DCE‐MRI of the prostate using shutter‐speed vs. Tofts model for tumor characterization and assessment of aggressiveness

Hectors, Stefanie; Besa, Cecilia; Wagner, Mathilde; Jajamovich, Guido H.; Haines, G. Kenneth; Lewis, Sara; Tewari, Ashutosh; Rastinehad, Ardeshir R.; Huang, Wei; Taouli, Bachir

doi:10.1002/jmri.25631

Cited by 11 publications

(5 citation statements)

References 45 publications

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“…In addition, we also confirm that the presence of cancer correlates well with lower values of dispersion, because k d is inversely proportional to the dispersion coefficient. This enforces the hypothesis that microvascular tortuosity, constraining the The estimated PK parameter values were comparable to those obtained in previous DCE-ultrasound [25] and DCE-MRI [19,33] studies for dispersion MRI, and they also were similar to those in previous reports for TM analysis [22]. However, compared with TM analysis, lower values of k ep were obtained by dispersion MRI.…”

Section: Discussionsupporting

confidence: 89%

“…This represents a limitation both theoretically (because the effects of delay and dispersion on the AIF caused by transport from the sampling site [the large artery] to the exchange site [the capillaries] are neglected) [18,19] and practically (because measurement of the AIF is notoriously affected by several sources of error, which can considerably influence parameter estimation) [16,20,21]. Moreover, quantitative analysis of DCE-MRI with use of the TM has failed to prove substantial improvements, compared with qualitative or semiquantitative DCE analysis [22,23]. As a result, DCE-MRI quantification is rarely performed in mpMRI protocols.…”

Section: Objectivementioning

confidence: 99%

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Evaluation of Dispersion MRI for Improved Prostate Cancer Diagnosis in a Multicenter Study

Turco

Lavini

Heijmink

et al. 2018

American Journal of Roentgenology

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found to be clinically insignificant [6]) and the related risk of overtreatment represent a major clinical challenge. In fact, the radical intervention (radical prostatectomy and radiotherapy) remains the most common treatment and is associated with such severe side effects as urinary, sexual, and bowel dysfunction [7]. Although focal therapies may reduce morbidity in patients with localized prostate cancer [4, 8], the efficient use of such therapies is limited by the lack of reliable imaging methods for in vivo cancer localization and grading [8, 9]. Multiparametric MRI (mpMRI), which typically combines T2-weighted imaging, DWI, and dynamic contrast-enhanced MRI (DCE-MRI), has emerged as a useful tool for prostate cancer detection, localization, grading, and treatment planning and guidance [3, 10, 11]. Although several studies have shown

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Section: Discussionsupporting

confidence: 89%

Section: Objectivementioning

confidence: 99%

Evaluation of Dispersion MRI for Improved Prostate Cancer Diagnosis in a Multicenter Study

Turco

Lavini

Heijmink

et al. 2018

American Journal of Roentgenology

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“…Quantitative analysis of dynamic contrast enhanced (DCE) MRI is important for the detection and diagnosis of cancers (Hectors et al 2017, Ream et al 2017, Wei et al 2018, Wu et al 2018. Twenty years ago, Tofts et al standardized quantities and symbols for quantitative analysis of DCE-MRI data using a two-compartment (i.e.…”

Section: Introductionmentioning

confidence: 99%

Signal intensity form of the Tofts model for quantitative analysis of prostate dynamic contrast enhanced MRI data

Fan¹,

Chatterjee²,

Medved³

et al. 2021

Phys. Med. Biol.

