Our goal was to create a deep network-based lesion detection algorithm for low dose dynamic contrast-enhanced MRI (DCE-MRI) breast images, using Radon Cumulative Distribution Transform (RCDT) to highlight subtle enhancement. We had a dataset of 11 enhancing lesions in eight women with suspected fibroadenomas who underwent a dual-dose DCEMRI protocol on a 3T Philips scanner. To overcome the data limitation, we used a domain-transfer approach, training the YOLOv5 detection model on a publicly available Duke DCE-MRI dataset of 922 biopsy-confirmed invasive breast cancer cases acquired using Siemens or GE scanners. The training data included 23,426 pre-contrast slices with corresponding post-contrast slices and biopsy-proven lesions. The dataset was split into a training set (830 women) and a validation set (92 women). We resized all slices to 400 x 400 pixels and applied RCDT on pre- and post-contrast pairs to highlight lesion enhancement. By combining RCDT images with pre- and post-contrast images, we created RGB images as input for our algorithm. The results were promising, with the algorithm successfully detecting a total of 6 lesions in both regular and low-dose slices, 3 lesions only in regular dose, and 1 lesion only in low dose. However, it missed 1 lesion in both regular and low-dose images. Our study demonstrated the feasibility of a domain-transferred and RCDT-assisted lesion detection algorithm for low-dose MRI, even when data was acquired from scanners made by three different vendors.
PurposeValidation of quantitative imaging biomarkers is a challenging task, due to the difficulty in measuring the ground truth of the target biological process. A digital phantom-based framework is established to systematically validate the quantitative characterization of tumor-associated vascular morphology and hemodynamics based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).ApproachA digital phantom is employed to provide a ground-truth vascular system within which 45 synthetic tumors are simulated. Morphological analysis is performed on high-spatial resolution DCE-MRI data (spatial/temporal resolution = 30 to 300 μm/60 s) to determine the accuracy of locating the arterial inputs of tumor-associated vessels (TAVs). Hemodynamic analysis is then performed on the combination of high-spatial resolution and high-temporal resolution (spatial/temporal resolution = 60 to 300 μm/1 to 10 s) DCE-MRI data, determining the accuracy of estimating tumor-associated blood pressure, vascular extraction rate, interstitial pressure, and interstitial flow velocity.ResultsThe observed effects of acquisition settings demonstrate that, when optimizing the DCE-MRI protocol for the morphological analysis, increasing the spatial resolution is helpful but not necessary, as the location and arterial input of TAVs can be recovered with high accuracy even with the lowest investigated spatial resolution. When optimizing the DCE-MRI protocol for hemodynamic analysis, increasing the spatial resolution of the images used for vessel segmentation is essential, and the spatial and temporal resolutions of the images used for the kinetic parameter fitting require simultaneous optimization.ConclusionAn in silico validation framework was generated to systematically quantify the effects of image acquisition settings on the ability to accurately estimate tumor-associated characteristics.
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