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In mild cognitive impairment (MCI), identifying individuals at high risk for progressive cognitive deterioration can be useful for prognostication and intervention. This study quantitatively characterizes cognitive decline rates in MCI and tests whether volumetric data from baseline magnetic resonance imaging (MRI) can predict accelerated cognitive decline.The authors retrospectively examined Alzheimer Disease Neuroimaging Initiative data to obtain serial Mini-Mental Status Exam (MMSE) scores, diagnoses, and the following baseline MRI volumes: total intracranial volume, whole-brain and ventricular volumes, and volumes of the hippocampus, entorhinal cortex, fusiform gyrus, and medial temporal lobe. Subjects with24 months or4 measurements of MMSE data were excluded. Predictive modeling of fast cognitive decline (defined as0.6/year) from baseline volumetric data was performed on subjects with MCI using a single hidden layer neural network.Among 698 baseline MCI subjects, the median annual decline in the MMSE score was 1.3 for converters to dementia versus 0.11 for stable MCI (P0.001). A 0.6/year threshold captured dementia conversion with 82% accuracy (sensitivity 79%, specificity 85%, area under the receiver operating characteristic curve 0.88). Regional volumes on baseline MRI predicted fast cognitive decline with a test accuracy of 71%.An MMSE score decrease of0.6/year is associated with MCI-to-dementia conversion and can be predicted from baseline MRI.

Patients undergoing MRI may experience fear, claustrophobia, or other anxiety manifestations due to the typically lengthy, spatially constrictive, and noisy MRI acquisition process and in some cases are not able to tolerate completion of the study. This article discusses several patient-centered aspects of radiology practice that emphasize interpersonal interactions. Patient education and prescan communication represent 1 way to increase patients' awareness of what to expect during MRI and therefore mitigate anticipatory anxiety. Some patient interaction strategies to promote relaxation or calming effects are also discussed. Staff teamwork and staff training in communication and interpersonal skills are also described, along with literature evidence of effectiveness with respect to patient satisfaction and productivity endpoints. Attention to how radiologists, nurses, technologists, and other members of the radiology team interact with patients before or during the MRI scan could improve patients' motivation and ability to cooperate with the MRI scanning process as well as their subjective perceptions of the quality of their care. The topics discussed in this article are relevant not only to MRI operations but also to other clinical settings in which patient anxiety or motion represent impediments to optimal workflow.

Patient comfort is an important factor of a successful magnetic resonance (MR) examination, and improvements in the patient's MR scanning experience can contribute to improved image quality, diagnostic accuracy, and efficiency in the radiology department, and therefore reduced cost. Magnet designs that are more open and accessible, reduced auditory noise of MR examinations, light and flexible radiofrequency (RF) coils, and faster motion-insensitive imaging techniques can all significantly improve the patient experience in MR imaging. In this work, we review the design, development, and implementation of these physics and engineering approaches to improve patient comfort.

Artificial intelligence, particularly deep learning, offers several possibilities to improve the quality or speed of image acquisition in magnetic resonance imaging (MRI). In this article, we briefly review basic machine learning concepts and discuss commonly used neural network architectures for image-to-image translation. Recent examples in the literature describing application of machine learning techniques to clinical MR image acquisition or postprocessing are discussed. Machine learning can contribute to better image quality by improving spatial resolution, reducing image noise, and removing undesired motion or other artifacts. As patients occasionally are unable to tolerate lengthy acquisition times or gadolinium agents, machine learning can potentially assist MRI workflow and patient comfort by facilitating faster acquisitions or reducing exogenous contrast dosage. Although artificial intelligence approaches often have limitations, such as problems with generalizability or explainability, there is potential for these techniques to improve diagnostic utility, throughput, and patient experience in clinical MRI practice.

For many patients, numerous unpleasant features of the magnetic resonance imaging (MRI) experience such as scan duration, auditory noise, spatial confinement, and motion restrictions can lead to premature termination or low diagnostic quality of imaging studies. This article discusses practical, patient-oriented considerations that are helpful for radiologists contemplating ways to improve the MRI experience for patients. Patient friendly scanner properties are discussed, with an emphasis on literature findings of effectiveness in mitigating patient claustrophobia, other anxiety, or motion and on reducing scan incompletion rates or need for sedation. As shorter scanning protocols designed to answer specific diagnostic questions may be more practical and tolerable to the patient than a full-length standard-of-care examination, a few select protocol adjustments potentially useful for specific clinical settings are discussed. In addition, adjunctive devices such as audiovisual or other sensory aides that can be useful distractive approaches to reduce patient discomfort are considered. These modifications to the MRI scanning process not only allow for a more pleasant experience for patients, but they may also increase patient compliance and decrease patient movement to allow more efficient acquisition of diagnostic-quality images.

Periprocedural anxiety is a major cause of morbidity, particularly for interventional radiology procedures that often depend on conscious sedation. Management of anxiety and pain during image-guided procedures has traditionally relied on pharmacologic agents such as benzodiazepines and opioids. Although generally safe, use of these medications risks adverse events, and newer noninvasive, nonpharmacologic techniques have evolved to address patient needs. In this review, we explore the roles of hypnosis, structured empathic attention, anodyne imagery, music, video glasses, and mobile applications in reducing procedural anxiety and pain with the goal of improving patient satisfaction, operational efficiency, and clinical outcomes.