Dynamic field‐of‐view imaging to increase temporal resolution in the early phase of contrast media uptake in breast <scp>DCE</scp>‐<scp>MRI</scp>: A feasibility study

Pineda, Federico; Easley, Ty; Karczmar, Gregory S.

doi:10.1002/mp.12747

Cited by 9 publications

(8 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This procedure represents the “ideal” ultrafast scan: it assumes a (spatially) Nyquist complete sample is acquired at 3.5s temporal resolution using typical ultrafast acquisition parameters (see Table 2 ). Because ultrafast often relies on other methods (like SENSE and Partial Fourier) to achieve such high temporal resolution [ 5 , 7 , 19 ], we are essentially benchmarking against an assumed perfect SENSE and Partial Fourier reconstruction.…”

Section: Theory and Methodsmentioning

confidence: 99%

“…In small mammals, which require small fields-of-view, researchers have been able to characterize AIFs with significantly higher sampling frequency; Yankeelov et al [ 16 ] measured an AIF at 0.9s/image in mice, while Kershaw et al measured an AIF at 0.44s/image in rabbits [ 17 ]. Current state-of-the-art in ultrafast breast DCE-MRI produces full 3D bilateral breast scans with temporal resolution 2.7–3.8s [ 4 , 7 , 18 , 19 ], well above the desired threshold of temporal resolution.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Enhancement-constrained acceleration: A robust reconstruction framework in breast DCE-MRI

et al. 2021

Self Cite

View full text Add to dashboard Cite

In patients with dense breasts or at high risk of breast cancer, dynamic contrast enhanced MRI (DCE-MRI) is a highly sensitive diagnostic tool. However, its specificity is highly variable and sometimes low; quantitative measurements of contrast uptake parameters may improve specificity and mitigate this issue. To improve diagnostic accuracy, data need to be captured at high spatial and temporal resolution. While many methods exist to accelerate MRI temporal resolution, not all are optimized to capture breast DCE-MRI dynamics. We propose a novel, flexible, and powerful framework for the reconstruction of highly-undersampled DCE-MRI data: enhancement-constrained acceleration (ECA). Enhancement-constrained acceleration uses an assumption of smooth enhancement at small time-scale to estimate points of smooth enhancement curves in small time intervals at each voxel. This method is tested in silico with physiologically realistic virtual phantoms, simulating state-of-the-art ultrafast acquisitions at 3.5s temporal resolution reconstructed at 0.25s temporal resolution (demo code available here). Virtual phantoms were developed from real patient data and parametrized in continuous time with arterial input function (AIF) models and lesion enhancement functions. Enhancement-constrained acceleration was compared to standard ultrafast reconstruction in estimating the bolus arrival time and initial slope of enhancement from reconstructed images. We found that the ECA method reconstructed images at 0.25s temporal resolution with no significant loss in image fidelity, a 4x reduction in the error of bolus arrival time estimation in lesions (p < 0.01) and 11x error reduction in blood vessels (p < 0.01). Our results suggest that ECA is a powerful and versatile tool for breast DCE-MRI.

show abstract

Section: Theory and Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhancement-constrained acceleration: A robust reconstruction framework in breast DCE-MRI

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the process of monitoring the targets, different sublight fields are fused according to other perspective information. [22][23][24][25][26] The multi-perspective light field digraph G = (n, e, E) represents the network topology among the different perspectives, as shown in Figure 5.…”

Section: The Multi-perspective Distributed Information Fusion Mechanismmentioning

confidence: 99%

Maneuvering target recognition method based on multi-perspective light field reconstruction

