736 research outputs found

    Oncologic Imaging

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    Imaging is an integral part of the multidisciplinary management of cancer. Radiographic techniques are indispensable for proper staging of cancers and evaluation of the response of tumors to treatment. A wide variety of imaging modalities is available to clinicians. This chapter in Cancer Concepts: A Guidebook for the Non-Oncologist will introduce the role of radiology in the diagnosis and treatment of cancer.https://escholarship.umassmed.edu/cancer_concepts/1017/thumbnail.jp

    Global Challenges for Cancer Imaging

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    Image findings of cranial nerve pathology on [18F]-2- deoxy-D-glucose (FDG) positron emission tomography with computerized tomography (PET/CT): a pictorial essay.

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    This article aims to increase awareness about the utility of (18)F -FDG-PET/CT in the evaluation of cranial nerve (CN) pathology. We discuss the clinical implication of detecting perineural tumor spread, emphasize the primary and secondary (18)F -FDG-PET/CT findings of CN pathology, and illustrate the individual (18)F -FDG-PET/CT CN anatomy and pathology of 11 of the 12 CNs

    Influence of the minimum b-value on prostate cancer assessment using conventional DWI and DKI models

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    To investigate the influence of the minimum b-value in diffusion parameters estimated for prostate tissues using mono-exponential and kurtosis models.info:eu-repo/semantics/publishedVersio

    Focal Spot, Spring/Summer 1985

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    https://digitalcommons.wustl.edu/focal_spot_archives/1040/thumbnail.jp

    Cellular automata segmentation of brain tumors on post contrast MR images

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    In this paper, we re-examine the cellular automata(CA) al- gorithm to show that the result of its state evolution converges to that of the shortest path algorithm. We proposed a complete tumor segmenta- tion method on post contrast T1 MR images, which standardizes the VOI and seed selection, uses CA transition rules adapted to the problem and evolves a level set surface on CA states to impose spatial smoothness. Val- idation studies on 13 clinical and 5 synthetic brain tumors demonstrated the proposed algorithm outperforms graph cut and grow cut algorithms in all cases with a lower sensitivity to initialization and tumor type

    Current state of pediatric neuro-oncology imaging, challenges and future directions

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    Imaging plays a central role in neuro-oncology including primary diagnosis, treatment planning, and surveillance of tumors. The emergence of quantitative imaging and radiomics provided an uprecedented opportunity to compile mineable databases that can be utilized in a variety of applications. In this review, we aim to summarize the current state of conventional and advanced imaging techniques, standardization efforts, fast protocols, contrast and sedation in pediatric neuro-oncologic imaging, radiomics-radiogenomics, multi-omics and molecular imaging approaches. We will also address the existing challenges and discuss future directions
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