Investigation of a Novel Deep Learning-Based Computed Tomography Perfusion Mapping Framework for Functional Lung Avoidance Radiotherapy

Abstract

Functional lung avoidance radiation therapy aims to minimize dose delivery to the normal lung tissue while favoring dose deposition in the defective lung tissue based on the regional function information. However, the clinical acquisition of pulmonary functional images is resource-demanding, inconvenient, and technically challenging. This study aims to investigate the deep learning-based lung functional image synthesis from the CT domain. Forty-two pulmonary macro-aggregated albumin SPECT/CT perfusion scans were retrospectively collected from the hospital. A deep learning-based framework (including image preparation, image processing, and proposed convolutional neural network) was adopted to extract features from 3D CT images and synthesize perfusion as estimations of regional lung function. Ablation experiments were performed to assess the effects of each framework component by removing each element of the framework and analyzing the testing performances. Major results showed that the removal of the CT contrast enhancement component in the image processing resulted in the largest drop in framework performance, compared to the optimal performance (~12%). In the CNN part, all the three components (residual module, ROI attention, and skip attention) were approximately equally important to the framework performance; removing one of them resulted in a 3–5% decline in performance. The proposed CNN improved ~4% overall performance and ~350% computational efficiency, compared to the U-Net model. The deep convolutional neural network, in conjunction with image processing for feature enhancement, is capable of feature extraction from CT images for pulmonary perfusion synthesis. In the proposed framework, image processing, especially CT contrast enhancement, plays a crucial role in the perfusion synthesis. This CTPM framework provides insights for relevant research studies in the future and enables other researchers to leverage for the development of optimized CNN models for functional lung avoidance radiation therapy.

A deep learning method for translating 3DCT to SPECT ventilation imaging: First comparison with 81mKr‐gas SPECT ventilation imaging

“…Ren et al implemented the DL-based approach to transform 3DCT to SPECT perfusion images and investigated the influence of different model architectures and preprocessing. 24,25 They reported that there were some cases in which the lowfunctional region was predicted to be a high-functional region, which can be partly attributed to the low occurrence and small volume of the low-functional regions compared with high-functional regions. Our results showed similar trends (Figures 3 and 4).…”

A review of deep learning-based three-dimensional medical image registration methods

“…Subsequent layers combine these extracted features for advanced object detection. This property makes DL a suitable tool for image-related tasks, such as computer-aided diagnosis (27,28), image enhancement (29), image synthesis (30,31), and functional information derivation (32,33).…”

Artificial intelligence in functional imaging of the lung

“…Like ventilation imaging, recent studies have also shown the use of CNN approaches to estimate lung perfusion from single energy CT scans. Ren et al 94 employed an attention U-net architecture to synthesize albumin SPECT/CT perfusion mapping from non-contrast CT scans to enable functional lung avoidance in radiotherapy planning. 94 Their proposed neural network is superior to the traditional U-Net architecture and is able to identify features from the CT domain that are compatible with perfusion defects with moderate correlation.…”

“…Ren et al 94 employed an attention U-net architecture to synthesize albumin SPECT/CT perfusion mapping from non-contrast CT scans to enable functional lung avoidance in radiotherapy planning. 94 Their proposed neural network is superior to the traditional U-Net architecture and is able to identify features from the CT domain that are compatible with perfusion defects with moderate correlation. Despite the limited size of the training (31 subjects) and testing data (11 subjects), these results illustrate the ability of deep learning approaches to estimate both ventilation and perfusion functional imaging from routine non-contrast CT scans under a common imaging platform.…”