Cai

Luo

Zhou

et al. 2019

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

It is difficult to reconstruct the complete light field, and the reconstructed light field can only recognize specific fixed targets. These have limited the applications of the light field in practice. To solve the problems above, this article introduces the multi-perspective distributed information fusion into light field reconstruction to monitor and recognize the maneuvering targets. First, the light field is represented as sub-light fields at different perspectives (i.e. the Multi-sensor distributed network), and sparse representation and reconstruction are then performed. Second, we establish the multi-perspective distributed information fusion under the condition of regional full-coverage constraints. Finally, the light field data from multiple perspectives are fused and the states of the maneuvering targets are estimated. Experimental results show that the light field reconstruction time of the proposed method is less than 583 s, and the reconstruction accuracy exceeds 92.447% compared with the existing spatially variable bidirectional reflectance distribution function, micro-lens array, and others. In the aspect of maneuvering target recognition, the recognition time of the algorithm in this article is no more than 3.5 s. The recognition accuracy of the algorithm in this article is up to 86.739%. Moreover, the more viewing angles used, the higher the accuracy.

show abstract

“…Multiparametric (mp) radiological imaging can accurately detect and characterize breast lesions using advanced quantitative parameters [ 11 , 12 , 13 , 14 ]. Dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI), which is a marker of the vascularity and permeability of breast lesions, can characterize malignant lesions by the rapid uptake of a contrast agent, followed by fast washout over time, and benign lesions by slow uptake and persistent or plateau washout [ 11 , 15 , 16 , 17 , 18 , 19 ]. Moreover, by interrogating the movement of water within the intra- and inter-cellular environments of normal and lesion tissue using diffusion-weighted imaging (DWI), with the apparent diffusion coefficient (ADC) of water map, can provide characterization of breast and other lesions [ 12 , 20 , 21 , 22 , 23 , 24 ].…”

Section: Introductionmentioning

confidence: 99%

Integrated Multiparametric Radiomics and Informatics System for Characterizing Breast Tumor Characteristics with the OncotypeDX Gene Assay

Jacobs

Umbricht

Parekh

et al. 2020

Cancers

View full text Add to dashboard Cite

Optimal use of multiparametric magnetic resonance imaging (mpMRI) can identify key MRI parameters and provide unique tissue signatures defining phenotypes of breast cancer. We have developed and implemented a new machine-learning informatic system, termed Informatics Radiomics Integration System (IRIS) that integrates clinical variables, derived from imaging and electronic medical health records (EHR) with multiparametric radiomics (mpRad) for identifying potential risk of local or systemic recurrence in breast cancer patients. We tested the model in patients (n = 80) who had Estrogen Receptor positive disease and underwent OncotypeDX gene testing, radiomic analysis, and breast mpMRI. The IRIS method was trained using the mpMRI, clinical, pathologic, and radiomic descriptors for prediction of the OncotypeDX risk score. The trained mpRad IRIS model had a 95% and specificity was 83% with an Area Under the Curve (AUC) of 0.89 for classifying low risk patients from the intermediate and high-risk groups. The lesion size was larger for the high-risk group (2.9 ± 1.7 mm) and lower for both low risk (1.9 ± 1.3 mm) and intermediate risk (1.7 ± 1.4 mm) groups. The lesion apparent diffusion coefficient (ADC) map values for high- and intermediate-risk groups were significantly (p < 0.05) lower than the low-risk group (1.14 vs. 1.49 × 10−3 mm2/s). These initial studies provide deeper insight into the clinical, pathological, quantitative imaging, and radiomic features, and provide the foundation to relate these features to the assessment of treatment response for improved personalized medicine.

show abstract

Dynamic field‐of‐view imaging to increase temporal resolution in the early phase of contrast media uptake in breast DCE‐MRI: A feasibility study

Cited by 9 publications

References 36 publications

Enhancement-constrained acceleration: A robust reconstruction framework in breast DCE-MRI

Enhancement-constrained acceleration: A robust reconstruction framework in breast DCE-MRI

Maneuvering target recognition method based on multi-perspective light field reconstruction

Integrated Multiparametric Radiomics and Informatics System for Characterizing Breast Tumor Characteristics with the OncotypeDX Gene Assay

Contact Info

Product

Resources

About