The Effect of Sacrificing the Posterior Cruciate Ligament in Total Knee Arthroplasties That Use a Highly Congruent Polyethylene Component.

BACKGROUND: There is continued debate regarding retention versus sacrificing of the posterior cruciate ligament (PCL) in total knee arthroplasty (TKA). We sought to determine if there was a difference in range of motion (ROM) after TKA between patients with PCL sacrifice versus PCL retention when using a highly congruent polyethylene insert. METHODS: We conducted an Institutional Review Board approved retrospective study of consecutive patients receiving TKA using the same implant with a highly congruent polyethylene component implanted by one surgeon from November 2013 to January 2016. Patients were placed in 2 groups based on whether the PCL was intact or released at the time of surgery. Patient charts were reviewed for age, body mass index, PCL status at surgery (incompetent, kept intact, or released), and preoperative/postoperative knee ROM. RESULTS: Both groups were similar in average age (60.5 vs 60.6, respectively) and body mass index (33.3 vs 32.6, respectively). Postoperative tibial slope (5.5° PCL release, 6.6° PCL retained, P = .028) was the only alignment variable reaching significance; all other alignment and motion variables were similar. CONCLUSION: Results indicate that the PCL can be successfully retained with the use of a congruent bearing design, with no evident limitation in postoperative ROM or loss of stability due to the bearing in comparison to patients who undergo PCL release.

CoIR: Compressive Implicit Radar.

Using millimeter wave (mmWave) signals for imaging has an important advantage in that they can penetrate through poor environmental conditions such as fog, dust, and smoke that severely degrade optical-based imaging systems. However, mmWave radars, contrary to cameras and LiDARs, suffer from low angular resolution because of small physical apertures and conventional signal processing techniques. Sparse radar imaging, on the other hand, can increase the aperture size while minimizing the power consumption and read out bandwidth. This paper presents CoIR, an analysis by synthesis method that leverages the implicit neural network bias in convolutional decoders and compressed sensing to perform high accuracy sparse radar imaging. The proposed system is data set-agnostic and does not require any auxiliary sensors for training or testing. We introduce a sparse array design that allows for a 5.5× reduction in the number of antenna elements needed compared to conventional MIMO array designs. We demonstrate our system's improved imaging performance over standard mmWave radars and other competitive untrained methods on both simulated and experimental mmWave radar data.