Sparse-view computed tomography (CT) holds great promise for speeding up data acquisition and reducing radiation dose in CT scans. Recent advances in reconstruction algorithms for sparse-view CT, such as iterative reconstruction algorithms, obtained high-quality image while requiring advanced computing power. Lately, deep learning (DL) has been widely used in various applications and has obtained many remarkable outcomes. In this paper, we propose a new method for sparse-view CT reconstruction based on the DL approach. The method can be divided into two steps. First, filter backprojection (FBP) was used to reconstruct the CT image from sparsely sampled sinogram. Then, the FBP results were fed to a DL neural network, which is a DenseNet and deconvolution-based network (DD-Net). The DD-Net combines the advantages of DenseNet and deconvolution and applies shortcut connections to concatenate DenseNet and deconvolution to accelerate the training speed of the network; all of those operations can greatly increase the depth of network while enhancing the expression ability of the network. After the training, the proposed DD-Net achieved a competitive performance relative to the state-of-the-art methods in terms of streaking artifacts removal and structure preservation. Compared with the other state-of-the-art reconstruction methods, the DD-Net method can increase the structure similarity by up to 18% and reduce the root mean square error by up to 42%. These results indicate that DD-Net has great potential for sparse-view CT image reconstruction.

Computed tomography (CT) has been widely used for medical diagnosis, assessment, and therapy planning and guidance. In reality, CT images may be affected adversely in the presence of metallic objects, which could lead to severe metal artifacts and influence clinical diagnosis or dose calculation in radiation therapy. In this article, we propose a generalizable framework for metal artifact reduction (MAR) by simultaneously leveraging the advantages of image domain and sinogram domain-based MAR techniques. We formulate our framework as a sinogram completion problem and train a neural network (SinoNet) to restore the metal-affected projections. To improve the continuity of the completed projections at the boundary of metal trace and thus alleviate new artifacts in the reconstructed CT images, we train another neural network (PriorNet) to generate a good prior image to guide sinogram learning, and further design a novel residual sinogram learning strategy to effectively utilize the prior image information for better sinogram completion. The two networks are jointly trained in an end-to-end fashion with a differentiable forward projection (FP) operation so that the prior image generation and deep sinogram completion procedures can benefit from each other. Finally, the artifact-reduced CT images are reconstructed using the filtered backward projection (FBP) from the completed sinogram. Extensive experiments on simulated and real artifacts data demonstrate that our method produces superior artifact-reduced results while preserving the anatomical structures and outperforms other MAR methods.

Substantial progress has been made in using deep learning for cancer detection and diagnosis in medical images. Yet, there is limited success on prediction of treatment response and outcomes, which has important implications for personalized treatment strategies. A significant hurdle for clinical translation of current data-driven deep learning models is lack of interpretability, often attributable to a disconnect from the underlying pathobiology. Here, we present a biology-guided deep learning approach that enables simultaneous prediction of the tumor immune and stromal microenvironment status as well as treatment outcomes from medical images. We validate the model for predicting prognosis of gastric cancer and the benefit from adjuvant chemotherapy in a multi-center international study. Further, the model predicts response to immune checkpoint inhibitors and complements clinically approved biomarkers. Importantly, our model identifies a subset of mismatch repair-deficient tumors that are non-responsive to immunotherapy and may inform the selection of patients for combination treatments.

Ring artifacts in cone beam computed tomography (CBCT) images are caused by pixel gain variations using flat-panel detectors, and may lead to structured non-uniformities and deterioration of image quality. The purpose of this study is to propose a method of general ring artifact removal in CBCT images. This method is based on the polar coordinate system, where the ring artifacts manifest as stripe artifacts. Using relative total variation, the CBCT images are first smoothed to generate template images with fewer image details and ring artifacts. By subtracting the template images from the CBCT images, residual images with image details and ring artifacts are generated. As the ring artifact manifests as a stripe artifact in a polar coordinate system, the artifact image can be extracted by mean value from the residual image; the image details are generated by subtracting the artifact image from the residual image. Finally, the image details are compensated to the template image to generate the corrected images. The proposed framework is iterated until the differences in the extracted ring artifacts are minimized. We use a 3D Shepp-Logan phantom, Catphan©504 phantom, uniform acrylic cylinder, and images from a head patient to evaluate the proposed method. In the experiments using simulated data, the spatial uniformity is increased by 1.68 times and the structural similarity index is increased from 87.12% to 95.50% using the proposed method. In the experiment using clinical data, our method shows high efficiency in ring artifact removal while preserving the image structure and detail. The iterative approach we propose for ring artifact removal in cone-beam CT is practical and attractive for CBCT guided radiation therapy.

Natural image quality assessment (NIQA) wins increasing attention, while NIQA models are rarely used in the medical community. A couple of studies employ the NIQA methodologies for medical image quality assessment (MIQA), but building the benchmark data sets necessitates considerable time and professional skills. Particularly, the characteristics of synthesized distortions are different from those of clinical distortions, which makes the results not so convincing. In clinic, signal-to-noise ratio (SNR) is widely used, which is defined as the quotient of the mean signal intensity measured in a tissue region of interest (ROI) and the standard deviation of the signal intensity in an air region outside the imaged object, and both regions are outlined by specialists. We take advantage of the knowledge that SNR is routinely used and concern whether SNR measure can perform as a baseline metric for the development of MIQA algorithms. To address the issue, the inter-observer reliability of SNR measure is investigated regarding to different tissue ROIs (WM, white matter; CSF, cerebral spinal fluid) in magnetic resonance (MR) images. A total of 192 T*2, 88 T1, 76 T2 and 55 contrast-enhanced T1 (T1C) weighted images are analyzed. Statistical analysis indicates that SNR values shows consistency between different observers to the same ROI in each modality (Wilcoxon rank sum test, pw ≥ 0:11; and paired sample t-test, pp ≥ 0:28). Morever, whether off-the-shelf NIQA models can predict MR image quality is considered by using SNR values as the reference scores. Four NIQA models (BIQI, BLIINDS-II, BRISQUE and NIQE) are evaluated, and the correlation between SNR values and NIQA results is evaluated. Pearson correlation coefficient (rp) shows that WM-based SNR values correlates well with BIQI, BLIINDS-II and BRISQUE in T*2 images (rp ≥ 0:77), BRISQUE and NIQE in T1 images (rp ≥ 0:75), BLIINDS-II in T2 images (rp ≥ 0:67), and BRISQUE and NIQE in T1C images (rp ≥ 0:58), while CSF-based SNR values correlates well with BLIINDS-II in T*2 images (rp ≥ 0:64) and T2 images (rp ≥ 0:60), and all pp < 10−4. The prediction performance analysis further proves the result from the correlation analysis. Conclusively, SNR measure is reliable to different observations and can perform as a baseline indicator for the development of MIQA algorithms. In general, BRISQUE and BLIINDS-II are full of potential to be conditionally used as objective MIQA models toward human brain MR images. This study presents the first attempt of using SNR measure to bridge the gap between NIQA and MIQA, and large-scale experiments should be further conducted to confirm the conclusion in this work.