Magnetic resonance imaging (MRI) is a powerful imaging modality, but one of its drawbacks is its relatively long scanning time to acquire high-resolution images. Reducing the scanning time has become a critical area of focus in MRI, aiming to enhance patient comfort, reduce motion artifacts, and increase MRI throughput. In the past 5 years, artificial intelligence (AI)-based algorithms, particularly deep learning models, have been developed to reconstruct high-resolution images from significantly fewer data points. These new techniques significantly enhance MRI efficiency, improve patient comfort and lower patient motion artifacts. Improving MRI throughput with lower scanning duration increases accessibility, potentially reducing the need for additional MRI machines and associated costs. Several fields can benefit from shortened protocols, especially for routine exams. In oncologic imaging, faster MRI scans can facilitate more regular monitoring of cancer patients. In patients suffering from neurological disorders, rapid brain imaging can aid in the quick assessment of conditions like stroke, multiple sclerosis, and epilepsy, improving patient outcomes. In chronic inflammatory disease, faster imaging may help in reducing the interval between imaging to better check therapy outcomes. Additionally, reducing scanning time could effectively help MRI to play a role in emergency medicine and acute conditions such as trauma or acute ischaemic stroke. The purpose of this paper is to describe and discuss the advantages and disadvantages of introducing deep learning reconstruction techniques to reduce MRI scanning times in clinical practice.

Dual-energy CT (DECT) is an innovative technology that is increasingly widespread in clinical practice. DECT allows for tissue characterization beyond that of conventional CT as imaging is performed using different energy spectra that can help differentiate tissues based on their specific attenuation properties at different X-ray energies. The most employed post-processing applications of DECT include virtual monoenergetic images (VMIs), iodine density maps, virtual non-contrast images (VNC), and virtual non-calcium (VNCa) for bone marrow edema (BME) detection. The diverse array of images obtained through DECT acquisitions offers numerous benefits, including enhanced lesion detection and characterization, precise determination of material composition, decreased iodine dose, and reduced artifacts. These versatile applications play an increasingly significant role in tumor assessment and oncologic imaging, encompassing the diagnosis of primary tumors, local and metastatic staging, post-therapy evaluation, and complication management. This article provides a comprehensive review of the principal applications and post-processing techniques of DECT, with a specific focus on its utility in managing oncologic patients.