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Estimation of the capillary level input function for dynamic contrast-enhanced MRI of the breast using a deep learning approach

Bae, Jonghyun; Huang, Zhengnan; Knoll, Florian; Geras, Krzysztof; Pandit Sood, Terlika; Feng, Li; Heacock, Laura; Moy, Linda; Kim, Sungheon Gene
PURPOSE/OBJECTIVE:To develop a deep learning approach to estimate the local capillary-level input function (CIF) for pharmacokinetic model analysis of DCE-MRI. METHODS:A deep convolutional network was trained with numerically simulated data to estimate the CIF. The trained network was tested using simulated lesion data and used to estimate voxel-wise CIF for pharmacokinetic model analysis of breast DCE-MRI data using an abbreviated protocol from women with malignant (n = 25) and benign (n = 28) lesions. The estimated parameters were used to build a logistic regression model to detect the malignancy. RESULT/RESULTS:The pharmacokinetic parameters estimated using the network-predicted CIF from our breast DCE data showed significant differences between the malignant and benign groups for all parameters. Testing the diagnostic performance with the estimated parameters, the conventional approach with arterial input function (AIF) showed an area under the curve (AUC) between 0.76 and 0.87, and the proposed approach with CIF demonstrated similar performance with an AUC between 0.79 and 0.81. CONCLUSION/CONCLUSIONS:This study shows the feasibility of estimating voxel-wise CIF using a deep neural network. The proposed approach could eliminate the need to measure AIF manually without compromising the diagnostic performance to detect the malignancy in the clinical setting.
PMID: 35001423
ISSN: 1522-2594
CID: 5118282

fastMRI+, Clinical pathology annotations for knee and brain fully sampled magnetic resonance imaging data

Zhao, Ruiyang; Yaman, Burhaneddin; Zhang, Yuxin; Stewart, Russell; Dixon, Austin; Knoll, Florian; Huang, Zhengnan; Lui, Yvonne W; Hansen, Michael S; Lungren, Matthew P
Improving speed and image quality of Magnetic Resonance Imaging (MRI) using deep learning reconstruction is an active area of research. The fastMRI dataset contains large volumes of raw MRI data, which has enabled significant advances in this field. While the impact of the fastMRI dataset is unquestioned, the dataset currently lacks clinical expert pathology annotations, critical to addressing clinically relevant reconstruction frameworks and exploring important questions regarding rendering of specific pathology using such novel approaches. This work introduces fastMRI+, which consists of 16154 subspecialist expert bounding box annotations and 13 study-level labels for 22 different pathology categories on the fastMRI knee dataset, and 7570 subspecialist expert bounding box annotations and 643 study-level labels for 30 different pathology categories for the fastMRI brain dataset. The fastMRI+ dataset is open access and aims to support further research and advancement of medical imaging in MRI reconstruction and beyond.
PMCID:8983757
PMID: 35383186
ISSN: 2052-4463
CID: 5201602

New-Generation Low-Field Magnetic Resonance Imaging of Hip Arthroplasty Implants Using Slice Encoding for Metal Artifact Correction: First In Vitro Experience at 0.55 T and Comparison With 1.5 T

