Feasibility of Dynamic Contrast-Enhanced Magnetic Resonance Imaging Using Low-Dose Gadolinium

He, Dakuo; Chatterjee, Aritrick; Fan, Xiaobing; Wang, Shiyang; Eggener, Scott E.; Yousuf, Ambereen; Antic, Tatjana; Oto, Aytekin; Karczmar, Gregory S.

doi:10.1097/rli.0000000000000466

Cited by 19 publications

(18 citation statements)

References 35 publications

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“…The present results also suggest some clinical and diagnostic advantages for use of a low-dose contrast media DCE-MRI (39). The association between Gd-based contrast media administration and nephrogenic systemic fibrosis has been a concern for patients with renal failure.…”

Section: Discussionsupporting

confidence: 64%

“…In addition, this was previously shown by comparing results from ultrafast DCE-MRI with those from DCE-CT with 120-mL Iohexol in 20 patients with prostate cancer (27). Previous work from this group showed that low-dose Gd contrast distinguishes prostate cancer from benign prostate tissue more effectively than a standard dose on the basis of the signal enhancement rate; this diagnostic accuracy is similar on qualitative assessments (39).…”

Section: Discussionmentioning

confidence: 86%

“…There are increasing concerns regarding intracellular accumulation of Gd-based contrast media (42). Therefore, a low-dose contrast media is preferred to minimize the risk (39). In addition, a standard dose of contrast media may lead to erroneous estimation of AIF owing to the high concentration of the contrast, water exchange effects, and T2* effects (12–14).…”

Section: Discussionmentioning

confidence: 99%

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Use of Indicator Dilution Principle to Evaluate Accuracy of Arterial Input Function Measured With Low-Dose Ultrafast Prostate Dynamic Contrast-Enhanced MRI

et al. 2019

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Accurately measuring arterial input function (AIF) is essential for quantitative analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI). We used the indicator dilution principle to evaluate the accuracy of AIF measured directly from an artery following a low-dose contrast media ultrafast DCE-MRI. In total, 15 patients with biopsy-confirmed localized prostate cancers were recruited. Cardiac MRI (CMRI) and ultrafast DCE-MRI were acquired on a Philips 3 T Ingenia scanner. The AIF was measured at iliac arties following injection of a low-dose (0.015 mmol/kg) gadolinium (Gd) contrast media. The cardiac output (CO) from CMRI (CO CMRI ) was calculated from the difference in ventricular volume at diastole and systole measured on the short axis of heart. The CO from DCE-MRI (CO DCE ) was also calculated from the AIF and dose of the contrast media used. A correlation test and Bland–Altman plot were used to compare CO CMRI and CO DCE . The average (±standard deviation [SD]) area under the curve measured directly from local AIF was 0.219 ± 0.07 mM·min. The average (±SD) CO CMRI and CO DCE were 6.52 ± 1.47 L/min and 6.88 ± 1.64 L/min, respectively. There was a strong positive correlation ( r = 0.82, P < .01) and good agreement between CO CMRI and CO DCE . The CO DCE is consistent with the reference standard CO CMRI . This indicates that the AIF can be measured accurately from an artery with ultrafast DCE-MRI following injection of a low-dose contrast media.

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Section: Discussionsupporting

confidence: 64%

Section: Discussionmentioning

confidence: 86%

Section: Discussionmentioning

confidence: 99%

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Use of Indicator Dilution Principle to Evaluate Accuracy of Arterial Input Function Measured With Low-Dose Ultrafast Prostate Dynamic Contrast-Enhanced MRI

et al. 2019

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“…The GBCA dose (0.02 mmol/kg) was chosen based on both the numerical simulations and previous low-dose investigations. [44][45][46] In the simulations, the SD of the noise was defined as the averaged signal intensity of tumor over the nominal SNR at the standard dose, and was fixed for the other doses, leading to lower true SNR proportionate to the decreased signal intensity for lower dose. Based on Equations 5-7, the signal intensity drops faster when the dose decreases (as shown in Supporting Information Figure S4), causing increased bias and variation of the estimation.…”

Section: Discussionmentioning

confidence: 99%

“…With higher dose, stronger T 2 * effect may lead to more severe underestimation of R 1 and CA concentration. 45 Despite all of these factors, the in vivo study demonstrated that the kinetic parameters had strong correlation and good agreement between the LD-MT-DCE and SD-MT-DCE measurements, indicating that the MT-DCE technique using 20% dose has a similar ability to quantify vascular properties when compared with using standard dose. The kinetic parameters estimated by LD-MT-DCE for normal breast tissue and malignant, and benign tumors are in the range of published results.…”

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confidence: 91%

Five‐dimensional quantitative low‐dose Multitasking dynamic contrast‐ enhanced MRI: Preliminary study on breast cancer

