also allows a simplified discussion about limits and prospects of this new method including one of its potential future uses as a sophisticated loss function in deep learning. To support its deployment, the work includes computer code to allow reproduction of the basic results.

show abstract

Section: ( ) ( )mentioning

confidence: 99%

Section: Mainmentioning

confidence: 99%

Correcting Systematic Errors by Hybrid 2D Correlation Loss Functions in nonlinear inverse modelling

Mayerhöfer

Noda

Pahlow

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…However, Zhou et al [67,68] assume the diffusion process is negligible; therefore, only the velocity field is estimated similar to optical flow [8,26,50]. Liu et al [37,38] and Zhang et al [66] estimate both the spatial-varying ve-locity and diffusion fields, yet modeling the diffusion as a scalar field, which cannot express the important diffusion anisotropy.…”

Section: Related Workmentioning

confidence: 99%

“…Koundal et al [30] use optimal mass transport combined with a spatially constant diffusion. Optimization approaches were proposed [37,38,66] to estimate the advection-diffusion parameters of an advectiondiffusion equation in 3D. Though promising, the numerical optimization approach is time-consuming, especially when dealing with large datasets.…”

Section: Introductionmentioning

confidence: 99%

Deep Decomposition for Stochastic Normal-Abnormal Transport

Liu¹,

Lee²,

Aylward³

et al. 2021

Preprint

View full text Add to dashboard Cite

Advection-diffusion equations describe a large family of natural transport processes, e.g., fluid flow, heat transfer, and wind transport. They are also used for optical flow and perfusion imaging computations. We develop a machine learning model, D 2 -SONATA, built upon a stochastic advection-diffusion equation, which predicts the velocity and diffusion fields that drive 2D/3D image time-series of transport. In particular, our proposed model incorporates a model of transport atypicality, which isolates abnormal differences between expected normal transport behavior and the observed transport. In a medical context such a normalabnormal decomposition can be used, for example, to quantify pathologies. Specifically, our model identifies the advection and diffusion contributions from the transport timeseries and simultaneously predicts an anomaly value field to provide a decomposition into normal and abnormal advection and diffusion behavior. To achieve improved estimation performance for the velocity and diffusion-tensor fields underlying the advection-diffusion process and for the estimation of the anomaly fields, we create a 2D/3D anomalyencoded advection-diffusion simulator, which allows for supervised learning. We further apply our model on a brain perfusion dataset from ischemic stroke patients via transfer learning. Extensive comparisons demonstrate that our model successfully distinguishes stroke lesions (abnormal) from normal brain regions, while reconstructing the underlying velocity and diffusion tensor fields. 1

show abstract

“…Recently, the quantitative transport mapping (QTM) method [ 14 , 15 ] has been developed to estimate mass flux characterized by velocity

without a global arterial input function (AIF) used in traditional Kety’s tracer kinetic analysis [ 16 , 17 ]. QTM velocity has been shown to be more accurate than Kety’s parameters in validation with numerical ground truth [ 14 ] and more sensitive than Kety’s parameters for differentiating benign from malignant tumors compared with biopsy [ 18 , 19 ]. Accordingly, we propose to investigate the feasibility of noninvasive prediction of LSF according to QTM velocity

, as well as Kety’s parameters, derived from DCE MRI.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Lung Shunt Fraction for Yttrium-90 Treatment of Hepatic Tumors Using Dynamic Contrast Enhanced MRI with Quantitative Perfusion Processing

et al. 2022

Self Cite

View full text Add to dashboard Cite

There is no noninvasive method to estimate lung shunting fraction (LSF) in patients with liver tumors undergoing Yttrium-90 (Y90) therapy. We propose to predict LSF from noninvasive dynamic contrast enhanced (DCE) MRI using perfusion quantification. Two perfusion quantification methods were used to process DCE MRI in 25 liver tumor patients: Kety’s tracer kinetic modeling with a delay-fitted global arterial input function (AIF) and quantitative transport mapping (QTM) based on the inversion of transport equation using spatial deconvolution without AIF. LSF was measured on SPECT following Tc-99m macroaggregated albumin (MAA) administration via hepatic arterial catheter. The patient cohort was partitioned into a low-risk group (LSF ≤ 10%) and a high-risk group (LSF > 10%). Results: In this patient cohort, LSF was positively correlated with QTM velocity |u| (r = 0.61, F = 14.0363, p = 0.0021), and no significant correlation was observed with Kety’s parameters, tumor volume, patient age and gender. Between the low LSF and high LSF groups, there was a significant difference for QTM |u| (0.0760 ± 0.0440 vs. 0.1822 ± 0.1225 mm/s, p = 0.0011), and Kety’s Ktrans (0.0401 ± 0.0360 vs 0.1198 ± 0.3048, p = 0.0471) and Ve (0.0900 ± 0.0307 vs. 0.1495 ± 0.0485, p = 0.0114). The area under the curve (AUC) for distinguishing between low LSF and high LSF was 0.87 for |u|, 0.80 for Ve and 0.74 for Ktrans. Noninvasive prediction of LSF is feasible from DCE MRI with QTM velocity postprocessing.

show abstract

Quantitative transport mapping (QTM) for differentiating benign and malignant breast lesion: Comparison with traditional kinetics modeling and semi-quantitative enhancement curve characteristics.

Cited by 15 publications

References 49 publications

Correcting Systematic Errors by Hybrid 2D Correlation Loss Functions in nonlinear inverse modelling

Correcting Systematic Errors by Hybrid 2D Correlation Loss Functions in nonlinear inverse modelling

Deep Decomposition for Stochastic Normal-Abnormal Transport

Prediction of Lung Shunt Fraction for Yttrium-90 Treatment of Hepatic Tumors Using Dynamic Contrast Enhanced MRI with Quantitative Perfusion Processing

Contact Info

Product

Resources

About