Novel deep learning-based noise reduction technique for prostate magnetic resonance imaging

Abstract

Introduction: Magnetic resonance imaging (MRI) has played an increasingly major role in the evaluation of patients with prostate cancer, although prostate MRI presents several technical challenges. Newer techniques, such as deep learning (DL), have been applied to medical imaging, leading to improvements in image quality. Our goal is to evaluate the performance of a new deep learning-based reconstruction method, "DLR" in improving image quality and mitigating artifacts, which is now commercially available as AIR^TM Recon DL (GE Healthcare, Waukesha, WI). We hypothesize that applying DLR to the T2WI images of the prostate provides improved image quality and reduced artifacts.

Methods: This study included 31 patients with a history of prostate cancer that had a multiparametric MRI of the prostate with an endorectal coil (ERC) at 1.5 T or 3.0 T. Four series of T2-weighted images were generated in total: one set with the ERC signal turned on (ERC) and another set with the ERC signal turned off (Non-ERC). Each of these sets then reconstructed using two different reconstruction methods: conventional reconstruction (Conv) and DL Recon (DLR): ERC_DLR, ERC_Conv, Non-ERC_DLR, and Non-ERC_Conv. Three radiologists independently reviewed and scored the four sets of images for (i) image quality, (ii) artifacts, and (iii) visualization of anatomical landmarks and tumor.

Results: The Non-ERC_DLR scored as the best series for (i) overall image quality (p < 0.001), (ii) reduced artifacts (p < 0.001), and (iii) visualization of anatomical landmarks and tumor.

Conclusion: Prostate imaging without the use of an endorectal coil could benefit from deep learning reconstruction as demonstrated with T2-weighted imaging MRI evaluations of the prostate.

Keywords: Cancer; Deep learning; Endorectal coil; Magnetic resonance; Prostate.

Fig. 1

Axial T2-weighted images of a 61-year-old patient with a history of prostate cancer. a Images obtained with the ERC signal turned off and without DLR (Non-ERC_Conv). b Images generated with the ERC signal turned off and with DLR (Non-ERC_DLR). c Images obtained with the ERC signal turned on and without DLR (ERC_Conv). d Images obtained with the ERC signal turned on and with DLR (ERC_DLR). On c and d, note the pulsation artifacts from the coil (black arrows) and the near field artifacts (arrowhead) that are not seen on a or b