Marcus Morris is averaging 8.6 points, 4.1 rebounds a game this season. The 6-9 power forward was drafted 14th overall in 2011 out of Kansas, but he failed to crack the Rockets' deep rotation of power forwards as a rookie and has been overshadowed by fellow 23-year-old Patrick Patterson this season.
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Cracking sounds emitted from human synovial joints have been attributed historically to the sudden collapse of a cavitation bubble formed as articular surfaces are separated. Unfortunately, bubble collapse as the source of joint cracking is inconsistent with many physical phenomena that define the joint cracking phenomenon. Here we present direct evidence from real-time magnetic resonance imaging that the mechanism of joint cracking is related to cavity formation rather than bubble collapse. In this study, ten metacarpophalangeal joints were studied by inserting the finger of interest into a flexible tube tightened around a length of cable used to provide long-axis traction. Before and after traction, static 3D T1-weighted magnetic resonance images were acquired. During traction, rapid cine magnetic resonance images were obtained from the joint midline at a rate of 3.2 frames per second until the cracking event occurred. As traction forces increased, real-time cine magnetic resonance imaging demonstrated rapid cavity inception at the time of joint separation and sound production after which the resulting cavity remained visible. Our results offer direct experimental evidence that joint cracking is associated with cavity inception rather than collapse of a pre-existing bubble. These observations are consistent with tribonucleation, a known process where opposing surfaces resist separation until a critical point where they then separate rapidly creating sustained gas cavities. Observed previously in vitro, this is the first in-vivo macroscopic demonstration of tribonucleation and as such, provides a new theoretical framework to investigate health outcomes associated with joint cracking.
Given the above, the objective of this study was to characterize the events associated with joint cracking within the joint itself using real-time cine magnetic resonance imaging (cine MRI). Here we present direct evidence from cine MRI that the mechanism of joint cracking is related to cavity formation rather than bubble collapse.
Cine MRI imaging revealed a sequence of events consistent with that outlined by Roston and Wheeler Haines [1]. A video of these events can be viewed in the supplemental material (S1 Video). Four still frames from the 4th right MCP joint depicting the characteristic intra-articular events associated with joint cracking are presented in Fig. 3: resting joint geometry (Fig. 3A), a time frame just prior to cracking (Fig. 3B), a time frame just after cracking (Fig. 3C) and a final frame following release of distraction forces (Fig. 3D). In the supplemental materials, a series of images is presented showing the moment just after joint cracking in all MCP joints investigated (S1 Fig.). Fig. 4 shows a time series of these events to display joint separation distance and changes in MRI signal-intensities for a representative finger cracking event. The joint separation distance shows a slow increase to the point of joint release at 6.2 seconds, as indicated by the vertical marker (left frame). The MRI signal intensity within the intra-articular space (Region 1) drops to reveal a signal void at the same time as the joint expands (6.2 seconds). Control regions in the fluid outside of the intra-articular space (Region 2) and in the bone (Region 3) show relatively unchanging signal intensities over the experiment. All regions were moved in each frame to track the motion of the bones. Finally, the signal intensity in the intra-articular space (Region 1) showed a steady increase with distraction just prior to joint cracking (Fig. 3B). Still images in the bottom of Fig. 4 highlight frames prior to, and just after, joint cracking which demonstrate a signal increase in Region 1 and the subsequent signal drop in the same region.
This study employed cine MRI to visualize joint cracking in real time. To our knowledge, cine MRI has not been used previously to characterize this phenomenon. Congruent with historic results, cine MRI demonstrated minimal joint surface separation in the resting phase prior to joint cracking followed by rapid joint separation during the crack itself. Incongruent with the prevailing perspective from the last half century, cine MRI revealed rapid cavity inception associated with concurrent sound production and joint separation. Following these events, the resulting cavity was never seen to collapse; the cavity formed at the time of rapid joint separation then persisted past the point of sound production.
Subjects were instructed to abstain from knuckle cracking for at least 4 hours preceding the examination. Subjects with and without a history of habitual knuckle cracking were examined while in the seated position with hands on a height-adjustable stand. The MPJs were examined sonographically in the longitudinal plane before, during, and after each MPJ was distracted manually with longitudinal traction, hyperflexion, or hyperextension by the subject. In addition to static images immediately (
In joints that cracked, sonography showed hyperechoic foci, suggesting the formation of intraarticular gas. In contrast, this finding was not observed in association with the distraction maneuvers that did not result in a joint crack. These findings corroborate existing studies [9, 11]. Thus, sonographic examinations were interpreted as positive for showing knuckle cracking if new echogenic foci appeared either during real-time cine images during distraction or on static images immediately after distraction (Fig. 1). Conversely, sonographic examinations were interpreted as showing no crack if no echogenic foci appeared in the joint during real-time cine imaging during distraction and on static imaging immediately after distraction (Fig. 2).
The pervasive real-time surveillancemachine that has been developed for online advertising is rapidlyexpanding into other fields including politics, pricing, credit scoring, andrisk management. Insurers all over the world have started to offer theircustomers programs involvingreal-time tracking of behaviors such as car driving, health activities, grocerypurchases, or visits to the fitness studio. New players in insuranceanalytics and financial technology predict individual health risks based onconsumer data, as well as the creditworthiness of individuals based onbehavioral data on phone calls or web searches.
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