Existing deformable registration methods require exhaustively iterative optimization, along with careful parameter tuning, to estimate the deformation field between images. Although some learning-based methods have been proposed for initiating deformation estimation, they are often template-specific and not flexible in practical use. In this paper, we propose a convolutional neural network (CNN) based regression model to directly learn the complex mapping from the input image pair (i.e., a pair of template and subject) to their corresponding deformation field. Specifically, our CNN architecture is designed in a patch-based manner to learn the complex mapping from the input patch pairs to their respective deformation field. First, the equalized active-points guided sampling strategy is introduced to facilitate accurate CNN model learning upon a limited image dataset. Then, the similarity-steered CNN architecture is designed, where we propose to add the auxiliary contextual cue, i.e., the similarity between input patches, to more directly guide the learning process. Experiments on different brain image datasets demonstrate promising registration performance based on our CNN model. Furthermore, it is found that the trained CNN model from one dataset can be successfully transferred to another dataset, although brain appearances across datasets are quite variable.

Our experiments show that the proposed method can tackle various registration tasks on different databases, giving consistent good performance without the need of manual parameter tuning, which could be applicable to various clinical applications.

PurposeTo propose a synthesis method of pseudo-CT (CTCycleGAN) images based on an improved 3D cycle generative adversarial network (CycleGAN) to solve the limitations of cone-beam CT (CBCT), which cannot be directly applied to the correction of radiotherapy plans.MethodsThe improved U-Net with residual connection and attention gates was used as the generator, and the discriminator was a full convolutional neural network (FCN). The imaging quality of pseudo-CT images is improved by adding a 3D gradient loss function. Fivefold cross-validation was performed to validate our model. Each pseudo CT generated is compared against the real CT image (ground truth CT, CTgt) of the same patient based on mean absolute error (MAE) and structural similarity index (SSIM). The dice similarity coefficient (DSC) coefficient was used to evaluate the segmentation results of pseudo CT and real CT. 3D CycleGAN performance was compared to 2D CycleGAN based on normalized mutual information (NMI) and peak signal-to-noise ratio (PSNR) metrics between the pseudo-CT and CTgt images. The dosimetric accuracy of pseudo-CT images was evaluated by gamma analysis.ResultsThe MAE metric values between the CTCycleGAN and the real CT in fivefold cross-validation are 52.03 ± 4.26HU, 50.69 ± 5.25HU, 52.48 ± 4.42HU, 51.27 ± 4.56HU, and 51.65 ± 3.97HU, respectively, and the SSIM values are 0.87 ± 0.02, 0.86 ± 0.03, 0.85 ± 0.02, 0.85 ± 0.03, and 0.87 ± 0.03 respectively. The DSC values of the segmentation of bladder, cervix, rectum, and bone between CTCycleGAN and real CT images are 91.58 ± 0.45, 88.14 ± 1.26, 87.23 ± 2.01, and 92.59 ± 0.33, respectively. Compared with 2D CycleGAN, the 3D CycleGAN based pseudo-CT image is closer to the real image, with NMI values of 0.90 ± 0.01 and PSNR values of 30.70 ± 0.78. The gamma pass rate of the dose distribution between CTCycleGAN and CTgt is 97.0% (2%/2 mm).ConclusionThe pseudo-CT images obtained based on the improved 3D CycleGAN have more accurate electronic density and anatomical structure.

In prostate cancer radiotherapy, computed tomography (CT) is widely used for dose planning purposes. However, because CT has low soft tissue contrast, it makes manual contouring difficult for major pelvic organs. In contrast, magnetic resonance imaging (MRI) provides high soft tissue contrast, which makes it ideal for accurate manual contouring. Therefore, the contouring accuracy on CT can be significantly improved if the contours in MRI can be mapped to CT domain by registering MRI with CT of the same subject, which would eventually lead to high treatment efficacy. In this paper, we propose a bi-directional image synthesis based approach for MRI-to-CT pelvic image registration. First, we use patch-wise random forest with auto-context model to learn the appearance mapping from CT to MRI domain, and then vice versa. Consequently, we can synthesize a pseudo-MRI whose anatomical structures are exactly same with CT but with MRI-like appearance, and a pseudo-CT as well. Then, our MRI-to-CT registration can be steered in a dual manner, by simultaneously estimating two deformation pathways: 1) one from the pseudo-CT to the actual CT and 2) another from actual MRI to the pseudo-MRI. Next, a dual-core deformation fusion framework is developed to iteratively and effectively combine these two registration pathways by using complementary information from both modalities. Experiments on a dataset with real pelvic CT and MRI have shown improved registration performance of the proposed method by comparing it to the conventional registration methods, thus indicating its high potential of translation to the routine radiation therapy.

Certain gases in the breath are known to be indicators of the presence of diseases and clinical conditions. These gases have been identified as biomarkers using equipments such as gas chromatography (GC) and electronic nose (e-nose). GC is very accurate but is expensive, time consuming, and non-portable. E-nose has the advantages of low-cost and easy operation, but is not particular for analyzing breath odor and hence has a limited application in diseases diagnosis. This article proposes a novel system that is special for breath analysis. We selected chemical sensors that are sensitive to the biomarkers and compositions in human breath, developed the system, and introduced the odor signal preprocessing and classification method. To evaluate the system performance, we captured breath samples from healthy persons and patients known to be afflicted with diabetes, renal disease, and airway inflammation repectively and conducted experiments on medical treatment evaluation and disease identification. The results show that the system is not only able to distinguish between breath samples from subjects suffering from various diseases or conditions (diabetes, renal disease, and airway inflammation) and breath samples from healthy subjects, but in the case of renal failure is also helpful in evaluating the efficacy of hemodialysis (treatment for renal failure).