Objective To propose an improved discrete wavelet transform (DWT ) and to apply it in multimodal medical im‐age fusion .Methods Firstly ,the source medical images were initially transformed into the high frequency and low frequency images by DWT ;then the high frequency part adopted the big direction absolute values ,which effectively preserved the detailed informa‐tion of image ,while the low frequency part used the fusion rule of local energy ratio for preserving the most of image information ;finally ,the discrete wavelet reverse transform was used for reconstructing the fusion sub‐images into fusion image .Results By comparing the fusion images by 3 groups of medical images ,this proposed algorithm was superior to other existing algorithms in the aspects of subjective visual effect and objective evaluation indicators .Conclusion The proposed algorithm of medical image fusion is rapid and accurate ,has excellent performance in the noise environment and clinical examples ,can obtain the high quality fusion im‐age and has higher clinical application value .

Objective: To compare the application of iASSIST assisted total knee arthroplasty (TKA) and three-dimentional(3D) printing personal specific instrument (PSI) assist TKA in the treatment of osteoarthritis (OA). Methods: Clinical data of 47 patients with OA admitted at Department of Orthopaedic Surgery in Nanjing Medical University Nanjing Hospital between April and September 2016 were retrospectively reviewed, including 20 males and 27 females, aging from 57 to 77 years with mean age of (63.8±8.2) years. They were randomly divided into iASSIST-TKA group (23 patients) and PSI-TKA group (24 patients). The data such as hip knee ankle (HKA) angle, frontal femoral component (FFC) angle, frontal tibial component (FTC) angle, lateral femoral component (LFC) angle, lateral tibial component (LTC) angle, time of operation, post-operative wound drainage, period of hospitalization, visual analog scale (VAS) and Knee Society Score (KSS) at 1 day, 7 days, 14 days, 1 month and 3 months were recorded and compared between the two groups. T test was used to compare measurement data, Fisher exact test and χ² test were applied to enumeration data in comparison among groups, and Kruskal-Wallis test was applied to ranked data. Results: The deviation values of HKA, FFC, LFC, FTC and LTC angles were all below 3°(-2° to 2°), and there were no significant difference between iASSIST-TKA group and PSI-TKA group (Z=-0.610 to 0.000, P=0.542 to 1.000). Compared to PSI-TKA group, the time of operation was long((80.7±8.8) minutes vs.(60.2±7.8) minutes), the amount of post-operative wound drainage was increased((210.7±32.1) ml vs.(185.5±30.2)ml) and the period of hospitalization decreased((5.4±2.4) d vs.(6.7±1.6) d) in iASSIST-TKA group, there were significant difference(t=-2.190 to 8.460, P=0.000 to 0.033). There were no significant difference in intra-operative blood drainage((18.4±5.4) ml vs.(17.3±6.2) ml) between the two groups(t=0.650, P=0.521). PSI-TKA group had a superior VAS score(4.8±0.6 vs. 5.5±0.9, 3.6±0.8 vs. 4.3±0.9), KSS clinical score(49.3±5.5 vs. 44.2±6.4, 54.9±4.0 vs. 50.8±4.2) and KSS function score(44.1±2.9 vs. 41.2±3.5, 49.6±3.8 vs. 46.6±3.2) in 1 day and 7 days post-operation(t=-3.420 to 3.150, P=0.001 to 0.007). There were no significant difference in VAS and KSS score in 14 days, 1 month and 3 months post-operation(t=-1.390 to 0.530, P=0.170 to 1.000) between the two groups. Conclusions The iASSIST-TKA and PSI-TKA can help to make TKA procedure more accurately. iASSIST-TKA may take longer time of operation and have slower recovery, PSI-TKA may need more X-ray input and longer period of hospitalization. The long-term research of both techniques may be valuable for the further clinical usage.