Inside AiCE DLR
The AiCE DLR applies the extraordinary classification abilities of a DCNN to the task of differentiating noise and signal in CT images and enhancing signal while suppressing noise to generate a high quality image for clinician interpretation. An overview of the AiCE DLR process is given in Figure 6. The AiCE reconstruction process begins in the raw data domain where AiCE analyzes the raw data and, armed with detailed scanner model information, makes modifications. These modifications in the projection domain improve output SNR and reduce artifacts, such as streaks. This raw data is then initially reconstructed to form a seed image, known as the “input layer”, to the DCNN.
Once the input image is fed into the DCNN, it is analyzed by several network layers referred to as “hidden layers.” The hidden layers of a DCNN contain convolutional layers, in which the component neurons act as feature selectors on small patches of data. In a traditional heuristic algorithm explicit image features, such as a curved edge, would be pre-selected by the programmer and “convolved,” i.e., filtered, with the image data. During the deep learning process, each neuron in a convolutional layer learns what features to look for based on the training data. AiCE’s DCNN has thousands of neurons, thoroughly sampling feature space. The network “learns” image features and their level of importance by adjusting the parameters, known as weight and bias, utilized by each neuron in the convolutional layer.
The output of the convolutional layer is the fed into an “activation layer.” In biology, a neuron only fires when the input to it surpasses a threshold. Similarly, the activation layer in a DCNN serves an analogous purpose in that, based on the strength of a neuronal response to the input data, the activation layer determines which neuron responses will pass to the next layer in the DCNN. After passing through all the hidden layers of the AICE neural network, the signal and noise are separated and a signal image, known as the output layer, is generated for the user.
One key to a successful DCNN lies in its network structure design, which impacts both image quality and reconstruction speed. To achieve the best computational efficiency and improve output image quality, network structure factors such as number of network layers, number of neurons in each layer, convolution kernel sizes, etc, were fully optimized in the AiCE algorithm. Elegant acceleration strategies and memory management technologies were carefully designed and integrated in the system to fully utilize hardware capabilities and maximize reconstruction speed.