ന㧋ኼ〛 • TOPIC INSIGHT 澳大ᑓゆ • 2019 UMAGAZINE 20 25 In addition to diagnosis, radioactive drugs can also be used for treatment of cancers, which is exactly the focus of the lab’s third project. The lab has developed a piece of 3D computer software for internal dose calculation in targeted radionuclide therapy. It can be used to evaluate the radiation dose absorbed by the tumour and various organs, an important index for treatment efficacy and potential toxicity. Hospitals and research institutions can apply this software to optimise dose calculation for each individual patient, so that treatment planning could be more precise and personalised. Prof Mok says that AI technology is increasingly used in medical imaging research. She says, ‘AI technology not only can help to improve the image quality, it can also reduce the radiation dose. I hope to apply AI technology in all three research projects. With our first project on respiratory artifact reduction, we have already successfully reduced image noise using AI technology. In the future, we will study the use of AI technology to reduce the radiation dose and image acquisition time to benefit the patients.’ Enormous Potential Under the leadership of Prof Mok, the lab has trained over a dozen postgraduate students from Macao and mainland China, who are now working at universities, industries, and medical institutions in Macao and other parts of the world. Among them, doctoral students Zhang Duo and Sun Jingzhang foresee enormous potential in applying AI technology to medical imaging research. Over the past few years, under the guidance of Prof Mok, Zhang has published some papers on medical image correction, including one paper that examines the effects of different respiratory patterns on medical images. During his doctoral study, Zhang was recommended by Prof Mok to work as a visiting scholar for one year at the University of Massachusetts Medical School and Yale University in the US, where he participated in cutting-edge research. ‘I gained a lot from working at Yale. According to my supervisor at Yale, I did in one month what other students did in one semester,’ Zhang says. Because of his outstanding performance and the solid grounding in research he developed at UM, Yale’s supervisor invited him to be a postdoctoral research fellow at the university, but he had other plans. ‘Our lab is in close contact with research institutions in the 㡗̜ 〹ᑘ䢬ഥလⳚḳ̏ 能ᑜℼ䢬 㬟ೡⅰⓥ˓個㦕↷ൄ 能᳤ ۩ឱ㤥ᘼ ♖ℼⅰ⃚̿ 〘┰Ⳛḳؓ 㕵䢬۠͞ くΎ Ⳛḳഭ⥁℧ⅰℼᐅ ܋ݶ 個࣮ ອϚⅰ ഥؓ 㕵ǎǗᆒм研發֦ ˍ個˓⛁ᘼ㕕 ܒ Φ♖ℼؓ 㕵〘┰㍇㭓䢬 ۽ ϫ㕕㡕ᆖ研 ኻ䢬Ԗ٨ؓ 㕵〘┰䢬͝ ᣵ個⃚̿ 㓃ᕔᕅᬞ⌒Ǎᕅ個̿ ٨ⅰ᧘ഥؓ 㕵〘 ┰ ݶ ㎔ഥؓ 㕵くΎǎǘ ⬃ᐑእゐ䢬㐼̿ 智能ᒴ㕕ಾྕ Ӎ研 ⅰˍ個㉏ ن ǎǗ̿ 智能 ͞۽ ዞ㭗ྕ Ӎㇰ♖䢬̏ 能᪾റ㎔ഥؓ 㕵䢬ᆒຒᕤ˓ 個研㦕↷㓃能ᅞ˔̿ 智能ᇤ⻐ǎ ॺۙ 㡗 ܻݩ Ѧྕ ⅰ㦕↷˔䢬ᆒмຂ⚭成 功ᅞ̿ 智能᪾Χྕ Ӎⅰࣰ ⢍ǎᆒм ധϚ̏ ᕐ研̿ 智能䢬᪾റኡዝᆳ 㣍ⅰ ܒ Φ♖ؓ 㕵ᆖ成ྕ Ӎⅰᒿ㟠ǎǘ Ⅼൢ ᯄ؝ ॺ⬃ᐑእອ㦦˕䢬㬟ೡ㨱̜ ڇ ૦Φ Ϛ⦨澳㟑 ݶ Յঁ ⅰ研䢬↷ נ ॺ˞₅ ܋ঁ۠ ᕲ澳ⅰ㡕ᘪǍᜪ₅ ݶ 㕕ℼ發 發ᶣ䢬Ր中ॺㅂ ژ 㞀 ݶ ಲᐟ ⇎⿇㕕ಾྕ Ӎ研⦾̿ 智能♺ⅰ נ 發ൠ נ ᓡǎ 㐿ۙ ᐨ䢬㞀ॺ⬃ᐑእമ˕䢬發⻠̜ ˗റ㠙ᑜ㕕ಾྕ Ӎᘪᢩ ⅰグᐵ䢬ٛ ሱ ᩔ۠ ˗ ឹܻݩܒ ཕഭ㕕ಾྕ Ӎⅰྕ 㦏ǎ ᏽㅂ ژ ᕧ㟠䢬㞀Ỽ⬃ᐑእⲧ䢬 ⟜२㷂⇆大ಾ㕕ಾ㡕 ݶ ⡪㯆大ಾᎨͭ 〯 ࠏ ಾ⡃ˍ䢬ۜ ⦾ נ ᦳ研ǎǗॺ⡪ 㯆εⅰᏹྩ 大䢬ᘽᎭ㒄㑳മຨⅰく ӯ䢬ᆒˍ個ᕓѵ̜ׄ̿ ˍ個ಾᕧᆾѵ ⅰεǎǘⁱᑜ⻠έ֦ ⨑ ݶ ॺ澳大㨱֦ ⅰ研ᘽ䢬⡪㯆大ಾⅰമຨ㑪エ 㞀つᘪѵ ژ ྭ 研䢬Π͒ べ᳤ ₔᜪྭ ₍ॺ◼澳大Ტ چ 發ൠ̏ ᒴˍ個ύႰ ⅰ㑢ǎǗᆒм㬟ೡ⦾大中⬷ঁ ݶچ ᢚ⟜ⅰ⏲研⢊❠ְ 䢬㉼㩿中ᐵ 大ಾ ݶ 中२⏲ಾ㡕᪦ঃԯ㐫ᇤ⻐研㡕 ⓺大Ტ چ 㡕ᘪ̏ ᕔε䢬ₔᜪྭ 大Ტ چ Ր͒ 大ಾͭ ⢑發ൠᯂ؛大䢬㑮ᑚϾ͞ྭ ㉼⬃ᐑእॺ研˔Йቆ⛛⢊❠ǎǘ
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