Khodarahmi, Iman; Brinkmann, Inge M; Lin, Dana J; Bruno, Mary; Johnson, Patricia M; Knoll, Florian; Keerthivasan, Mahesh B; Chandarana, Hersh; Fritz, Jan
OBJECTIVES/OBJECTIVE:Despite significant progress, artifact-free visualization of the bone and soft tissues around hip arthroplasty implants remains an unmet clinical need. New-generation low-field magnetic resonance imaging (MRI) systems now include slice encoding for metal artifact correction (SEMAC), which may result in smaller metallic artifacts and better image quality than standard-of-care 1.5 T MRI. This study aims to assess the feasibility of SEMAC on a new-generation 0.55 T system, optimize the pulse protocol parameters, and compare the results with those of a standard-of-care 1.5 T MRI. MATERIALS AND METHODS/METHODS:Titanium (Ti) and cobalt-chromium total hip arthroplasty implants embedded in a tissue-mimicking American Society for Testing and Materials gel phantom were evaluated using turbo spin echo, view angle tilting (VAT), and combined VAT and SEMAC (VAT + SEMAC) pulse sequences. To refine an MRI protocol at 0.55 T, the type of metal artifact reduction techniques and the effect of various pulse sequence parameters on metal artifacts were assessed through qualitative ranking of the images by 3 expert readers while taking measured spatial resolution, signal-to-noise ratios, and acquisition times into consideration. Signal-to-noise ratio efficiency and artifact size of the optimized 0.55 T protocols were compared with the 1.5 T standard and compressed-sensing SEMAC sequences. RESULTS:Overall, the VAT + SEMAC sequence with at least 6 SEMAC encoding steps for Ti and 9 for cobalt-chromium implants was ranked higher than other sequences for metal reduction (P < 0.05). Additional SEMAC encoding partitions did not result in further metal artifact reductions. Permitting minimal residual artifacts, low magnetic susceptibility Ti constructs may be sufficiently imaged with optimized turbo spin echo sequences obviating the need for SEMAC. In cross-platform comparison, 0.55 T acquisitions using the optimized protocols are associated with 45% to 64% smaller artifacts than 1.5 T VAT + SEMAC and VAT + compressed-sensing/SEMAC protocols at the expense of a 17% to 28% reduction in signal-to-noise ratio efficiency. B1-related artifacts are invariably smaller at 0.55 T than 1.5 T; however, artifacts related to B0 distortion, although frequently smaller, may appear as signal pileups at 0.55 T. CONCLUSIONS:Our results suggest that new-generation low-field SEMAC MRI reduces metal artifacts around hip arthroplasty implants to better advantage than current 1.5 T MRI standard of care. While the appearance of B0-related artifacts changes, reduction in B1-related artifacts plays a major role in the overall benefit of 0.55 T.
PMID: 35239614
ISSN: 1536-0210
CID: 5174642

Bayesian Uncertainty Estimation of Learned Variational MRI Reconstruction

Narnhofer, Dominik; Effland, Alexander; Kobler, Erich; Hammernik, Kerstin; Knoll, Florian; Pock, Thomas
Recent deep learning approaches focus on improving quantitative scores of dedicated benchmarks, and therefore only reduce the observation-related (aleatoric) uncertainty. However, the model-immanent (epistemic) uncertainty is less frequently systematically analyzed. In this work, we introduce a Bayesian variational framework to quantify the epistemic uncertainty. To this end, we solve the linear inverse problem of undersampled MRI reconstruction in a variational setting. The associated energy functional is composed of a data fidelity term and the total deep variation (TDV) as a learned parametric regularizer. To estimate the epistemic uncertainty we draw the parameters of the TDV regularizer from a multivariate Gaussian distribution, whose mean and covariance matrix are learned in a stochastic optimal control problem. In several numerical experiments, we demonstrate that our approach yields competitive results for undersampled MRI reconstruction. Moreover, we can accurately quantify the pixelwise epistemic uncertainty, which can serve radiologists as an additional resource to visualize reconstruction reliability.
PMID: 34506279
ISSN: 1558-254x
CID: 4998482

Virtual mouse brain histology from multi-contrast MRI via deep learning

Liang, Zifei; Lee, Choong H; Arefin, Tanzil M; Dong, Zijun; Walczak, Piotr; Hai Shi, Song; Knoll, Florian; Ge, Yulin; Ying, Leslie; Zhang, Jiangyang
PMID: 35088711
ISSN: 2050-084x
CID: 5154822

Deep Learning Reconstruction Enables Highly Accelerated Biparametric MR Imaging of the Prostate

