Accelerating multi-emitter localization in super-resolution localization microscopy with FPGA-GPU cooperative computation.

The real-time multi-emitter localization method is essential for advancing high-throughput super-resolution localization microscopy (HT-SRLM). In the past decade, the graphics processing unit (GPU) computation has been dominantly used to accelerate the execution speed of the multi-emitter localization method. However, if HT-SRLM is combined with a scientific complementary metal-oxide-semiconductor (sCMOS) camera working at full frame rate, real-time image processing is still difficult to achieve using this acceleration approach, thus resulting in a massive data storage challenge and even system crash. Here we take advantage of the cooperative acceleration power of field programming gate array (FPGA) computation and GPU computation, and propose a method called HCP-STORM to enable real-time multi-emitter localization. Using simulated images, we verified that HCP-STORM is capable of providing real-time image processing for raw images from a representative Hamamatsu Flash 4 V3 sCMOS camera working at full frame rate (that is, 2048×2048 pixels @ 10 ms exposure time). Using experimental images, we prove that HCP-STORM is 25 times faster than QC-STORM and 295 times faster than ThunderSTORM, with a small but acceptable degradation in image quality. This study shows the potential of FPGA-GPU cooperative computation in accelerating multi-emitter localization, and pushes a significant step toward the maturity of HT-SRLM technology.

PCIe-based FPGA-GPU heterogeneous computation for real-time multi-emitter fitting in super-resolution localization microscopy.

Real-time multi-emitter fitting is a key technology for advancing super-resolution localization microscopy (SRLM), especially when it is necessary to achieve dynamic imaging quality control and/or optimization of experimental conditions. However, with the increase of activation densities, the requirements in the computing resources would increase rapidly due to the complexity of the fitting algorithms, making it difficult to realize real-time multi-emitter fitting for emitter density more than 0.6 mol/µm2 in large field of view (FOV), even after acceleration with the popular Graphics Processing Unit (GPU) computation. Here we adopt the task parallelism strategy in computer science to construct a Peripheral Component Interconnect Express (PCIe) based all-in-one heterogeneous computing platform (AIO-HCP), where the data between two major parallel computing hardware, Field Programmable Gate Array (FPGA) and GPU, are interacted directly and executed simultaneously. Using simulated and experimental data, we verify that AIO-HCP could achieve a data throughput of up to ∼ 1.561 GB/s between FPGA and GPU. With this new platform, we develop a multi-emitter fitting method, called AIO-STORM, under big data stream parallel scheduling. We show that AIO-STORM is capable of providing real-time image processing on raw images with 100 µm × 100 µm FOV, 10 ms exposure time and 5.5 mol/µm2 structure density, without scarifying image quality. This study overcomes the data throughput limitation of heterogeneous devices, demonstrates the power of the PCIe-based heterogeneous computation platform, and offers opportunities for multi-scale stitching of super-resolution images.

[Exploring the mechanism of children elbow joint injury led to elbow varus].

OBJECTIVE: To explore the causes and mechanisms of children elbow joint injury led to elbow varus. METHODS: One hundred children with elbow varus causded by elbow joint injuries were retrospectively studied during January 2005 to April 2008 in department of child orthopedics involving 54 males and 46 females with an average age of 9.7 years old ranging from 7 to 16 years. The anterior-posterior and lateral position X-ray films of elbow joint continuous 12 to 18 months (means 15 months) of all the cases were collected. The postoperative X-ray films were observed to analyze the cause of elbow varus. RESULTS: There were 81 cases caused by epiphyseal dysplasia, growth imbalance, included 47 cases of humeral supracondylar fractures, 16 cases of entire epiphysis separation of distal humerus, 11 cases of humeral lateral condyle fractures, 7 cases of humeral medial condyle fractures. There were 19 cases caused by ulna drift and rotation of the distal fracture end,the ulnaris cortibal bone crushing and collapse involving 11 of humeral supracondylar fractures, 2 of entire epiphysis separation of distal humerus, 5 of humeral lateral condyle fractures, 1 of humeral medial condyle fractures. CONCLUSION: There are two main factors of formation for the children elbow varus deformity: the main causes is epiphysial dysplasia after fracture and imbalance growth of epiphysis; the secondary reasons is ulnar inclination of the distal fracture, rotation of the distal fracture, fracture shift such as ulnar bone cortical extrusion collapse.