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<section data-role="paragraph" data-color="rgb(182, 228, 253)" data-custom="rgb(182, 228, 253)"><section><section><section><section powered-by="gulangu"><section><section><section><section powered-by="gulangu"><section><section><section><section powered-by="gulangu"><section><section><section><section powered-by="gulangu"><section><section><section><section><p><p><img src="image/20201014/86b79d371e9862b01189f0252a03b2cd_1.png" /></p></p></section><section><section><span><strong></strong></span></section><p><span>医疗器械媒体报道先锋</span></p><p><span>分享专业医疗器械知识</span></p></section><section><section><section><span>关注</span></section></section></section></section></section></section></section></section></section></section></section></section></section></section></section></section></section></section></section></section></section></section></section><section powered-by="gulangu"></section><p><br /></p><section data-role="outer" label="Powered by gulangu"><section data-tools="gulangu" data-id="85410"><section><section data-width="65%"></section><section><p><br /></p><p><span>“See Possibilities Together”共同见证无限可能是2019年第105届北美放射学年会RSNA的主题。</span><span>来自全球放射领域专家聚集于美国芝加哥分享这一学术和前沿技术的盛宴。</span><span>正如Prof. Sanjiv S. Gambhir, MD PhD教授在Plenary Sessions的诊断放射学年度演讲的题目“Next Generation Technologies and Strategies for Precision Health”，当今诊断以精准医疗为追求目标。</span></p><p><br /></p><p><span>“Together, DLR-AiCE and Precision bring the power of deep learning and Ultra- High Resolution scanning at standard radiation doses”本届RSNA佳能医疗展出两大影像新利器：</span><span>业内首款深度学习图像重建系统DLR-AiCE和业内首款搭载4K采集影像链的超高分辨率CT Aquilion Precision。</span><span>图像重建算法和CT采集硬件的双重突破，共同带来了无限可能，助力用户同享精准高清“See possibilities together, Enjoy Precision together”，完美契合RSNA2019的主题。</span></p></section></section></section><section data-role="paragraph"><p><br /></p></section></section><section data-role="outer" label="Powered by gulangu"><section data-tools="gulangu" data-id="87844" data-color="#27939d"><section data-width="20em"><section><section><p data-brushtype="text"><strong>业内首款深度学习图像重建系统DLR-AiCE</strong></p></section></section><section></section></section></section><section data-role="paragraph" data-color="#27939d"><p><br /></p></section></section><p><span></span></p><p><span>可能有读者还记得11月14日佳能医疗在北京新品发布会上的彩蛋（点此查看），这个彩蛋就是业内首款深度学习图像重建系统DLR-AiCE。本届RSNA，和DLR-AiCE技术相关的研究成果有16篇，与DLR-AiCE相关的话题也受到了与会专家们的高度关注。同时，佳能医疗也展示出了更多DLR-AiCE系统的相关资料和临床病例。</span></p><p><span><br /></span></p><p><video width="100%" height="580" controls="controls"><source src="http://mpvideo.qpic.cn/0af2df2jz4zvmcapb4faqbqnaahfbuhljbvltbm2bqeaadqoayfa.f10002.mp4?dis_k=bca73c14e79bd52d876ead043dfd3ad4&dis_t=1602652461" type="video/mp4"></source></video></p><p><br /></p><p><span></span></p><p><span>曾有学者说：“医学影像学在一定程度上是一门“看图说话”的学科。“这虽是一句玩笑话，却也反映出图像质量对影像诊断的重要性。CT在人体内采集的数据，首先被转化为“0101”的原始数据形式，之后通过不同的重建算法被还原为灰度图像。</span></p><p><span><br /></span></p><p><span>CT刚诞生时采用的图像重建算法是迭代算法的前身ART法（线性代数法）。ART法运算量大，重建速度慢，很快被FBP法（卷积反投影法）取代。近几年随着计算机算力的提升，改进后的迭代重建算法又开始应用于临床。迭代算法的模型设计越复杂，重建出的图像越真实，但重建速度相应变慢。受计算机“摩尔定律”限制，全模型域迭代算法还不能进行实时重建。</span></p><p><br /></p><section data-role="outer" label="Powered by gulangu"><section data-tools="gulangu" data-id="87569"><section><p><img src="image/20201014/57660eb651b81772ef687bff6e268dfd_2.gif" /></p></section></section><section data-role="paragraph"><p><br /></p></section></section><p><span>人工智能技术的发展，提供了新的契机。