Johnson, Patricia M; Tong, Angela; Donthireddy, Awani; Melamud, Kira; Petrocelli, Robert; Smereka, Paul; Qian, Kun; Keerthivasan, Mahesh B; Chandarana, Hersh; Knoll, Florian
BACKGROUND:Early diagnosis and treatment of prostate cancer (PCa) can be curative; however, prostate-specific antigen is a suboptimal screening test for clinically significant PCa. While prostate magnetic resonance imaging (MRI) has demonstrated value for the diagnosis of PCa, the acquisition time is too long for a first-line screening modality. PURPOSE/OBJECTIVE:To accelerate prostate MRI exams, utilizing a variational network (VN) for image reconstruction. STUDY TYPE/METHODS:Retrospective. SUBJECTS/METHODS:One hundred and thirteen subjects (train/val/test: 70/13/30) undergoing prostate MRI. FIELD STRENGTH/SEQUENCE/UNASSIGNED:3.0 T; a T2 turbo spin echo (TSE) T2-weighted image (T2WI) sequence in axial and coronal planes, and axial echo-planar diffusion-weighted imaging (DWI). ASSESSMENT/RESULTS:, and apparent diffusion coefficient map-according to the Prostate Imaging Reporting and Data System (PI-RADS v2.1), for both VN and standard reconstructions. Accuracy of PI-RADS ≥3 for clinically significant cancer was computed. Projected scan time of the retrospectively under-sampled biparametric exam was also computed. STATISTICAL TESTS/UNASSIGNED:One-sided Wilcoxon signed-rank test was used for comparison of image quality. Sensitivity, specificity, positive predictive value, and negative predictive value were calculated for lesion detection and grading. Generalized estimating equation with cluster effect was used to compare differences between standard and VN bp-MRI. A P-value of <0.05 was considered statistically significant. RESULTS:(Reader 1: 3.20 ± 0.70 (Standard), 3.40 ± 0.75 (VN) P = 0.98; Reader 2: 2.85 ± 0.81 (Standard), 3.00 ± 0.79 (VN) P = 0.93; Reader 3: 4.45 ± 0.72 (Standard), 4.05 ± 0.69 (VN) P = 0.02; Reader 4: 4.50 ± 0.69 (Standard), 4.45 ± 0.76 (VN) P = 0.50). In the lesion evaluation study, there was no significant difference in the number of PI-RADS ≥3 lesions identified on standard vs. VN bp-MRI (P = 0.92, 0.59, 0.87) with similar sensitivity and specificity for clinically significant cancer. The average scan time of the standard clinical biparametric exam was 11.8 minutes, and this was projected to be 3.2 minutes for the accelerated exam. DATA CONCLUSION/UNASSIGNED:Diagnostic accelerated biparametric prostate MRI exams can be performed using deep learning methods in <4 minutes, potentially enabling rapid screening prostate MRI. LEVEL OF EVIDENCE/METHODS:3 TECHNICAL EFFICACY: Stage 5.
PMID: 34877735
ISSN: 1522-2586
CID: 5110242

Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction

Muckley, Matthew J; Riemenschneider, Bruno; Radmanesh, Alireza; Kim, Sunwoo; Jeong, Geunu; Ko, Jingyu; Jun, Yohan; Shin, Hyungseob; Hwang, Dosik; Mostapha, Mahmoud; Arberet, Simon; Nickel, Dominik; Ramzi, Zaccharie; Ciuciu, Philippe; Starck, Jean-Luc; Teuwen, Jonas; Karkalousos, Dimitrios; Zhang, Chaoping; Sriram, Anuroop; Huang, Zhengnan; Yakubova, Nafissa; Lui, Yvonne W; Knoll, Florian
Accelerating MRI scans is one of the principal outstanding problems in the MRI research community. Towards this goal, we hosted the second fastMRI competition targeted towards reconstructing MR images with subsampled k-space data. We provided participants with data from 7,299 clinical brain scans (de-identified via a HIPAA-compliant procedure by NYU Langone Health), holding back the fully-sampled data from 894 of these scans for challenge evaluation purposes. In contrast to the 2019 challenge, we focused our radiologist evaluations on pathological assessment in brain images. We also debuted a new Transfer track that required participants to submit models evaluated on MRI scanners from outside the training set. We received 19 submissions from eight different groups. Results showed one team scoring best in both SSIM scores and qualitative radiologist evaluations. We also performed analysis on alternative metrics to mitigate the effects of background noise and collected feedback from the participants to inform future challenges. Lastly, we identify common failure modes across the submissions, highlighting areas of need for future research in the MRI reconstruction community.
PMID: 33929957
ISSN: 1558-254x
CID: 4853732

