Recent years have seen a resurgence of interest in inexpensive low magnetic field (< 0.3 T) MRI systems mainly due to advances in magnet, coil and gradient set designs. Most of these advances have focused on improving hardware and signal acquisition strategies, and far less on the use of advanced image reconstruction methods to improve attainable image quality at low field. We describe here the use of our end-to-end deep neural network approach (AUTOMAP) to improve the image quality of highly noise-corrupted low-field MRI data. We compare the performance of this approach to two additional state-of-the-art denoising pipelines. We find that AUTOMAP improves image reconstruction of data acquired on two very different low-field MRI systems: human brain data acquired at 6.5 mT, and plant root data acquired at 47 mT, demonstrating SNR gains above Fourier reconstruction by factors of 1.5- to 4.5-fold, and 3-fold, respectively. In these applications, AUTOMAP outperformed two different contemporary image-based denoising algorithms, and suppressed noise-like spike artifacts in the reconstructed images. The impact of domain-specific training corpora on the reconstruction performance is discussed. The AUTOMAP approach to image reconstruction will enable significant image quality improvements at low-field, especially in highly noise-corrupted environments.

In vivo investigations of enzymatic processes using non-invasive approaches are a long-lasting challenge. Recently, we showed that Overhauser-enhanced MRI is suitable to such a purpose. A β-phosphorylated nitroxide substrate prototype exhibiting keto-enol equilibrium upon enzymatic activity has been prepared. Upon enzymatic hydrolysis, a large variation of the phosphorus hyperfine coupling constant (Δa(P)=4 G) was observed. The enzymatic activities of several enzymes were conveniently monitored by electronic paramagnetic resonance (EPR). Using a 0.2 T MRI machine, in vitro and in vivo OMRI experiments were successfully performed, affording a 1200% enhanced MRI signal in vitro, and a 600% enhanced signal in vivo. These results highlight the enhanced imaging potential of these nitroxides upon specific enzymatic substrate-to-product conversion.

Theranostics combines therapeutic and diagnostic or drug deposition monitoring abilities of suitable molecules. Here we describe the first steps of building an alkoxyamine-based theranostic agent against cancer. The labile alkoxyamine ALK-1 (t(1/2) = 50 min at 37 °C) cleaves spontaneously to generate (1) a highly reactive free alkyl radical used as therapeutic agents to induce cell damages leading to cell death and (2) a stable nitroxide used as contrast agent for Overhauser-enhanced magnetic resonance imaging (OMRI). The ALK-1 toxicity was studied extensively in vitro on the glioblastoma cell line U87-MG. Cell viability appeared to be dependent on ALK-1 concentration and on the time of the observation following alkoxyamine treatment. For instance, the LC50 at 72 h was 250 μM. Data showed that cell toxicity was specifically due to the in situ released alkyl radical. This radical induced oxidative stress, mitochondrial changes, and ultimately the U87 cell apoptosis. The nitroxide production, during the alkoxyamine homolysis, was monitored by OMRI, showing a progressive MRI signal enhancement to 6-fold concomitant to the ALK-1 homolysis. In conclusion, we have demonstrated for the first time that the alkoxyamines are promising molecules to build theranostic tools against solid tumors.