Recent Developments in Speeding up Prostate MRI.
Nida MirStefan J FransenJelmer M WolterinkJurgen J FüttererFrank F J SimonisPublished in: Journal of magnetic resonance imaging : JMRI (2023)
The increasing incidence of prostate cancer cases worldwide has led to a tremendous demand for multiparametric MRI (mpMRI). In order to relieve the pressure on healthcare, reducing mpMRI scan time is necessary. This review focuses on recent techniques proposed for faster mpMRI acquisition, specifically shortening T2W and DWI sequences while adhering to the PI-RADS (Prostate Imaging Reporting and Data System) guidelines. Speeding up techniques in the reviewed studies rely on more efficient sampling of data, ranging from the acquisition of fewer averages or b-values to adjustment of the pulse sequence. Novel acquisition methods based on undersampling techniques are often followed by suitable reconstruction methods typically incorporating synthetic priori information. These reconstruction methods often use artificial intelligence for various tasks such as denoising, artifact correction, improvement of image quality, and in the case of DWI, for the generation of synthetic high b-value images or apparent diffusion coefficient maps. Reduction of mpMRI scan time is possible, but it is crucial to maintain diagnostic quality, confirmed through radiological evaluation, to integrate the proposed methods into the standard mpMRI protocol. Additionally, before clinical integration, prospective studies are recommended to validate undersampling techniques to avoid potentially inaccurate results demonstrated by retrospective analysis. This review provides an overview of recently proposed techniques, discussing their implementation, advantages, disadvantages, and diagnostic performance according to PI-RADS guidelines compared to conventional methods. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 3.
Keyphrases
- prostate cancer
- diffusion weighted imaging
- artificial intelligence
- healthcare
- image quality
- computed tomography
- big data
- deep learning
- magnetic resonance imaging
- contrast enhanced
- machine learning
- radical prostatectomy
- electronic health record
- blood pressure
- primary care
- dual energy
- clinical practice
- risk factors
- randomized controlled trial
- high resolution
- magnetic resonance
- benign prostatic hyperplasia
- health information
- optical coherence tomography
- data analysis
- amino acid
- social media
- drug induced