Synergistic multi-contrast cardiac magnetic resonance image reconstruction.

Cardiac magnetic resonance imaging (CMR) is an important tool for the non-invasive diagnosis of a variety of cardiovascular diseases. Parametric mapping with multi-contrast CMR is able to quantify tissue alterations in myocardial disease and promises to improve patient care. However, magnetic resonance imaging is an inherently slow imaging modality, resulting in long acquisition times for parametric mapping which acquires a series of cardiac images with different contrasts for signal fitting or dictionary matching. Furthermore, extra efforts to deal with respiratory and cardiac motion by triggering and gating further increase the scan time. Several techniques have been developed to speed up CMR acquisitions, which usually acquire less data than that required by the Nyquist-Shannon sampling theorem, followed by regularized reconstruction to mitigate undersampling artefacts. Recent advances in CMR parametric mapping speed up CMR by synergistically exploiting spatial-temporal and contrast redundancies. In this article, we will review the recent developments in multi-contrast CMR image reconstruction for parametric mapping with special focus on low-rank and model-based reconstructions. Deep learning-based multi-contrast reconstruction has recently been proposed in other magnetic resonance applications. These developments will be covered to introduce the general methodology. Current technical limitations and potential future directions are discussed. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 1'.

The future direction of imaging in prostate cancer: MRI with or without contrast injection.

BACKGROUND: Multiparametric MRI (mpMRI) is the "state of the art" management tool for patients with suspicion of prostate cancer (PCa). The role of non-contrast MRI is investigated to move toward a more personalized, less invasive, and highly cost-effective PCa diagnostic workup. OBJECTIVE: To perform a non-systematic review of the existing literature to highlight strength and flaws of performing non-contrast MRI, and to provide a critical overview of the international scientific production on the topic. MATERIALS AND METHODS: Online databases (Medline, PubMed, and Web of Science) were searched for original articles, systematic review and meta-analysis, and expert opinion papers. RESULTS: Several investigations have shown comparable diagnostic accuracy of biparametric (bpMRI) and mpMRI for the detection of PCa. The advantage of abandoning contrast-enhanced sequences improves operational logistics, lowering costs, acquisition time, and side effects. The main limitations of bpMRI are that most studies comparing non-contrast with contrast MRI come from centers with high expertise that might not be reproducible in the general community setting; besides, reduced protocols might be insufficient for estimation of the intra- and extra-prostatic extension and regional disease. The mentioned observations suggest that low-quality mpMRI for the general population might represent the main shortage to overcome. DISCUSSION: Non-contrast MRI future trends are likely represented by PCa screening and the application of artificial intelligence (AI) tools. PCa screening is still a controversial topic; bpMRI has become one of the most promising diagnostic applications, as it is a more sensitive test for PCa early detection, compared to serum PSA level test. Also, AI applications and radiomic have been the object of several studies investigating PCa detection using bpMRI, showing encouraging results. CONCLUSION: Today, the accessibility to MRI for early detection of PCa is a priority. Results from prospective, multicenter, multireader, and paired validation studies are needed to provide evidence supporting its role in the clinical practice.

Multiparametric MRI for prostate cancer diagnosis: current status and future directions.

The current diagnostic pathway for prostate cancer has resulted in overdiagnosis and consequent overtreatment as well as underdiagnosis and missed diagnoses in many men. Multiparametric MRI (mpMRI) of the prostate has been identified as a test that could mitigate these diagnostic errors. The performance of mpMRI can vary depending on the population being studied, the execution of the MRI itself, the experience of the radiologist, whether additional biomarkers are considered and whether mpMRI-targeted biopsy is carried out alone or in addition to systematic biopsy. A number of challenges to implementation remain, such as ensuring high-quality execution and reporting of mpMRI and ensuring that this diagnostic pathway is cost-effective. Nevertheless, emerging clinical trial data support the adoption of this technology as part of the standard of care for the diagnosis of prostate cancer.