X-RAY CT HIGH DENSITY ARTIFACT SUPPRESSION IN CRYOSURGERY

Abstract: X-ray CT provides imaging guidance for cryosurgery that allows 3D visualization of frozen and unfrozen tissue. Temperature in the tissue water-ice interface (0 to -10

GPU-based Iterative Cone Beam CT Reconstruction Using Tight Frame Regularization

X-ray imaging dose from serial cone-beam CT (CBCT) scans raises a clinical concern in most image guided radiation therapy procedures. It is the goal of this paper to develop a fast GPU-based algorithm to reconstruct high quality CBCT images from undersampled and noisy projection data so as to lower the imaging dose. For this purpose, we have developed an iterative tight frame (TF) based CBCT reconstruction algorithm. A condition that a real CBCT image has a sparse representation under a TF basis is imposed in the iteration process as regularization to the solution. To speed up the computation, a multi-grid method is employed. Our GPU implementation has achieved high computational efficiency and a CBCT image of resolution 512\times512\times70 can be reconstructed in ~5 min. We have tested our algorithm on a digital NCAT phantom and a physical Catphan phantom. It is found that our TF-based algorithm is able to reconstrct CBCT in the context of undersampling and low mAs levels. We have also quantitatively analyzed the reconstructed CBCT image quality in terms of modulation-transfer-function and contrast-to-noise ratio under various scanning conditions. The results confirm the high CBCT image quality obtained from our TF algorithm. Moreover, our algorithm has also been validated in a real clinical context using a head-and-neck patient case. Comparisons of the developed TF algorithm and the current state-of-the-art TV algorithm have also been made in various cases studied in terms of reconstructed image quality and computation efficiency.Comment: 24 pages, 8 figures, accepted by Phys. Med. Bio

Triggered optical coherence tomography for capturing rapid periodic motion

Quantitative cross-sectional imaging of vocal folds during phonation is potentially useful for diagnosis and treatments of laryngeal disorders. Optical coherence tomography (OCT) is a powerful technique, but its relatively low frame rates makes it challenging to visualize rapidly vibrating tissues. Here, we demonstrate a novel method based on triggered laser scanning to capture 4-dimensional (4D) images of samples in motu at audio frequencies over 100 Hz. As proof-of-concept experiments, we applied this technique to imaging the oscillations of biopolymer gels on acoustic vibrators and aerodynamically driven vibrations of the vocal fold in an ex vivo calf larynx model. Our results suggest that triggered 4D OCT may be useful in understanding and assessing the function of vocal folds and developing novel treatments in research and clinical settings

Overexpression of LCMR1 is significantly associated with clinical stage in human NSCLC

<p>Abstract</p> <p>Background</p> <p>Lung cancer is one of the most common human cancers and the leading cause of cancer death worldwide. The identification of lung cancer associated genes is essential for lung cancer diagnosis and treatment.</p> <p>Methods</p> <p>Differential Display-PCR technique was used to achieve the novel cDNA, which were then verified by real-time PCR. Northern blot was utilized to observe the expression of LCMR1 in different human tissues. 84 cases human NSCLC tissues and normal counterparts were analyzed for the expression of LCMR1 by immunohistochemistry.</p> <p>Results</p> <p>A novel 778-bp cDNA fragment from human large cell lung carcinoma cell lines 95C and 95D was obtained, and named <it>LCMR1 </it>(Lung Cancer Metastasis Related protein 1). LCMR1 was differentially expressed in different human tissues. LCMR1 was strongly overexpressed in NSCLC and its expression was significantly associated with clinical stage.</p> <p>Conclusion</p> <p>Our data indicated that <it>LCMR1</it>, strongly overexpressed in NSCLC, might have applications in the clinical diagnosis and treatment of lung cancer.</p

Orthogonal methods based ant colony search for solving continuous optimization problems

Research into ant colony algorithms for solving continuous optimization problems forms one of the most significant and promising areas in swarm computation. Although traditional ant algorithms are designed for combinatorial optimization, they have shown great potential in solving a wide range of optimization problems, including continuous optimization. Aimed at solving continuous problems effectively, this paper develops a novel ant algorithm termed "continuous orthogonal ant colony" (COAC), whose pheromone deposit mechanisms would enable ants to search for solutions collaboratively and effectively. By using the orthogonal design method, ants in the feasible domain can explore their chosen regions rapidly and e±ciently. By implementing an "adaptive regional radius" method, the proposed algorithm can reduce the probability of being trapped in local optima and therefore enhance the global search capability and accuracy. An elitist strategy is also employed to reserve the most valuable points. The performance of the COAC is compared with two other ant algorithms for continuous optimization of API and CACO by testing seventeen functions in the continuous domain. The results demonstrate that the proposed COAC algorithm outperforms the others

Accurate Diagnosis of Colorectal Cancer Based On Histopathology Images Using Artificial Intelligence

Background: Accurate and robust pathological image analysis for colorectal cancer (CRC) diagnosis is time-consuming and knowledge-intensive, but is essential for CRC patients’ treatment. The current heavy workload of pathologists in clinics/hospitals may easily lead to unconscious misdiagnosis of CRC based on daily image analyses. Methods: Based on a state-of-the-art transfer-learned deep convolutional neural network in artificial intelligence (AI), we proposed a novel patch aggregation strategy for clinic CRC diagnosis using weakly labeled pathological whole-slide image (WSI) patches. This approach was trained and validated using an unprecedented and enormously large number of 170,099 patches, \u3e 14,680 WSIs, from \u3e 9631 subjects that covered diverse and representative clinical cases from multi-independent-sources across China, the USA, and Germany. Results: Our innovative AI tool consistently and nearly perfectly agreed with (average Kappa statistic 0.896) and even often better than most of the experienced expert pathologists when tested in diagnosing CRC WSIs from multicenters. The average area under the receiver operating characteristics curve (AUC) of AI was greater than that of the pathologists (0.988 vs 0.970) and achieved the best performance among the application of other AI methods to CRC diagnosis. Our AI-generated heatmap highlights the image regions of cancer tissue/cells. Conclusions: This first-ever generalizable AI system can handle large amounts of WSIs consistently and robustly without potential bias due to fatigue commonly experienced by clinical pathologists. It will drastically alleviate the heavy clinical burden of daily pathology diagnosis and improve the treatment for CRC patients. This tool is generalizable to other cancer diagnosis based on image recognition

Implementation and evaluation of various demons deformable image registration algorithms on GPU

Online adaptive radiation therapy (ART) promises the ability to deliver an optimal treatment in response to daily patient anatomic variation. A major technical barrier for the clinical implementation of online ART is the requirement of rapid image segmentation. Deformable image registration (DIR) has been used as an automated segmentation method to transfer tumor/organ contours from the planning image to daily images. However, the current computational time of DIR is insufficient for online ART. In this work, this issue is addressed by using computer graphics processing units (GPUs). A grey-scale based DIR algorithm called demons and five of its variants were implemented on GPUs using the Compute Unified Device Architecture (CUDA) programming environment. The spatial accuracy of these algorithms was evaluated over five sets of pulmonary 4DCT images with an average size of 256x256x100 and more than 1,100 expert-determined landmark point pairs each. For all the testing scenarios presented in this paper, the GPU-based DIR computation required around 7 to 11 seconds to yield an average 3D error ranging from 1.5 to 1.8 mm. It is interesting to find out that the original passive force demons algorithms outperform subsequently proposed variants based on the combination of accuracy, efficiency, and ease of implementation.Comment: Submitted to Physics in Medicine and Biolog