通过神经网络和深度学习系统，AI技术可对同一原始数据不同重建算法下生成的不同特性的图像进行智能比对和分析。经大数据训练后，AI技术可以总结出高质量图像的特征规则。依据这些规则，AI技术可将低质量图像快速转化为高质量图像，这是深度学习图像重建技术的基本原理。</span></p><p><br /></p><p><p><img src="image/20201014/3403cd83284990e1989005eedb500416_3.png" /></p></p><p><span>图：深度学习图像重建系统的原理</span><span></span></p><p><br /></p><p><span>由原理可知，深度学习图像重建算法的能力高低直接取决于训练时所采用的参照图像的质量。参照图像的质量越高，则深度学习后的重建算法的质量越高。佳能的DLR - AiCE基于高质量的全模型域迭代算法进行训练，降低伪影和噪声的同时，更带来了CT设备在密度分辨率上的突破。目前市场上最好的CT设备的密度分辨率均在2mm水平，而搭载佳能医疗DLR – AiCE系统的几款CT，其密度分辨率达到了1.5mm。</span></p><p><br /></p><p><p><img src="image/20201014/dab47aeefc428ce494e275878ae24022_4.png" /></p></p><p><span>图：佳能DLR – AiCE系统可提升密度分辨率至1.5mm</span><span></span></p><p><br /></p><p><span>更低的图像噪声和伪影，更高的密度分辨率，更快的实时重建能力，佳能的深度学习图像重建系统DLR-AiCE和CT设备的结合，将改变CT应用的面貌，将CT的图像质量提升到新的高度。</span></p><p><br /></p><p><p><img src="image/20201014/0516b14ec9538e3880e5fb4348589ada_5.png" /></p></p><p><span>图：DLR – AiCE系统肺部图像对比</span></p><p><br /></p><p><p><img src="image/20201014/d6a9307bade79bb378b47e96cc9131e0_6.png" /></p></p><p><span>图：DLR – AiCE系统腹部图像对比</span></p><p><br /></p><p><span>作为世界首款深度学习图像重建系统，DLR-AiCE深度学习重建系统2018年3月获得日本药事批准，在全球范围内首家投入临床使用，2019年获得美国FDA批准，目前NMPA（原CFDA）正在申请中。</span></p><p><span><br /></span></p><p><p><img src="image/20201014/b8cd3b17cbbf24e6d151f3c6aa5582d5_7.png" /></p></p><p><span>图：DLR – AiCE系统NMPA（原CFDA）正在申请中</span><span></span></p><p><br /></p><p><span>本届RSNA上，DLR-AiCE深度学习重建系统得到了进一步提升。最新的DLR-AiCE已不限于CT应用，更拓展至MR和PETCT平台，拓展了该技术的临床应用范围。</span></p><p><br /></p><p><p><img src="image/20201014/393c0766992a384a21fd1d80d5412b58_8.png" /></p></p><p><span>图：DLR – AiCE，首款跨平台深度学习重建系统</span><span></span></p><p><br /></p><p><span>结合DLR-AiCE对图像质量的提升能力，佳能MR提出了“重新定义“场强”“的口号。1.5T MR设备结合DLR-AiCE算法，其图像质量可以媲美3T MR的效果，3T MR+DLR-AiCE更可媲美7T MR的质量。从现场的临床展示病例来看，DLR-AiCE的能力的确令人期待。</span></p><p><br /></p><p><p><img src="image/20201014/e4c94d6db0e7a79b50ef5f5dfa9c89c6_9.png" /></p></p><p><span>图：重新定义“场强”？</span><span></span></p><p><br /></p><section data-role="outer" label="Powered by gulangu"><section data-tools="gulangu" data-id="87844" data-color="#27939d"><section data-width="20em"><section><section><p data-brushtype="text"><strong>业内首款搭载4K级采集影像链的超高分辨率CT Aquilion Precision</strong></p></section></section><section></section></section></section></section><p><br /></p><p><p><img src="image/20201014/e5916d852aac7417ea110a288ffcf95b_10.jpg" /></p><span>图：全球首款搭载4K级影像采集链的超高分辨率CT：Aquilion Precision</span><span></span></p><p><span><br /></span></p><p><span>对更高分辨率的追求，是影像行业永恒的目标之一。</span><span>本次RSNA上，佳能医疗就带来了全球首款搭载4K级影像采集链的超高分辨率CT：</span><span>Aquilion Precision。</span><span>Aquilion Precision CT的探测器单元为0.25mm，是业内最薄的探测器，空间分辨率可以到50lp/cm，提供了8倍的数据处理速度，配备高性能GPU，可以对1024x1024矩阵实现80幅/秒的重建和处理速度，且图像存储是常规CT的6倍。</span></p><p><br /></p><p><span>佳能Precision CT结合业内第一家提出的深度学习重建DLR-AiCE算法真正实现了超高清成像，保证每个细节都精准“Precision in Every Detail”。</span></p><p><br /></p><section data-role="outer" label="Powered by gulangu"><section data-tools="gulangu" data-id="87569"><section><p><img src="image/20201014/57660eb651b81772ef687bff6e268dfd_2.gif" /></p></section></section><section data-role="paragraph"><p><br /></p></section></section><p><span>Precision CT已于2019年3月在美国UC DAVIS University of California 和John Hopkins University正式装机使用，并开展多项临床和科研工作。