We Built This House; It’s Time to Move in: Leveraging Existing DICOM Structure to More Completely Utilize Readily Available Detailed Contrast Administration Information

Hirsch, Jeffrey; Siegel, Eliot L.; Balasubramanian, Sridhar; Wang, Kenneth C.

doi:10.1007/s10278-015-9771-y

Cited by 5 publications

(2 citation statements)

References 8 publications

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“…It follows that measurements in contrast-enhanced CT scans should be adjusted to avoid possible misdiagnoses, such as of osteoporosis [ 15 ]. Although modern CT scanners usually provide information on contrast administration in the imaging metadata, there is commonly no direct documentation on the contrast phase present and any application errors that may have occurred [ 16 ]. Furthermore, these metadata are often incompletely reported, causing major problems for fully automated pipelines.…”

Section: Introductionmentioning

confidence: 99%

Automated detection of the contrast phase in MDCT by an artificial neural network improves the accuracy of opportunistic bone mineral density measurements

et al. 2021

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Objectives To determine the accuracy of an artificial neural network (ANN) for fully automated detection of the presence and phase of iodinated contrast agent in routine abdominal multidetector computed tomography (MDCT) scans and evaluate the effect of contrast correction for osteoporosis screening. Methods This HIPPA-compliant study retrospectively included 579 MDCT scans in 193 patients (62.4 ± 14.6 years, 48 women). Three different ANN models (2D DenseNet with random slice selection, 2D DenseNet with anatomy-guided slice selection, 3D DenseNet) were trained in 462 MDCT scans of 154 patients (threefold cross-validation), who underwent triphasic CT. All ANN models were tested in 117 unseen triphasic scans of 39 patients, as well as in a public MDCT dataset containing 311 patients. In the triphasic test scans, trabecular volumetric bone mineral density (BMD) was calculated using a fully automated pipeline. Root-mean-square errors (RMSE) of BMD measurements with and without correction for contrast application were calculated in comparison to nonenhanced (NE) scans. Results The 2D DenseNet with anatomy-guided slice selection outperformed the competing models and achieved an F1 score of 0.98 and an accuracy of 98.3% in the test set (public dataset: F1 score 0.93; accuracy 94.2%). Application of contrast agent resulted in significant BMD biases (all p < .001; portal-venous (PV): RMSE 18.7 mg/ml, mean difference 17.5 mg/ml; arterial (AR): RMSE 6.92 mg/ml, mean difference 5.68 mg/ml). After the fully automated correction, this bias was no longer significant (p > .05; PV: RMSE 9.45 mg/ml, mean difference 1.28 mg/ml; AR: RMSE 3.98 mg/ml, mean difference 0.94 mg/ml). Conclusion Automatic detection of the contrast phase in multicenter CT data was achieved with high accuracy, minimizing the contrast-induced error in BMD measurements. Key Points • A 2D DenseNet with anatomy-guided slice selection achieved an F1 score of 0.98 and an accuracy of 98.3% in the test set. In a public dataset, an F1 score of 0.93 and an accuracy of 94.2% were obtained. • Automated adjustment for contrast injection improved the accuracy of lumbar bone mineral density measurements (RMSE 18.7 mg/ml vs. 9.45 mg/ml respectively, in the portal-venous phase). • An artificial neural network can reliably reveal the presence and phase of iodinated contrast agent in multidetector CT scans (https://github.com/ferchonavarro/anatomy_guided_contrast_c). This allows minimizing the contrast-induced error in opportunistic bone mineral density measurements.

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Section: Introductionmentioning

confidence: 99%

Automated detection of the contrast phase in MDCT by an artificial neural network improves the accuracy of opportunistic bone mineral density measurements

et al. 2021

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“…Moreover, in some cases, this metadata is not consistently stored (e.g. contrast administration Hirsch et al 2015 ) and might even be partially or entirely missing, as can be the case for anonymized data (Moore et al 2015 ). As a result, the sorting is frequently done manually, by looking at each scan and labeling it according to the perceived scan type.…”

Section: Introductionmentioning

confidence: 99%

DeepDicomSort: An Automatic Sorting Algorithm for Brain Magnetic Resonance Imaging Data

2020

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With the increasing size of datasets used in medical imaging research, the need for automated data curation is arising. One important data curation task is the structured organization of a dataset for preserving integrity and ensuring reusability. Therefore, we investigated whether this data organization step can be automated. To this end, we designed a convolutional neural network (CNN) that automatically recognizes eight different brain magnetic resonance imaging (MRI) scan types based on visual appearance. Thus, our method is unaffected by inconsistent or missing scan metadata. It can recognize precontrast T1-weighted (T1w), post-contrast T1-weighted (T1wC), T2-weighted (T2w), proton density-weighted (PDw) and derived maps (e.g. apparent diffusion coefficient and cerebral blood flow). In a first experiment, we used scans of subjects with brain tumors: 11065 scans of 719 subjects for training, and 2369 scans of 192 subjects for testing. The CNN achieved an overall accuracy of 98.7%. In a second experiment, we trained the CNN on all 13434 scans from the first experiment and tested it on 7227 scans of 1318 Alzheimer's subjects. Here, the CNN achieved an overall accuracy of 98.5%. In conclusion, our method can accurately predict scan type, and can quickly and automatically sort a brain MRI dataset virtually without the need for manual verification. In this way, our method can assist with properly organizing a dataset, which maximizes the shareability and integrity of the data.

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Multi-phase synthetic contrast enhancement in interventional computed tomography for guiding renal cryotherapy

Pinnock

Bandula

et al. 2023

Int J CARS

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Purpose Minimally invasive treatments for renal carcinoma offer a low rate of complications and quick recovery. One drawback of the use of computed tomography (CT) for needle guidance is the use of iodinated contrast agents, which require an increased X-ray dose and can potentially cause adverse reactions. The purpose of this work is to generalise the problem of synthetic contrast enhancement to allow the generation of multiple phases on non-contrast CT data from a real-world, clinical dataset without training multiple convolutional neural networks. Methods A framework for switching between contrast phases by conditioning the network on the phase information is proposed and compared with separately trained networks. We then examine how the degree of supervision affects the generated contrast by evaluating three established architectures: U-Net (fully supervised), Pix2Pix (adversarial with supervision), and CycleGAN (fully adversarial). Results We demonstrate that there is no performance loss when testing the proposed method against separately trained networks. Of the training paradigms investigated, the fully adversarial CycleGAN performs the worst, while the fully supervised U-Net generates more realistic voxel intensities and performed better than Pix2Pix in generating contrast images for use in a downstream segmentation task. Lastly, two models are shown to generalise to intra-procedural data not seen during the training process, also enhancing features such as needles and ice balls relevant to interventional radiological procedures. Conclusion The proposed contrast switching framework is a feasible option for generating multiple contrast phases without the overhead of training multiple neural networks, while also being robust towards unseen data and enhancing contrast in features relevant to clinical practice.

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We Built This House; It’s Time to Move in: Leveraging Existing DICOM Structure to More Completely Utilize Readily Available Detailed Contrast Administration Information

Cited by 5 publications

References 8 publications

Automated detection of the contrast phase in MDCT by an artificial neural network improves the accuracy of opportunistic bone mineral density measurements

Automated detection of the contrast phase in MDCT by an artificial neural network improves the accuracy of opportunistic bone mineral density measurements

DeepDicomSort: An Automatic Sorting Algorithm for Brain Magnetic Resonance Imaging Data

Multi-phase synthetic contrast enhancement in interventional computed tomography for guiding renal cryotherapy

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