Artificial Intelligence for MR Image Reconstruction: An Overview for Clinicians

Lin, Dana J; Johnson, Patricia M; Knoll, Florian; Lui, Yvonne W
Artificial intelligence (AI) shows tremendous promise in the field of medical imaging, with recent breakthroughs applying deep-learning models for data acquisition, classification problems, segmentation, image synthesis, and image reconstruction. With an eye towards clinical applications, we summarize the active field of deep-learning-based MR image reconstruction. We review the basic concepts of how deep-learning algorithms aid in the transformation of raw k-space data to image data, and specifically examine accelerated imaging and artifact suppression. Recent efforts in these areas show that deep-learning-based algorithms can match and, in some cases, eclipse conventional reconstruction methods in terms of image quality and computational efficiency across a host of clinical imaging applications, including musculoskeletal, abdominal, cardiac, and brain imaging. This article is an introductory overview aimed at clinical radiologists with no experience in deep-learning-based MR image reconstruction and should enable them to understand the basic concepts and current clinical applications of this rapidly growing area of research across multiple organ systems.
PMID: 32048372
ISSN: 1522-2586
CID: 4304412

CG-SENSE revisited: Results from the first ISMRM reproducibility challenge

Maier, Oliver; Baete, Steven Hubert; Fyrdahl, Alexander; Hammernik, Kerstin; Harrevelt, Seb; Kasper, Lars; Karakuzu, Agah; Loecher, Michael; Patzig, Franz; Tian, Ye; Wang, Ke; Gallichan, Daniel; Uecker, Martin; Knoll, Florian
PURPOSE/OBJECTIVE:The aim of this work is to shed light on the issue of reproducibility in MR image reconstruction in the context of a challenge. Participants had to recreate the results of "Advances in sensitivity encoding with arbitrary k-space trajectories" by Pruessmann et al. METHODS: The task of the challenge was to reconstruct radially acquired multicoil k-space data (brain/heart) following the method in the original paper, reproducing its key figures. Results were compared to consolidated reference implementations created after the challenge, accounting for the two most common programming languages used in the submissions (Matlab/Python). RESULTS:Visually, differences between submissions were small. Pixel-wise differences originated from image orientation, assumed field-of-view, or resolution. The reference implementations were in good agreement, both visually and in terms of image similarity metrics. DISCUSSION AND CONCLUSION/CONCLUSIONS:While the description level of the published algorithm enabled participants to reproduce CG-SENSE in general, details of the implementation varied, for example, density compensation or Tikhonov regularization. Implicit assumptions about the data lead to further differences, emphasizing the importance of sufficient metadata accompanying open datasets. Defining reproducibility quantitatively turned out to be nontrivial for this image reconstruction challenge, in the absence of ground-truth results. Typical similarity measures like NMSE of SSIM were misled by image intensity scaling and outlier pixels. Thus, to facilitate reproducibility, researchers are encouraged to publish code and data alongside the original paper. Future methodological papers on MR image reconstruction might benefit from the consolidated reference implementations of CG-SENSE presented here, as a benchmark for methods comparison.
PMID: 33179826
ISSN: 1522-2594
CID: 4663022

Training a neural network for Gibbs and noise removal in diffusion MRI

Muckley, Matthew J; Ades-Aron, Benjamin; Papaioannou, Antonios; Lemberskiy, Gregory; Solomon, Eddy; Lui, Yvonne W; Sodickson, Daniel K; Fieremans, Els; Novikov, Dmitry S; Knoll, Florian
PURPOSE/OBJECTIVE:To develop and evaluate a neural network-based method for Gibbs artifact and noise removal. METHODS:A convolutional neural network (CNN) was designed for artifact removal in diffusion-weighted imaging data. Two implementations were considered: one for magnitude images and one for complex images. Both models were based on the same encoder-decoder structure and were trained by simulating MRI acquisitions on synthetic non-MRI images. RESULTS:Both machine learning methods were able to mitigate artifacts in diffusion-weighted images and diffusion parameter maps. The CNN for complex images was also able to reduce artifacts in partial Fourier acquisitions. CONCLUSIONS:The proposed CNNs extend the ability of artifact correction in diffusion MRI. The machine learning method described here can be applied on each imaging slice independently, allowing it to be used flexibly in clinical applications.
PMID: 32662910
ISSN: 1522-2594
CID: 4528102