Clinically Practical Sparse Reconstruction for 4D Prostate DCE-MRI: Algorithm and Initial Experience

Dynamic 3D contrast-enhanced MRI (DCE-MRI) is increasingly used clinically for prostate cancer lesion detection, staging, treatment planning/monitoring, and recurrence detection. However, achieving high spatiotemporal resolution and SNR in this application is challenging given the target signals transiency and glands medial location. Sparsity-driven image reconstruction is an increasingly popular tool that mitigate the tradeoff between resolution and SNR (relative to conventional methods). In this work, we present an alternating direction method-of-multipliers (ADMM) optimization strategy specifically for our Cartesian acquisition protocol that enables <5 minute 4D DCE-MRI sparse reconstructions. After overviewing the mechanics of this algorithm, we show that its results were consistently preferred for diagnosis over the clinical standard (SENSE) by radiologists in 19 suspected prostate cancer patient studies.

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