Abstract: Disclosed are an input drum assembly for improving performance of a 68RFE transmission and a method. The cooling and lubricating efficiencies of the input drum assembly in the 68RFE transmission can be improved by an input drum assembly and/or transmission housing; mounting spaces of clutch plates and clutch friction discs can be increased by the input drum assembly, so that thicknesses of the clutch plates 14 and discs or the number thereof can be increased, and a torque borne by the 68RFE transmission can be increased finally; a reinforcing rib is increased when the transmission housing is cast, so that the overall strength of the transmission housing can be enhanced; and an inner wall is cut after the transmission housing is cast, so that a diameter of the input drum assembly can be increased, thereby increasing diameters of the clutch plates and discs, and finally increasing the torque.

Abstract: A dual energy x-ray imaging system and method of operation includes an artificial intelligence-based motion correction system to minimize the effects of motion artifacts in images produced by the imaging system. The motion correction system is trained to apply simulated motion to various objects of interest within the LE and HE projections in the training dataset to improve registration of the LE and HE projections. The motion correction system is also trained to enhance the correction of small motion artifacts using noise attenuation and subtraction image-based edge detection on the training dataset images reduce noise from the LE projection, consequently improving small motion artifact correction. The motion correction system additionally employs separate motion corrections for soft and bone tissue in forming subtraction soft tissue and bone tissue images, and includes a motion alarm to indicate when motion between LE and HE projections requires a retake of the projections.

Abstract: Disclosed are an input drum assembly for improving performance of a 68RFE transmission and a method. The cooling and lubricating efficiencies of the input drum assembly in the 68RFE transmission can be improved by an input drum assembly and/or transmission housing; mounting spaces of clutch plates and clutch friction discs can be increased by the input drum assembly, so that thicknesses of the clutch plates 14 and discs or the number thereof can be increased, and a torque borne by the 68RFE transmission can be increased finally; a reinforcing rib is increased when the transmission housing is cast, so that the overall strength of the transmission housing can be enhanced; and an inner wall is cut after the transmission housing is cast, so that a diameter of the input drum assembly can be increased, thereby increasing diameters of the clutch plates and discs, and finally increasing the torque.

Abstract: Techniques are described that facilitate dynamic multimodal segmentation selection and fusion in medical imaging. In one example embodiment, a computer processing system receives a segmentation dataset comprising a combination of different image segmentations of an anatomical object of interest respectively segmented via different segmentation models from different medical images captured of the (same) anatomical object, wherein the different medical images and the different image segmentations vary with respect to at least one of, capture modality, acquisition protocol, or acquisition parameters. The system employs a dynamic ranking protocol as opposed to a static ranking protocol to determine ranking scores for the different image segmentations that control relative contributions of the different image segmentations in association with combining the different image segmentations into a fused segmentation for the anatomical object.

Abstract: An image processing system is provided. The image processing system includes a display, a processor, and a memory. The memory stores processor-executable code that when executed by the processor causes receiving an image of a region of interest of a patient with an enteric tube or line disposed within the region of interest, detecting the medical tube or line within the image, generating a combined image by superimposing graphical markers on the image that indicate placement or misplacement of the enteric tube or line, and displaying the combined image on a display. In further aspects, a classification of the enteric tube or line (e.g., correctly placed tube present, malpositioned tube present, and so forth) may be determined and communicated to one or more clinicians. Additionally, the outputs of the image processing system may also be provided to facilitate triage of patients, helping prioritize which tube placements require further attention and in what order.

Abstract: Systems/techniques that facilitate multi-layer image registration are provided. In various embodiments, a system can access a first image and a second image. In various aspects, the system can generate, via execution of a machine learning model on the first image and the second image, a plurality of registration fields and a plurality of weight matrices that respectively correspond to the plurality of registration fields. In various instances, the system can register the first image with the second image based on the plurality of registration fields and the plurality of weight matrices.

Abstract: Provided is a suspension arm manufactured by casting. The suspension arm includes a processing reference hole formed in the casting. The processing reference hole is formed to be elongated in a direction of bending of the suspension arm that occurs in a shaping step. A recess is formed which has one end thereof communicating with the processing reference hole and another end thereof being open to an outer side.

Abstract: Various methods and systems are provided for automatically registering and stitching images. In one example, a method includes entering a first image of a subject and a second image of the subject to a model trained to output a transformation matrix based on the first image and the second image, where the model is trained with a plurality of training data sets, each training data set including a pair of images, a mask indicating a region of interest (ROI), and associated ground truth, automatically stitching together the first image and the second image based on the transformation matrix to form a stitched image, and outputting the stitched image for display on a display device and/or storing the stitched image in memory.

Abstract: Disclosed in the present disclosure is a commutative encryption and watermarking method based on a chaotic system and a zero watermark for vector geospatial data. According to the method, firstly, the vector geospatial data are scrambled and encrypted by using chaotic sequences generated by a composite chaotic system. Then, vector geospatial elements are randomly combined in pairs. A feature matrix is constructed according to the number of vertex coordinates of the vector geospatial elements in combinations, and the parity of the number. Finally, an XOR operation is performed on the feature matrix and the watermark image to construct a zero watermark image, and the zero watermark is constructed through invariant features of the vector geospatial data.

Abstract: A tomosynthesis machine allows for faster image acquisition and improved signal-to-noise by acquiring a projection attenuation data and using machine learning to identify a subset of the projection attenuation data for the production of thinner slices and/or higher resolution slices using machine learning.

