Transfer-learning is a key ingredient to fast deep learning-based 4D liver MRI reconstruction.

Time-resolved volumetric magnetic resonance imaging (4D MRI) could be used to address organ motion in image-guided interventions like tumor ablation. Current 4D reconstruction techniques are unsuitable for most interventional settings because they are limited to specific breathing phases, lack temporal/spatial resolution, and have long prior acquisitions or reconstruction times. Deep learning-based (DL) 4D MRI approaches promise to overcome these shortcomings but are sensitive to domain shift. This work shows that transfer learning (TL) combined with an ensembling strategy can help alleviate this key challenge. We evaluate four approaches: pre-trained models from the source domain, models directly trained from scratch on target domain data, models fine-tuned from a pre-trained model and an ensemble of fine-tuned models. For that the data base was split into 16 source and 4 target domain subjects. Comparing ensemble of fine-tuned models (N = 10) with directly learned models, we report significant improvements (P < 0.001) of the root mean squared error (RMSE) of up to 12% and the mean displacement (MDISP) of up to 17.5%. The smaller the target domain data amount, the larger the effect. This shows that TL + Ens significantly reduces beforehand acquisition time and improves reconstruction quality, rendering it a key component in making 4D MRI clinically feasible for the first time in the context of 4D organ motion models of the liver and beyond.

Automatic detection of prostate cancer grades and chronic prostatitis in biparametric MRI.

BACKGROUND AND OBJECTIVE: With emerging evidence to improve prostate cancer (PCa) screening, multiparametric magnetic prostate imaging is becoming an essential noninvasive component of the diagnostic routine. Computer-aided diagnostic (CAD) tools powered by deep learning can help radiologists interpret multiple volumetric images. In this work, our objective was to examine promising methods recently proposed in the multigrade prostate cancer detection task and to suggest practical considerations regarding model training in this context. METHODS: We collected 1647 fine-grained biopsy-confirmed findings, including Gleason scores and prostatitis, to form a training dataset. In our experimental framework for lesion detection, all models utilized 3D nnU-Net architecture that accounts for anisotropy in the MRI data. First, we explore an optimal range of b-values for diffusion-weighted imaging (DWI) modality and its effect on the detection of clinically significant prostate cancer (csPCa) and prostatitis using deep learning, as the optimal range is not yet clearly defined in this domain. Next, we propose a simulated multimodal shift as a data augmentation technique to compensate for the multimodal shift present in the data. Third, we study the effect of incorporating the prostatitis class alongside cancer-related findings at three different granularities of the prostate cancer class (coarse, medium, and fine) and its impact on the detection rate of the target csPCa. Furthermore, ordinal and one-hot encoded (OHE) output formulations were tested. RESULTS: An optimal model configuration with fine class granularity (prostatitis included) and OHE has scored the lesion-wise partial Free-Response Receiver Operating Characteristic (FROC) area under the curve (AUC) of 1.94 (CI 95%: 1.76-2.11) and patient-wise ROC AUC of 0.874 (CI 95%: 0.793-0.938) in the detection of csPCa. Inclusion of the auxiliary prostatitis class has demonstrated a stable relative improvement in specificity at a false positive rate (FPR) of 1.0 per patient, with an increase of 3%, 7%, and 4% for coarse, medium, and fine class granularities. CONCLUSIONS: This paper examines several configurations for model training in the biparametric MRI setup and proposes optimal value ranges. It also shows that the fine-grained class configuration, including prostatitis, is beneficial for detecting csPCa. The ability to detect prostatitis in all low-risk cancer lesions suggests the potential to improve the quality of the early diagnosis of prostate diseases. It also implies an improved interpretability of the results by the radiologist.

Predicting 4D liver MRI for MR-guided interventions.

Organ motion poses an unresolved challenge in image-guided interventions like radiation therapy, biopsies or tumor ablation. In the pursuit of solving this problem, the research field of time-resolved volumetric magnetic resonance imaging (4D MRI) has evolved. However, current techniques are unsuitable for most interventional settings because they lack sufficient temporal and/or spatial resolution or have long acquisition times. In this work, we propose a novel approach for real-time, high-resolution 4D MRI with large fields of view for MR-guided interventions. To this end, we propose a network-agnostic, end-to-end trainable, deep learning formulation that enables the prediction of a 4D liver MRI with respiratory states from a live 2D navigator MRI. Our method can be used in two ways: First, it can reconstruct high quality fast (near real-time) 4D MRI with high resolution (209×128×128 matrix size with isotropic 1.8mm voxel size and 0.6s/volume) given a dynamic interventional 2D navigator slice for guidance during an intervention. Second, it can be used for retrospective 4D reconstruction with a temporal resolution of below 0.2s/volume for motion analysis and use in radiation therapy. We report a mean target registration error (TRE) of 1.19±0.74mm, which is below voxel size. We compare our results with a state-of-the-art retrospective 4D MRI reconstruction. Visual evaluation shows comparable quality. We compare different network architectures within our formulation. We show that small training sizes with short acquisition times down to 2 min can already achieve promising results and 24 min are sufficient for high quality results. Because our method can be readily combined with earlier time reducing methods, acquisition time can be further decreased while also limiting quality loss. We show that an end-to-end, deep learning formulation is highly promising for 4D MRI reconstruction.

Domain adaptation for segmentation of critical structures for prostate cancer therapy.

Preoperative assessment of the proximity of critical structures to the tumors is crucial in avoiding unnecessary damage during prostate cancer treatment. A patient-specific 3D anatomical model of those structures, namely the neurovascular bundles (NVB) and the external urethral sphincters (EUS), can enable physicians to perform such assessments intuitively. As a crucial step to generate a patient-specific anatomical model from preoperative MRI in a clinical routine, we propose a multi-class automatic segmentation based on an anisotropic convolutional network. Our specific challenge is to train the network model on a unique source dataset only available at a single clinical site and deploy it to another target site without sharing the original images or labels. As network models trained on data from a single source suffer from quality loss due to the domain shift, we propose a semi-supervised domain adaptation (DA) method to refine the model's performance in the target domain. Our DA method combines transfer learning and uncertainty guided self-learning based on deep ensembles. Experiments on the segmentation of the prostate, NVB, and EUS, show significant performance gain with the combination of those techniques compared to pure TL and the combination of TL with simple self-learning ([Formula: see text] for all structures using a Wilcoxon's signed-rank test). Results on a different task and data (Pancreas CT segmentation) demonstrate our method's generic application capabilities. Our method has the advantage that it does not require any further data from the source domain, unlike the majority of recent domain adaptation strategies. This makes our method suitable for clinical applications, where the sharing of patient data is restricted.