Synthetic computed tomography generation for abdominal adaptive radiotherapy using low-field magnetic resonance imaging.

Background and Purpose: Magnetic Resonance guided Radiotherapy (MRgRT) still needs the acquisition of Computed Tomography (CT) images and co-registration between CT and Magnetic Resonance Imaging (MRI). The generation of synthetic CT (sCT) images from the MR data can overcome this limitation. In this study we aim to propose a Deep Learning (DL) based approach for sCT image generation for abdominal Radiotherapy using low field MR images. Materials and methods: CT and MR images were collected from 76 patients treated on abdominal sites. U-Net and conditional Generative Adversarial Network (cGAN) architectures were used to generate sCT images. Additionally, sCT images composed of only six bulk densities were generated with the aim of having a Simplified sCT.Radiotherapy plans calculated using the generated images were compared to the original plan in terms of gamma pass rate and Dose Volume Histogram (DVH) parameters. Results: sCT images were generated in 2 s and 2.5 s with U-Net and cGAN architectures respectively.Gamma pass rates for 2%/2mm and 3%/3mm criteria were 91% and 95% respectively. Dose differences within 1% for DVH parameters on the target volume and organs at risk were obtained. Conclusion: U-Net and cGAN architectures are able to generate abdominal sCT images fast and accurately from low field MRI.

Bladder Wall Segmentation and Characterization on MR Images: Computer-Aided Spina Bifida Diagnosis.

(1) Background: Segmentation of the bladder inner's wall and outer boundaries on Magnetic Resonance Images (MRI) is a crucial step for the diagnosis and the characterization of the bladder state and function. This paper proposes an optimized system for the segmentation and the classification of the bladder wall. (2) Methods: For each image of our data set, the region of interest corresponding to the bladder wall was extracted using LevelSet contour-based segmentation. Several features were computed from the extracted wall on T2 MRI images. After an automatic selection of the sub-vector containing most discriminant features, two supervised learning algorithms were tested using a bio-inspired optimization algorithm. (3) Results: The proposed system based on the improved LevelSet algorithm proved its efficiency in bladder wall segmentation. Experiments also showed that Support Vector Machine (SVM) classifier, optimized by Gray Wolf Optimizer (GWO) and using Radial Basis Function (RBF) kernel outperforms the Random Forest classification algorithm with a set of selected features. (4) Conclusions: A computer-aided optimized system based on segmentation and characterization, of bladder wall on MRI images for classification purposes is proposed. It can significantly be helpful for radiologists as a part of spina bifida study.

Technical note: Design and initial evaluation of a novel physical breast phantom to monitor image quality in digital breast tomosynthesis.

PURPOSE: To describe the creation process of a new breast phantom specifically designed to monitor quality control (QC) metrics consistency over several months in digital breast tomosynthesis (DBT). METHODS: The semi-anthropomorphic Tomomam® phantom was designed and evaluated twice monthly on a single Hologic Selenia Dimensions® unit over 5 months. The phantom is manufactured in a one-piece epoxy resin homogeneous material as the basis for manufacturing, simulating breast tissue as 50% equivalent glandular (GL)/50% equivalent adipose (AD) and compressed thickness of 60 mm. The distribution of test objects on different planes inside the phantom should allow the quantification of 10 image quality metrics: reproducibility, signal difference-to-noise ratio (SDNR), geometric distortions in the plane, missing or added tissue at chest wall, at the top and bottom of images stack and lateral sides, in-plane homogeneity, image scoring, artifact spread function (ASF), geometric distortions in the volume. SDNR was quantified according to GL and AD tissues. Tolerance criteria per parameter were described to analyze results over the study time. RESULTS: Mean scores were equal to 15.4, 15.0, and 11.6 for masses, microcalcifications, and fibers, respectively. A large difference between GL and AD tissues for SDNR metrics was noted over the study time: the best results were obtained from GL tissues. Both geometric distortions and local homogeneity in the plane conformed to expected values. The mean volume value of the triangular prism was 11.3% greater than the expected value due to a reconstruction height equal to 66 mm instead of 60 mm. CONCLUSIONS: In this study, we monitored several QC metrics discriminating GL and AD tissues by using a new breast phantom developed by us. The preliminary clinical tests demonstrated that the Tomomam® phantom could be used to reliably and efficiently track 10 QC metrics with a single acquisition. More data need to be acquired to refine tolerance criteria for some metrics.

Multi-View Separable Pyramid Network for AD Prediction at MCI Stage by 18F-FDG Brain PET Imaging.