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The aim of this study is to develop a signal intensity (S(t)) form of the standard Tofts pharmacokinetic model that avoids the need to calculate tissue contrast agent concentration (C(t)) as function of time (t). We refer to this as ‘SI-Tofts’ model. Physiological parameters (K trans and v e) calculated using the SI-Tofts and standard Tofts models were compared by using simulations and human prostate dynamic contrast enhanced (DCE) MRI data. This approach was also applied to the Patlak model to compare K trans values calculated from C(t) and S(t). Simulations were performed on DCE-MRI data from the quantitative imaging biomarkers alliance to validate SI-Tofts model. In addition, ultrafast DCE-MRI data were acquired from 18 prostate cancer patients on a Philips Achieva 3T-TX scanner. Regions-of-interest (ROIs) for prostate cancer, normal tissue, gluteal muscle, and iliac artery were manually traced. The C(t) was calculated for each ROI using the standard model with measured pre-contrast tissue T 1 values. Both the simulation and clinical results showed strong correlation (r = 0.87–0.99, p < 0.001) for K trans and v e calculated from the SI-Tofts and standard Tofts models. The SI-Tofts model with a correction factor using the T 1 ratio of blood to tissue significantly improved the K trans estimates. The correlation of K trans obtained from the Patlak model with C(t) vs S(t) was also strong (r = 0.95–0.99, p < 0.001). These preliminary results suggest that physiological parameters from DCE-MRI can be reliably estimated from the SI-Tofts model without contrast agent concentration calculation.

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“…The DCE-MRI data are frequently analyzed based on a region of interest (ROI) defined by a radiologist (Othman et al, 2016). For a specific ROI, normally the average contrast agent concentration curve as function of time over whole ROI is calculated first, and then the pharmacokinetic model is used to extract physiological parameters for the ROI (Langer et al, 2009, Hectors et al, 2017). We would like to call this as ‘ROI-averaged’ analysis.…”

Section: Introductionmentioning

confidence: 99%

Comparison of region-of-interest-averaged and pixel-averaged analysis of DCE-MRI data based on simulations and pre-clinical experiments

Zamora

Oto

et al. 2017

Phys. Med. Biol.

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Differences between region-of-interest (ROI) and pixel-by-pixel analysis of dynamic contrast enhanced (DCE) MRI data were investigated in this study with computer simulations and pre-clinical experiments. ROIs were simulated with 10, 50, 100, 200, 400, and 800 different pixels. For each pixel, a contrast agent concentration as a function of time, C(t), was calculated using the Tofts DCE-MRI model with randomly generated physiological parameters (Ktrans and ve) and the Parker population arterial input function. The average C(t) for each ROI was calculated and then Ktrans and ve for the ROI was extracted. The simulations were run 100 times for each ROI with new Ktrans and ve generated. In addition, white Gaussian noise was added to C(t) with 3, 6, and 12 dB signal-to-noise ratios to each C(t). For pre-clinical experiments, Copenhagen rats (n = 6) with implanted prostate tumors in the hind limb were used in this study. The DCE-MRI data were acquired with a temporal resolution of ~5 s in a 4.7 T animal scanner, before, during, and after a bolus injection (< 5 s) of Gd-DTPA for a total imaging duration of ~10 min. Ktrans and ve were calculated in two ways: (i) by fitting C(t) for each pixel, and then averaging the pixel values over the entire ROI, and (ii) by averaging C(t) over the entire ROI, and then fitting C(t) to extract Ktrans and ve. The simulation results showed that in heterogeneous ROIs, the pixel-by-pixel averaged Ktrans was ~25% to ~50% larger (p < 0.01) than the ROI-averaged Ktrans. At higher noise levels, the pixel-averaged Ktrans was greater than the ‘true’ Ktrans, but the ROI-averaged Ktrans was lower than the ‘true’ Ktrans. The ROI-averaged Ktrans was closer to the true Ktrans than pixel-averaged Ktrans for high noise levels. In pre-clinical experiments, the pixel-by-pixel averaged Ktrans was ~15% larger than the ROI-averaged Ktrans. Overall, with the Tofts model, the extracted physiological parameters from the pixel-by-pixel averages were larger than the ROI averages. These differences were dependent on the heterogeneity of the ROI.

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DCE‐MRI of the prostate using shutter‐speed vs. Tofts model for tumor characterization and assessment of aggressiveness

Abstract: 3 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:837-849.

Cited by 11 publications

References 45 publications

Evaluation of Dispersion MRI for Improved Prostate Cancer Diagnosis in a Multicenter Study

Evaluation of Dispersion MRI for Improved Prostate Cancer Diagnosis in a Multicenter Study

Signal intensity form of the Tofts model for quantitative analysis of prostate dynamic contrast enhanced MRI data

Comparison of region-of-interest-averaged and pixel-averaged analysis of DCE-MRI data based on simulations and pre-clinical experiments

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