MRI Estimation of T 1 Relaxation Time Using a Constrained Optimization Algorithm

Cao, Fang; Commowick, Olivier; Bannier, Élise; Ferré, Jean-Christophe; Edan, Gilles; Barillot, Christian

doi:10.1007/978-3-642-33530-3_18

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…The utilization of a digital phantom has the principal advantage of the existence of a known ground truth for the assorted radiomic parameters. For each voxel of the digital phantom, nuclear magnetic resonance (NMR) properties including equilibrium magnetization ( M 0 ), longitudinal relaxation time ( T 1 ), transverse relaxation time ( T 2 ), and transverse relaxation time with extra dephasing effects ( T 2 ∗ ) were defined [ 26 ]. Figure 1 shows one representative axial slice of the M 0 , T 1 , T 2 , and T 2 ∗ maps comprising the digital phantom.…”

Section: Methodsmentioning

confidence: 99%

Quantitative Radiomics: Impact of Pulse Sequence Parameter Selection on MRI-Based Textural Features of the Brain

Ford

Dogan

Young

et al. 2018

Contrast Media & Molecular Imaging

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Objectives Radiomic features extracted from diverse MRI modalities have been investigated regarding their predictive and/or prognostic value in a variety of cancers. With the aid of a 3D realistic digital MRI phantom of the brain, the aim of this study was to examine the impact of pulse sequence parameter selection on MRI-based textural parameters of the brain. Methods MR images of the employed digital phantom were realized with SimuBloch, a simulation package made for fast generation of image sequences based on the Bloch equations. Pulse sequences being investigated consisted of spin echo (SE), gradient echo (GRE), spoiled gradient echo (SP-GRE), inversion recovery spin echo (IR-SE), and inversion recovery gradient echo (IR-GRE). Twenty-nine radiomic textural features related, respectively, to gray-level intensity histograms (GLIH), cooccurrence matrices (GLCOM), zone size matrices (GLZSM), and neighborhood difference matrices (GLNDM) were evaluated for the obtained MR realizations, and differences were identified. Results It was found that radiomic features vary considerably among images generated by the five different T1-weighted pulse sequences, and the deviations from those measured on the T1 map vary among features, from a few percent to over 100%. Radiomic features extracted from T1-weighted spin-echo images with TR varying from 360 ms to 620 ms and TE = 3.4 ms showed coefficients of variation (CV) up to 45%, while up to 70%, for T2-weighted spin-echo images with TE varying over the range 60–120 ms and TR = 6400 ms. Conclusion Variability of radiologic textural appearance on MR realizations with respect to the choice of pulse sequence and imaging parameters is feature-dependent and can be substantial. It calls for caution in employing MRI-derived radiomic features especially when pooling imaging data from multiple institutions with intention of correlating with clinical endpoints.

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Section: Methodsmentioning

confidence: 99%

Quantitative Radiomics: Impact of Pulse Sequence Parameter Selection on MRI-Based Textural Features of the Brain

Ford

Dogan

Young

et al. 2018

Contrast Media & Molecular Imaging

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“…We use the DIRAC v6r6p2 instance provided by France-Grilles, § with a first-come, first-served policy imposed by submitting workflows with decreasing priority values for the fairness control process experiment. Four real medical simulation workflows are considered: GATE [33], SimuBloch [34], FIELD-II [35], and PET-Sorteo [36]; their main characteristics are summarized in Table II. For all experiments, control and tested executions are launched simultaneously to ensure similar grid conditions that may vary among repetitions because computing, storage, and network resources are shared with other users. To cover different grid conditions, experiments are repeated over a period of 4 weeks.…”

Section: Experiments Conditions and Metricsmentioning

confidence: 99%

Controlling fairness and task granularity in distributed, online, non‐clairvoyant workflow executions

Silva

Glatard

Desprez

2014

Concurrency and Computation

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International audienceDistributed computing infrastructures are commonly used for scientific computing, and science gateways provide complete middleware stacks to allow their transparent exploitation by end users. However, administrating such systems manually is time consuming and sub-optimal because of the complexity of the execution conditions. Algorithms and frameworks aiming at automating system administration must deal with online and non-clairvoyant conditions, where most parameters are unknown and evolve over time. We consider the problem of controlling task granularity and fairness among scientific workflows executed in these conditions. We present two self-managing loops monitoring the fineness, coarseness, and fairness of workflow executions, comparing these metrics with thresholds extracted from knowledge acquired in previous executions and planning appropriate actions to maintain these metrics to appropriate ranges. Experiments on the European Grid Infrastructure show that our task granularity control can speed up executions up to a factor of 2 and that our fairness control reduces slowdown variability by 3-7 compared with first-come, first-served. We also study the interaction between granularity control and fairness control: our experiments demonstrate that controlling task granularity degrades fairness but that our fairness control algorithm can compensate this degradation

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On-Line, Non-clairvoyant Optimization of Workflow Activity Granularity on Grids

Silva

Glatard

Desprez

2013

Euro-Par 2013 Parallel Processing

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MRI Estimation of T 1 Relaxation Time Using a Constrained Optimization Algorithm

Cited by 3 publications

References 13 publications

Quantitative Radiomics: Impact of Pulse Sequence Parameter Selection on MRI-Based Textural Features of the Brain

Quantitative Radiomics: Impact of Pulse Sequence Parameter Selection on MRI-Based Textural Features of the Brain

Controlling fairness and task granularity in distributed, online, non‐clairvoyant workflow executions

On-Line, Non-clairvoyant Optimization of Workflow Activity Granularity on Grids

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