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Background: In vivo cartilage deformation has been studied by static magnetic resonance imaging (MRI) with in situ loading, but knowledge about strain dynamics after load onset and release is scarce. Purpose: To measure the dynamics of patellofemoral cartilage deformation and recovery in response to in situ loading and unloading by using MRI with prospective motion correction. Study Type: Prospective. Subjects: Ten healthy male volunteers (age: [31.4 ± 3.2] years). Field Strength/Sequence: T1‐weighted RF‐spoiled 2D gradient‐echo sequence with a golden angle radial acquisition scheme, augmented with prospective motion correction, at 3 T. Assessment: In situ knee loading was realized with a flexion angle of approximately 40° using an MR‐compatible pneumatic loading device. The loading paradigm consisted of 2 minutes of unloaded baseline followed by a 5‐minute loading bout with 50% body weight and an unloading period of 38 minutes. The cartilage strain was assessed as the mean distance between patellar and femoral bone–cartilage interfaces as a percentage of the initial (pre‐load) distance. Statistical Tests: Wilcoxon signed‐rank tests (significance level: P < 0.05), Pearson correlation coefficient (r). Results: The cartilage compression and recovery behavior was characterized by a viscoelastic response. The elastic compression ([−12.5 ± 3.1]%) was significantly larger than the viscous compression ([−7.6 ± 1.5]%) and the elastic recovery ([10.5 ± 2.1]%) was significantly larger than the viscous recovery ([6.1 ± 1.8]%). There was a significant residual offset strain ([−3.6 ± 2.3]%) across the cohort. A significant negative correlation between elastic compression and elastic recovery was observed (r = −0.75). Data Conclusion: The in vivo cartilage compression and recovery time course in response to loading was successfully measured via dynamic MRI with prospective motion correction. The clinical relevance of the strain characteristics needs to be assessed in larger subject and patient cohorts. Level of Evidence: 2 Technical Efficacy: Stage 1 [ABSTRACT FROM AUTHOR]

Complex engineered systems are often equipped with suites of sensors and ancillary devices that monitor their performance and maintenance needs. MRI scanners are no different in this regard. Some of the ancillary devices available to support MRI equipment, the ones of particular interest here, have the distinction of actually participating in the image acquisition process itself. Most commonly, such devices are used to monitor physiological motion or variations in the scanner's imaging fields, allowing the imaging and/or reconstruction process to adapt as imaging conditions change. "Classic" examples include electrocardiography (ECG) leads and respiratory bellows to monitor cardiac and respiratory motion, which have been standard equipment in scan rooms since the early days of MRI. Since then, many additional sensors and devices have been proposed to support MRI acquisitions. The main physical properties that they measure may be primarily "mechanical" (eg acceleration, speed, and torque), "acoustic" (sound and ultrasound), "optical" (light and infrared), or "electromagnetic" in nature. A review of these ancillary devices, as currently available in clinical and research settings, is presented here. In our opinion, these devices are not in competition with each other: as long as they provide useful and unique information, do not interfere with each other and are not prohibitively cumbersome to use, they might find their proper place in future suites of sensors. In time, MRI acquisitions will likely include a plurality of complementary signals. A little like the microbiome that provides genetic diversity to organisms, these devices can provide signal diversity to MRI acquisitions and enrich measurements. Machine‐learning (ML) algorithms are well suited at combining diverse input signals toward coherent outputs, and they could make use of all such information toward improved MRI capabilities. Evidence Level: 2 Technical Efficacy: Stage 1 [ABSTRACT FROM AUTHOR]