Surface‐to‐volume ratio mapping of tumor microstructure using oscillating gradient diffusion weighted imaging

Reynaud, Olivier; Winters, Kerryanne Veronica; Hoang, Dung Minh; Wadghiri, Youssef Zaim; Novikov, Dmitry S.; Kim, Sungheon

doi:10.1002/mrm.25865

Cited by 56 publications

(75 citation statements)

References 39 publications

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“…diffusion contrast on commercial scanners are restricted to the far left side of the spectrum (f OGSE < 300 Hz), insufficient to explore diffusion inside small structures (R = 1-2 µm). On the other hand, the short-time limit-characterized by the linear relationship between D and ω −1/2 -is already within reach for larger cells (R = 5-10 µm, see Figure 3B), as demonstrated in vivo in Reynaud et al [18].…”

Section: Figure 3 | Intracellular Diffusivity and Cell Size (A)supporting

confidence: 63%

“…For small in vivo structures (R < 10 µm), only OGSE can achieve sufficient diffusion strength to probe this regime, by accumulating contrast over N oscillations: b total = N × b N=1 [27]. The linearity of D with ω −1/2 was recently demonstrated for f = ω/2π > 90 Hz in mice brain glioma [18] with large cellular radius (GL261, R cell ∼ 5 µm). The quadratic inequality f ∼ 1/t ≫ D 0 /R 2 rapidly becomes impossible to satisfy for smaller structures (healthy brain tissue, astrocytes, neurons, with R∼1 µm).…”

Section: The Short Time Regimementioning

confidence: 99%

“…Microstructural parameter estimation is performed in two steps. The surface-to-volume ratio and free diffusivity are first evaluated using high-frequency OGSE in the short-time regime [18] using Equation (2). These values are then used as constraints when fitting the lowfrequency OGSE and PGSE data ( Figure 5, f OGSE < 88 Hz) to a model of impermeable spheres bathing in ECS [15].…”

Section: The Pomace Modelmentioning

confidence: 99%

“…The validity of the short diffusion-time regime was demonstrated in vivo and ex vivo in mice gliomas (GL261, R∼5 µm) in the range 88 Hz ≤ f OGSE ≤ 225 Hz [18]. The decoupling of diffusive and geometric properties was assessed ex vivo by varying the sample temperature, only impacting the term D 0 in Equation (2).…”

Section: The Short Time Regimementioning

confidence: 99%

“…Modeling cells as impermeable spheres, additional assumptions are made to describe the ECS, and finally estimate diffusivities, cell size and volume fraction ex vivo and in vivo. Non-geometrical models [17,18] are also discussed.…”

Section: Introductionmentioning

confidence: 99%

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Time-Dependent Diffusion MRI in Cancer: Tissue Modeling and Applications

Reynaud

2017

Front. Phys.

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In diffusion weighted imaging (DWI), the apparent diffusion coefficient (ADC) has been recognized as a useful and sensitive surrogate for cell density, paving the way for non-invasive tumor staging, and characterization of treatment efficacy in cancer. However, microstructural parameters, such as cell size, density and/or compartmental diffusivities affect diffusion in various fashions, making of conventional DWI a sensitive but non-specific probe into changes happening at cellular level. Alternatively, tissue complexity can be probed and quantified using the time dependence of diffusion metrics, sometimes also referred to as temporal diffusion spectroscopy when only using oscillating diffusion gradients. Time-dependent diffusion (TDD) is emerging as a strong candidate for specific and non-invasive tumor characterization. Despite the lack of a general analytical solution for all diffusion times/frequencies, TDD can be probed in various regimes where systems simplify in order to extract relevant information about tissue microstructure. The fundamentals of TDD are first reviewed (a) in the short time regime, disentangling structural and diffusive tissue properties, and (b) near the tortuosity limit, assuming weakly heterogeneous media near infinitely long diffusion times. Focusing on cell bodies (as opposed to neuronal tracts), a simple but realistic model for intracellular diffusion can offer precious insight on diffusion inside biological systems, at all times. Based on this approach, the main three geometrical models implemented so far (IMPULSED, POMACE, VERDICT) are reviewed. Their suitability to quantify cell size, intra-and extracellular spaces (ICS and ECS) and diffusivities are assessed. The proper modeling of tissue membrane permeability-hardly a newcomer in the field, but lacking applications-and its impact on microstructural estimates are also considered. After discussing general issues with tissue modeling and microstructural parameter estimation (i.e., fitting), potential solutions are detailed. The in vivo applications of this new, non-invasive, specific approach in cancer are reviewed, ranging from the characterization of gliomas in rodent brains and observation of time-dependence in breast tissue lesions and prostate cancer, to the recent preclinical evaluation of new treatments efficacy. It is expected that clinical applications of TDD will strongly benefit the community in terms of non-invasive cancer screening.