Wang

Fan

et al. 2021

Magnetic Resonance in Med

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To develop a low-dose Multitasking DCE technique (LD-MT-DCE) for breast imaging, enabling dynamic T 1 mapping-based quantitative characterization of tumor blood flow and vascular properties with whole-breast coverage, a spatial resolution of 0.9 × 0.9 × 1.1 mm 3 , and a temporal resolution of 1.4 seconds using a 20% gadolinium dose (0.02 mmol/kg). Methods: Magnetic resonance Multitasking was used to reconstruct 5D images with three spatial dimensions, one T 1 recovery dimension for dynamic T 1 quantification, and one DCE dimension for contrast kinetics. Kinetic parameters F p , v p , K trans , and v e were estimated from dynamic T 1 maps using the two-compartment exchange model. The LD-MT-DCE repeatability and agreement against standard-dose MT-DCE were evaluated in 20 healthy subjects. In 7 patients with triple-negative breast cancer, LD-MT-DCE image quality and diagnostic results were compared with that of standarddose clinical DCE in the same imaging session. One-way unbalanced analysis of variance with Tukey test was performed to evaluate the statistical significance of the kinetic parameters between control and patient groups. Results: The LD-MT-DCE technique was repeatable, agreed with standard-dose MT-DCE, and showed excellent image quality. The diagnosis using LD-MT-DCE matched well with clinical results. The values of F p , v p , and K trans were significantly different between malignant tumors and normal breast tissue (P < .001, < .001, and < .001, respectively), and between malignant and benign tumors (P = .020, .003, and < .001, respectively). Conclusion: The LD-MT-DCE technique was repeatable and showed excellent image quality and equivalent diagnosis compared with standard-dose clinical DCE.

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Magnetic resonance imaging contrast enhancement synthesis using cascade networks with local supervision

et al. 2022

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Purpose: Gadolinium-based contrast agents (GBCAs) are widely administrated in MR imaging for diagnostic studies and treatment planning. Although GBCAs are generally thought to be safe, various health and environmental concerns have been raised recently about their use in MR imaging. The purpose of this work is to derive synthetic contrast enhance MR images from unenhanced counterpart images, thereby eliminating the need for GBCAs, using a cascade deep learning workflow that incorporates contour information into the network. Methods and materials: The proposed workflow consists of two sequential networks: (1) a retina U-Net, which is first trained to derive semantic features from the non-contrast MR images in representing the tumor regions; and (2) a synthesis module, which is trained after the retina U-Net to take the concatenation of the semantic feature maps and non-contrast MR image as input and to generate the synthetic contrast enhanced MR images. After network training, only the non-contrast enhanced MR images are required for the input in the proposed workflow. The MR images of 369 patients from the multimodal brain tumor segmentation challenge 2020 (BraTS2020) dataset were used in this study to evaluate the proposed workflow for synthesizing contrast enhanced MR images (200 patients for five-fold cross-validation and 169 patients for holdout test). Quantitative evaluations were conducted by calculating the normalized mean absolute error (NMAE), structural similarity index measurement (SSIM), and Pearson correlation coefficient (PCC). The original contrast enhanced MR images were considered as the ground truth in this analysis. Results: The proposed cascade deep learning workflow synthesized contrast enhanced MR images that are not visually differentiable from the ground truth with and without supervision of the tumor contours during the network training. Difference images and profiles of the synthetic contrast enhanced MR images revealed that intensity differences could be observed in the tumor region if the contour information was not incorporated in network training. Among the holdout test patients, mean values and standard deviations of the NMAE, SSIM, and PCC were 0.063±0.022, 0.991±0.007 and 0.995±0.006, respectively, for the whole brain; and were 0.050±0.025, 0.993±0.008 and 0.999±0.003, respectively, for the tumor contour regions. Quantitative evaluations with five-fold crossvalidation and hold-out test showed that the calculated metrics can be significantly enhanced (p-values ≤ 0.002) with the tumor contour supervision in network training. Conclusion:The proposed workflow was able to generate synthetic contrast enhanced MR images that closely resemble the ground truth images from non-contrast enhanced MR images when the network training included tumor 3278

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Feasibility of Dynamic Contrast-Enhanced Magnetic Resonance Imaging Using Low-Dose Gadolinium

Cited by 19 publications

References 35 publications

Use of Indicator Dilution Principle to Evaluate Accuracy of Arterial Input Function Measured With Low-Dose Ultrafast Prostate Dynamic Contrast-Enhanced MRI

Use of Indicator Dilution Principle to Evaluate Accuracy of Arterial Input Function Measured With Low-Dose Ultrafast Prostate Dynamic Contrast-Enhanced MRI

Five‐dimensional quantitative low‐dose Multitasking dynamic contrast‐ enhanced MRI: Preliminary study on breast cancer

Magnetic resonance imaging contrast enhancement synthesis using cascade networks with local supervision

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