Abstract: Systems/techniques that facilitate multi-layer image registration are provided. In various embodiments, a system can access a first image and a second image. In various aspects, the system can generate, via execution of a machine learning model on the first image and the second image, a plurality of registration fields and a plurality of weight matrices that respectively correspond to the plurality of registration fields. In various instances, the system can register the first image with the second image based on the plurality of registration fields and the plurality of weight matrices.

Abstract: Provided is a method for obtaining a double-stranded sequence by single-stranded rolling circle amplification, comprising: 1) performing rolling circle amplification reaction on single-stranded circular DNA by means of a first primer to obtain an amplified sequence, the first primer being complementary to a partial region of the single-stranded circular DNA, and the single-stranded circular DNA having a break mechanism that can cause the single-stranded circular DNA to ring-open; 2) ring-opening the single-stranded circular DNA by means of the break mechanism to obtain single-stranded linear DNA; and 3) using the single-stranded linear DNA as a second primer and using the amplified sequence obtained in step 1) as a template to perform amplification reaction to obtain an amplified double-stranded sequence.

Abstract: This disclosure relates to anti-CitH3 (citrullinated histone H3) antibodies, antigen-binding fragments, and the uses thereof.

Abstract: Techniques are described for optimizing deep learning model performance using image harmonization as a pre-processing step. According to an embodiment, a method comprises decomposing, by a system operatively coupled to a processor, an input image into sub-images. The method further comprises harmonizing the sub-images with corresponding reference sub-images of at least one reference image based on two or more different statistical values respectively calculated for the sub-images and the corresponding reference-sub images, resulting in transformation of the sub-images into modified sub-images images. In some implementations, the modified sub-images can be combined into a harmonized image having a more similar appearance to the at least one reference image relative to the input image.

Abstract: Systems/techniques that facilitate improved deep learning image processing are provided. In various embodiments, a system can access a medical image, wherein pixels or voxels of the medical image can be allocated among a plurality of regions. In various aspects, the system can generate, via execution of a deep learning neural network on the medical image, a set of region-wise parameter maps, wherein a region-wise parameter map can consist of one predicted parameter per region of the medical image. In various instances, the system can generate a transformed version of the medical image by feeding the set of region-wise parameter maps to an analytical transformation function. In various cases, the system can render the transformed version of the medical image on an electronic display. In various aspects, the plurality of regions can be irregular or tissue-based.

Abstract: Systems/techniques that facilitate AI-based region-of-interest masks for improved data reconstructions are provided. In various embodiments, a system can access a set of two-dimensional medical scan projections. In various aspects, the system can generate a set of two-dimensional region-of-interest masks respectively corresponding to the set of two-dimensional medical scan projections. In various instances, the system can generate a region-of-interest visualization based on the set of two-dimensional region-of-interest masks and the set of two-dimensional medical scan projections. In various cases, the system can generate the set of two-dimensional region-of-interest masks by executing a machine learning segmentation model on the set of two-dimensional medical scan projections.

Abstract: Various methods and systems are provided for training a denoising system for a digital imaging system. The denoising system can be a deep learning denoising system formed as a blind or non-blind denoising system in which the training dataset provided to the denoising system includes a noisy image formed with simulated noise added to a clean digital image, and a reference image formed of the clean image having residual noise added thereto, where the residual noise is a fraction of the simulated noise used to form the noisy image. The use of the residual noise within the reference image of the training dataset teaches the DL network in the training process to remove less than all the noise during subsequent inferencing of digital images from the digital imaging system. By leaving selected amounts of noise in the digital images, the denoiser can be tuned to improve image attributes and texture.

Abstract: A dual energy x-ray imaging system and method of operation includes an artificial intelligence-based motion correction system to minimize the effects of motion artifacts in images produced by the imaging system. The motion correction system is trained to apply simulated motion to various objects of interest within the LE and HE projections in the training dataset to improve registration of the LE and HE projections. The motion correction system is also trained to enhance the correction of small motion artifacts using noise attenuation and subtraction image-based edge detection on the training dataset images reduce noise from the LE projection, consequently improving small motion artifact correction. The motion correction system additionally employs separate motion corrections for soft and bone tissue in forming subtraction soft tissue and bone tissue images, and includes a motion alarm to indicate when motion between LE and HE projections requires a retake of the projections.

Abstract: Systems/techniques that facilitate deep learning image analysis with increased modularity and reduced footprint are provided. In various embodiments, a system can access medical imaging data. In various aspects, the system can perform, via execution of a deep learning neural network, a plurality of inferencing tasks on the medical imaging data. In various instances, the deep learning neural network can comprise a common backbone in parallel with a plurality of task-specific backbones. In various cases, the plurality of task-specific backbones can respectively correspond to the plurality of inferencing tasks.

Abstract: Methods and systems are provided for inferring thickness and volume of one or more object classes of interest in two-dimensional (2D) medical images, using deep neural networks. In an exemplary embodiment, a thickness of an object class of interest may be inferred by acquiring a 2D medical image, extracting features from the 2D medical image, mapping the features to a segmentation mask for an object class of interest using a first convolutional neural network (CNN), mapping the features to a thickness mask for the object class of interest using a second CNN, wherein the thickness mask indicates a thickness of the object class of interest at each pixel of a plurality of pixels of the 2D medical image; and determining a volume of the object class of interest based on the thickness mask and the segmentation mask.