Learning Task-Specific Strategies for Accelerated MRI

TL;DR

Tackle is proposed as a unified co-design framework for jointly optimizing subsampling, reconstruction, and prediction strategies for the performance on downstream tasks and achieves an improved performance on various tasks over traditional CS-MRI methods.

Abstract

Compressed sensing magnetic resonance imaging (CS-MRI) seeks to recover visual information from subsampled measurements for diagnostic tasks. Traditional CS-MRI methods often separately address measurement subsampling, image reconstruction, and task prediction, resulting in a suboptimal end-to-end performance. In this work, we propose TACKLE as a unified co-design framework for jointly optimizing subsampling, reconstruction, and prediction strategies for the performance on downstream tasks. The naïve approach of simply appending a task prediction module and training with a task-specific loss leads to suboptimal downstream performance. Instead, we develop a training procedure where a backbone architecture is first trained for a generic pre-training task (image reconstruction in our case), and then fine-tuned for different downstream tasks with a prediction head. Experimental results on multiple public MRI datasets show that TACKLE achieves an improved performance on various tasks over traditional CS-MRI methods. We also demonstrate that TACKLE is robust to distribution shifts by showing that it generalizes to a new dataset we experimentally collected using different acquisition setups from the training data. Without additional fine-tuning, TACKLE leads to both numerical and visual improvements compared to existing baselines. We have further implemented a learned 4$\times$-accelerated sequence on a Siemens 3T MRI Skyra scanner. Compared to the fully-sampling scan that takes 335 seconds, our optimized sequence only takes 84 seconds, achieving a four-fold time reduction as desired, while maintaining high performance.

Figures (15)

• Figure 1: Comparison between (a) traditional CS-MRI, (b) a naïve approach to task-specific CS-MRI, and (c) the proposed Tackle framework. Compared with panel (a) that separately deals with reconstruction and task prediction, panel (b) is a simple extension of co-design methods for solving downstream tasks by adding a learnable mapping from measurements to task predictions. However, this naïve approach leads to a suboptimal performance and can even lead to a worse task prediction accuracy, as shown in the example above. On the other hand, we introduce Tackle for effectively learning task-specific CS-MRI strategies. Tackle is first pre-trained for generic reconstruction, and then all three modules are fine-tuned for a more specific downstream task. We find that this training schedule allows Tackle to robustly learn generalizable task-specific strategies. In the above knee segmentation example, all three approaches are trained with the same architectures for the reconstructor (second module) and predictor (third module). Nevertheless, Tackle significantly outperforms the two baseline approaches.
• Figure 2: Block diagram of the proposed framework Tackle and a summary of the investigated datasets and settings. Tackle uses a task-specific loss to jointly optimize a sampler, a retriever, and an optional predictor, ranging from scanner-level sampling to human-level diagnosis. A summary of the investigated settings is presented in the bottom left panel. FSE, GRE, DESS, and FLAIR stand for fast spin echo, gradient echo, double-echo steady-state, and fluid-attenuated inversion recovery, respectively. We comprehensively investigate multiple CS-MRI tasks on a variety of common MRI settings with six datasets.
• Figure 3: Visual examples of two Meniscus Tear samples reconstructed by different methods in the 16$\times$ acceleration single-coil setting. For each reconstruction, the full-FOV PSNR is labeled in white, and the local PSNR for the ROI is in orange. Note how Tackle$_\text{ROI}$ recovers the structure and details of the ROI more accurately than the two baselines, as indicated by the red arrows. The better recovery of Tackle$_\text{ROI}$ over the ROI leads to a more accurate diagnosis of the Meniscus Tear. We emphasize that the location of the ROI is not an input to any of these models and is only used for evaluating the accuracy of each method on the region that contains the pathology.
• Figure 4: Comparison of a subsampling PSF optimized for full-FOV reconstruction and another optimized for the reconstruction of menicus tear (MT) ROIs. Optimizing for MT ROI reconstruction leads to around 40% improvement on the vertical resolution in terms of the full width at half maximum (FWHM), as shown by the PSF profiles in the bottom panel. This improved vertical resolution leads to a better reconstruction of the meniscus that has horizontal anatomy.
• Figure 5: Box plots of the knee tissue segmentation results under 16$\times$ (a) and 64$\times$ (b). Within the rectangle between each pair of methods, the top number is the percentage of samples that get improved and the bottom number is the $p$-value given by the paired samples t-test. A higher percentage and lower $p$-value indicate a more significant improvement. We also provide the 95% confidence intervals for all methods below their names. For both acceleration ratios, Tackle$_\text{seg.}$ outperforms other baselines in terms of all the statistical measures.