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Section: Figure 3 | Intracellular Diffusivity and Cell Size (A)supporting

confidence: 63%

Section: The Short Time Regimementioning

confidence: 99%

Section: The Pomace Modelmentioning

confidence: 99%

Section: The Short Time Regimementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Time-Dependent Diffusion MRI in Cancer: Tissue Modeling and Applications

Reynaud

2017

Front. Phys.

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Stimulated echo diffusion tensor imaging (STEAM-DTI) with varying diffusion times as a probe of breast tissue

Teruel

Cho

et al. 2016

J. Magn. Reson. Imaging

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Time‐dependent diffusion MRI to distinguish malignant from benign head and neck tumors

Iima

Yamamoto

Kataoka

et al. 2018

Magnetic Resonance Imaging

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Background There is growing interest in the effect of diffusion time on apparent diffusion coefficient (ADC) values in cancers; however, little evidence exists regarding its utility to differentiate malignant from benign head and neck tumors. Purpose To investigate the utility of ADC value changes in distinguishing between malignant and benign head and neck tumors using the different diffusion times obtained from oscillating gradient spin‐echo (OGSE) and pulsed gradient spin‐echo (PGSE) MRI sequences. Study Type Prospective. Subjects Thirty‐one consecutive patients with suspected head and neck tumors and a phantom. Field Strength/Sequence 3T MRI with diffusion‐weighted imaging (DWI) using OGSE (effective diffusion time: 4.3 msec) and PGSE (effective diffusion time: 82.6 msec) sequences and b‐values of 0 and 700 s/mm2. Assessment ADC values using OGSE (ADCOGSE) and PGSE (ADCPGSE) and relative ADC value changes between ADCOGSE and ADCPGSE. Statistical Tests Wilcoxon test, Mann–Whitney test, and McNemar test. Results Relative ADC changes for each polyvinylpyrrolidone (PVP) and water in the phantom between OGSE and PGSE sequences were small (relative ADC change within 0.6%). Malignant tumors had significantly smaller ADCOGSE and ADCPGSE values than benign tumors (P < 0.001 and < 0.0001, respectively). Significantly larger relative ADC changes were observed in malignant compared with benign head and neck tumors (P < 0.0001). ADCPGSE values were significantly lower than ADCOGSE values in both malignant and benign head and neck tumors (0.97 vs. 1.28 × 10−3mm2/s: P < 0.0001 and 1.93 vs. 1.99 × 10−3mm2/s: P = 0.0056, respectively). Relative ADC change and ADCPGSE tended to have higher diagnostic performance than ADCOGSE, with area under the curve (AUC) values of 0.97, 0.96, and 0.89, respectively. Data Conclusion ADC values obtained using the PGSE sequence were lower than those obtained with OGSE. This difference was larger for malignant than benign tumors, suggesting differences in tissue structure (diffusion hindrance) or cell permeability, revealed by changes in diffusion time. The results underline the potential importance of reporting diffusion time for interpretation of head and neck diffusion MRI. Level of Evidence: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:88–95.

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Surface‐to‐volume ratio mapping of tumor microstructure using oscillating gradient diffusion weighted imaging

Cited by 56 publications

References 39 publications

Time-Dependent Diffusion MRI in Cancer: Tissue Modeling and Applications

Time-Dependent Diffusion MRI in Cancer: Tissue Modeling and Applications

Stimulated echo diffusion tensor imaging (STEAM-DTI) with varying diffusion times as a probe of breast tissue

Time‐dependent diffusion MRI to distinguish malignant from benign head and neck tumors

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