Question

1. Doradla, P., Joseph, C., & Giles, R. H. (2017). Terahertz endoscopic imaging for colorectal cancer detection: Current status and future perspectives. World journal of gastrointestinal endoscopy, 9(8), 346.‏

2. Lambin, P., Leijenaar, R. T., Deist, T. M., Peerlings, J., De Jong, E. E., Van Timmeren, J., ... & van Wijk, Y. (2017). Radiomics: the bridge between medical imaging and personalized medicine. Nature reviews Clinical oncology, 14(12), 749-762.‏

3. Stouthandel, M. E., Veldeman, L., & Van Hoof, T. (2019). Call for a multidisciplinary effort to map the lymphatic system with advanced medical imaging techniques: a review of the literature and suggestions for future anatomical research. The Anatomical Record, 302(10), 1681-1695.‏

4. Bödenler, M., de Rochefort, L., Ross, P. J., Chanet, N., Guillot, G., Davies, G. R., ... & Broche, L. M. (2019). Comparison of fast field-cycling magnetic resonance imaging methods and future perspectives. Molecular physics, 117(7-8), 832-848.‏

5. Zhou, L. Q., Wang, J. Y., Yu, S. Y., Wu, G. G., Wei, Q., Deng, Y. B., ... & Dietrich, C. F. (2019). Artificial intelligence in medical imaging of the liver. World journal of gastroenterology, 25(6), 672.‏

6. DeSouza, N. M., Winfield, J. M., Waterton, J. C., Weller, A., Papoutsaki, M. V., Doran, S. J., ... & Jackson, A. (2018). Implementing diffusion-weighted MRI for body imaging in prospective multicentre trials: current considerations and future perspectives. European radiology, 28(3), 1118-1131.‏
7. Pesapane, F., Codari, M., & Sardanelli, F. (2018). Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine. European radiology experimental, 2(1), 35.‏
8. Lee, J. G., Jun, S., Cho, Y. W., Lee, H., Kim, G. B., Seo, J. B., & Kim, N. (2017). Deep learning in medical imaging: general overview. Korean journal of radiology, 18(4), 570-584.‏
9. Choi, H. (2018). Deep learning in nuclear medicine and molecular imaging: current perspectives and future directions. Nuclear medicine and molecular imaging, 52(2), 109-118.‏
10. Wang, G. (2016). A perspective on deep imaging. IEEE access, 4, 8914-8924.‏

1. Please can you write a short paragraph 6-8 lines (summary) for each reference

Answer 1

1.Doradla, P., Joseph, C., & Giles, R. H. (2017). Terahertz endoscopic imaging for colorectal cancer detection: Current status and future perspectives. World journal of gastrointestinal endoscopy, 9(8), 346.

Colorectal cancer is the most reason for the no of deaths in the world. It is the third most diagosed cancer. Early detection and treatment can lead to reduce the no of deaths. The given article has described the steps for clinical applications of Thz imaging of CRC. A lot of barriers have been overcomed and the next step is development and testing of an in vivo THz endoscopy system capable of providing sensitivity and specificity numbers for the technique in identifying multiple stages of colon cancer.

2. Lambin, P., Leijenaar, R. T., Deist, T. M., Peerlings, J., De Jong, E. E., Van Timmeren, J., ... & van Wijk, Y. (2017). Radiomics: the bridge between medical imaging and personalized medicine. Nature reviews Clinical oncology, 14(12), 749-762.

Radiomics, enable the data to be extracted and applied to improve diagonostic and predictive accuracy in gaining importance to cancer research within clinical decision support system. It extracts valuable information from patient's images and correlate those with diagonostic outcomes.

3.Stouthandel, M. E., Veldeman, L., & Van Hoof, T. (2019). Call for a multidisciplinary effort to map the lymphatic system with advanced medical imaging techniques: a review of the literature and suggestions for future anatomical research. The Anatomical Record, 302(10), 1681-1695.

Some of the important functions of lymphatic system are:

The very first is forming a connection from the interstitial space to the venous system. Second is,in order to mount targeted immune responses, transport of antigens and antigen‐presenting cells to the lymph nodes. The most known disease that is related to the lymphatic system is cancer. The spread of cancer from the affected lymph node cannot be halted indefinitely. Because of this mechanism of cancer spread, the lymph node biopsy is a crucial method to assess the stage/severity and treatment approach of newly diagnosed tumors.

4. Bödenler, M., de Rochefort, L., Ross, P. J., Chanet, N., Guillot, G., Davies, G. R., ... & Broche, L. M. (2019). Comparison of fast field-cycling magnetic resonance imaging methods and future perspectives. Molecular physics, 117(7-8), 832-848.

Fast field-cycling (FFC) nuclear magnetic resonance relaxometry is a method to determine the relaxation rates. The combination of FFC techniques with magnetic resonance imaging (MRI) providess a high potential for new types of image contrast. This article gives an overview of the hardware systems currently in operation and the growth of it in the last deacde. In this article,limitations and error correction strategies specific to FFC-MRI are discuused.

5. Zhou, L. Q., Wang, J. Y., Yu, S. Y., Wu, G. G., Wei, Q., Deng, Y. B., ... & Dietrich, C. F. (2019). Artificial intelligence in medical imaging of the liver. World journal of gastroenterology, 25(6), 672.‏

AI is becoming very important in aid in liver image tasks,therfore leading to improved performance in detecting and evaluating liver lesions, facilitating liver clinical therapy, and predicting liver treatment response. In this article it is discussed that we need to determine which specific radiology tasks are most likely to benefit from the deep learning algorithm.

6. DeSouza, N. M., Winfield, J. M., Waterton, J. C., Weller, A., Papoutsaki, M. V., Doran, S. J., ... & Jackson, A. (2018). Implementing diffusion-weighted MRI for body imaging in prospective multicentre trials: current considerations and future perspectives. European radiology, 28(3), 1118-1131.‏

Diffusion-weighted MRI may be used for tumour detection, staging, prognostic information, assessing response and follow-up for body imaging. Prerequisites are data protection and good clinical practices. This article discuss the points which contributes to ADC variability in terms of data protection and analysis of it.

7.Pesapane, F., Codari, M., & Sardanelli, F. (2018). Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine. European radiology experimental, 2(1), 35.‏

Artificial intelligence, deep learning is becoming very important in medical imaging. This article discusses the basic definition of machine or deep learning with respect to AI into radiology. Radiology os now moving from a subjective topic to a more objective science where the digital era can guide more into the medical areas.