Alzheimer's Disease (AD), one of the main causes of death in elderly people, is characterized by Mild Cognitive Impairment (MCI) at prodromal stage. Nevertheless, only part of MCI subjects could progress to AD. The main objective of this paper is thus to identify those who will develop a dementia of AD type among MCI patients. 18F-FluoroDeoxyGlucose Positron Emission Tomography (18F-FDG PET) serves as a neuroimaging modality for early diagnosis as it can reflect neural activity via measuring glucose uptake at resting-state. In this paper, we design a deep network on 18F-FDG PET modality to address the problem of AD identification at early MCI stage. To this end, a Multi-view Separable Pyramid Network (MiSePyNet) is proposed, in which representations are learned from axial, coronal and sagittal views of PET scans so as to offer complementary information and then combined to make a decision jointly. Different from the widely and naturally used 3D convolution operations for 3D images, the proposed architecture is deployed with separable convolution from slice-wise to spatial-wise successively, which can retain the spatial information and reduce training parameters compared to 2D and 3D networks, respectively. Experiments on ADNI dataset show that the proposed method can yield better performance than both traditional and deep learning-based algorithms for predicting the progression of Mild Cognitive Impairment, with a classification accuracy of 83.05%.

Multiscale spatial gradient features for 18F-FDG PET image-guided diagnosis of Alzheimer's disease.

BACKGROUND AND OBJECTIVE: 18F-FluoroDeoxyGlucose Positron Emission Tomography (18F-FDG PET) is one of the imaging biomarkers to diagnose Alzheimer's Disease (AD). In 18F-FDG PET images, the changes of voxels' intensities reflect the differences of glucose rates, therefore voxel intensity is usually used as a feature to distinguish AD from Normal Control (NC), or at earlier stage to distinguish between progressive and stable Mild Cognitive Impairment (pMCI and sMCI). In this paper, 18F-FDG PET images are characterized in an alternative way-the spatial gradient, which is motivated by the observation that the changes of 18F-FDG rates also cause gradient changes. METHODS: We improve Histogram of Oriented Gradient (HOG) descriptor to quantify spatial gradients, thereby achieving the goal of diagnosing AD. First, the spatial gradient of 18F-FDG PET image is computed, and then each subject is segmented into different regions by using an anatomical atlas. Second, two types of improved HOG features are extracted from each region, namely Small Scale HOG and Large Scale HOG, then some relevant regions are selected based on a classifier fed with spatial gradient features. Last, an ensemble classification framework is designed to make a decision, which considers the performance of both individual and concatenated selected regions. RESULTS: the evaluation is done on ADNI dataset. The proposed method outperforms other state-of-the-art 18F-FDG PET-based algorithms for AD vs. NC with an accuracy, a sensitivity and a specificity values of 93.65%, 91.22% and 96.25%, respectively. For the case of pMCI vs. sMCI, the three metrics are 75.38%, 74.84% and 77.11%, which is significantly better than most existing methods. Besides, promising results are also achieved for multiple classifications under 18F-FDG PET modality. CONCLUSIONS: 18F-FDG PET images can be characterized by spatial gradient features for diagnosing AD and its early stage, and the proposed ensemble framework can enhance the classification performance.

Stimulated Raman histology: one to one comparison with standard hematoxylin and eosin staining.

We present for the first time one-to-one correspondence between standard hematoxylin/eosin (H&E) stained tissue sections and stimulated Raman histology (SRH) - a label-free technique in which stimulated Raman scattering (SRS) and second harmonic generation (SHG) are combined to generate virtual H&E images. Experiments were performed on both human thin cryogenic slides from the gastrointestinal tract (GI) and thick freshly excised biopsies from endoscopic surgery. Results on cryogenic slides evidenced an excellent agreement between SRH and H&E images while the ones on biopsies established the relevance of SRH for rapid intraoperative histology to assist in surgical decision making.

Fast stimulated Raman and second harmonic generation imaging for intraoperative gastro-intestinal cancer detection.

Conventional haematoxylin, eosin and saffron (HES) histopathology, currently the 'gold-standard' for pathological diagnosis of cancer, requires extensive sample preparations that are achieved within time scales that are not compatible with intra-operative situations where quick decisions must be taken. Providing to pathologists a close to real-time technology revealing tissue structures at the cellular level with HES histologic quality would provide an invaluable tool for surgery guidance with evident clinical benefit. Here, we specifically develop a stimulated Raman imaging based framework that demonstrates gastro-intestinal (GI) cancer detection of unprocessed human surgical specimens. The generated stimulated Raman histology (SRH) images combine chemical and collagen information to mimic conventional HES histopathology staining. We report excellent agreements between SRH and HES images acquire on the same patients for healthy, pre-cancerous and cancerous colon and pancreas tissue sections. We also develop a novel fast SRH imaging modality that captures at the pixel level all the information necessary to provide instantaneous SRH images. These developments pave the way for instantaneous label free GI histology in an intra-operative context.