Purpose: To apply tracer kinetic models as temporal constraints during reconstruction of under-sampled dynamic contrast enhanced (DCE) MRI.
Methods: A library of concentration v.s time profiles is simulated for a range of physiological kinetic parameters. The library is reduced to a dictionary of temporal bases, where each profile is approximated by a sparse linear combination of the bases. Image reconstruction is formulated as estimation of concentration profiles and sparse model coefficients with a fixed sparsity level. Simulations are performed to evaluate modeling error, and error statistics in kinetic parameter estimation in presence of noise. Retrospective under-sampling experiments are performed on a brain tumor DCE digital reference object (DRO) at different signal to noise levels (SNR=20-40) at (k-t) space under-sampling factor (R=20), and 12 brain tumor in- vivo 3T datasets at (R=20-40). The approach is compared against an existing compressed sensing based temporal finite-difference (tFD) reconstruction approach.
Results: Simulations demonstrate that sparsity levels of 2 and 3 model the library profiles from the Patlak and extended Tofts-Kety (ETK) models, respectively. Noise sensitivity analysis showed equivalent kinetic parameter estimation error statistics from noisy concentration profiles, and model approximated profiles. DRO based experiments showed good fidelity in recovery of kinetic maps from 20-fold under- sampled data at SNRs between 10-30. In-vivo experiments demonstrated reduced bias and uncertainty in kinetic mapping with the proposed approach compared to tFD at R>=20.
Conclusions: Tracer kinetic models can be applied as temporal constraints during DCE-MRI reconstruction, enabling more accurate reconstruction from under- sampled data. The approach is flexible, can use several kinetic models, and does not require tuning of regularization parameters.
MITK-ModelFit: A generic open-source framework for model fits and their exploration in medical imaging -- design, implementation and application on the example of DCE-MRI
Sustainable Deep Learning-Based Breast Lesion Segmentation: Impact of Breast Region Segmentation on Performance
Norwegian University of Science and Technology（挪威科技大学）；More og Romsdal Hospital Trust（默勒-鲁姆斯达尔医院信托基金）；Stavanger University Hospital（斯塔万格大学医院）；The University of Stavanger（斯塔万格大学）；Akershus University Hospital（阿克什胡斯大学医院）；University of Oslo（奥斯陆大学）
Robustness of breast lesion segmentation under MRI undersampling improves with k-space-aware deep learning
Institute for AI in Medicine (IKIM), University Hospital Essen（人工智能医学研究所（IKIM），埃森大学医院）；Department of Physics, Technical University Dortmund（物理系，多特蒙德技术大学）；Division of Radiology, German Cancer Research Center (DKFZ)（放射学部，德国癌症研究中心（DKFZ））；Cancer Research Center Cologne Essen (CCCE), University Medicine Essen（科隆埃森癌症研究中心（CCCE），埃森大学医学中心）；RACOON Study Group, Site Essen（RACOON研究组，埃森站点）；German Cancer Consortium (DKTK), Partner Site Essen（德国癌症联合会（DKTK），埃森合作伙伴站点）；Medical Faculty and Faculty of Computer Science, University of Duisburg-Essen（医学系和计算机科学系，杜伊斯堡-埃森大学）