随后在荷兰Radboud University，法国CHU Nancy，德国Mainz装机，截止目前，全球将近30台装机。来自荷兰Radboud University Medical Center Nijmegen的Prof. M. Proko讲到Precision结合DLR-AiCE可以获得excellent image quality, natural noise pattern，而且重建速度非常快，真正实现了常规剂量下的超高清成像，保证精准诊断。</span></p><p><br /></p><section><section powered-by="gulangu"><section><section powered-by="gulangu"><section><section powered-by="gulangu"><section><p><img src="image/20201014/abcfa3adf1a81e577bdebb0ff6f5e5ae_12.png" /></p></section></section></section></section></section><section><section powered-by="gulangu"><section><section powered-by="gulangu"><section><p><img src="image/20201014/e7fe0212053d72de73bf597c3f1db03e_13.png" /></p></section></section></section></section></section></section></section><p><br /></p><p><span>佳能Precision CT的每一幅图像都是对过去传统图像和诊断的颠覆，让诊断医生see the possibilities and enjoy precision，以下是来自日本国立癌症中心的胰腺病变超高清诊断，以及舌下腺的超高清成像。</span></p><section><section powered-by="gulangu"><section><section><section powered-by="gulangu"><section><section><section powered-by="gulangu"><section><section><section powered-by="gulangu"><section><p><img src="image/20201014/2322b0917c11a54a5ca0669b3921a052_14.png" /></p></section></section><section powered-by="gulangu"><section><section powered-by="gulangu"><section><table width="100%"><tbody><tr opera-tn-ra-comp="_$.pages:0.layers:0.comps:0.col1:0.col1:0.col1:1.col1:0.classicTable1:0" powered-by="gulangu"><td colspan="1" rowspan="1" opera-tn-ra-cell="_$.pages:0.layers:0.comps:0.col1:0.col1:0.col1:1.col1:0.classicTable1:0.td@@0" width="20.0000%"><section powered-by="gulangu"><section><svg xmlns="http://www.w3.org/2000/svg" x="0px" y="0px" viewbox="0 0 27 27" width="100%"><g><path d="M13.5,27C6.06,27,0,20.94,0,13.5S6.06,0,13.5,0S27,6.06,27,13.5S20.94,27,13.5,27z M13.5,2 C7.16,2,2,7.16,2,13.5S7.16,25,13.5,25S25,19.84,25,13.5S19.84,2,13.5,2z" fill="rgba(1,169,216,0.3)"></path><circle cx="13.5" cy="13.5" r="6" fill="rgb(1,169,216)"></circle></g><text x="-10000" y="-10000">_</text></svg></section></section></td><td colspan="1" rowspan="1" opera-tn-ra-cell="_$.pages:0.layers:0.comps:0.col1:0.col1:0.col1:1.col1:0.classicTable1:0.td@@1" width="20.0000%"><section powered-by="gulangu"><section><svg xmlns="http://www.w3.org/2000/svg" x="0px" y="0px" viewbox="0 0 27 27" width="100%"><g><path d="M13.5,27C6.06,27,0,20.94,0,13.5S6.06,0,13.5,0S27,6.06,27,13.5S20.94,27,13.5,27z M13.5,2 C7.16,2,2,7.16,2,13.5S7.16,25,13.5,25S25,19.84,25,13.5S19.84,2,13.5,2z" fill="rgba(118,178,124,0)"></path><circle cx="13.5" cy="13.5" r="6" fill="rgba(118,178,124,0)"></circle></g><text x="-10000" y="-10000">_</text></svg></section></section></td><td colspan="1" rowspan="1" opera-tn-ra-cell="_$.pages:0.layers:0.comps:0.col1:0.col1:0.col1:1.col1:0.classicTable1:0.td@@2" width="20.0000%"><section powered-by="gulangu"><section><svg xmlns="http://www.w3.org/2000/svg" x="0px" y="0px" viewbox="0 0 27 27" width="100%"><g><path d="M13.5,27C6.06,27,0,20.94,0,13.5S6.06,0,13.5,0S27,6.06,27,13.5S20.94,27,13.5,27z M13.5,2 C7.16,2,2,7.16,2,13.5S7.16,25,13.5,25S25,19.84,25,13.5S19.84,2,13.5,2z" fill="rgba(118,178,124,0)"></path><circle cx="13.5" cy="13.5" r="6" fill="rgba(118,178,124,0)"></circle></g><text x="-10000" y="-10000">_</text></svg></section></section></td><td colspan="1" rowspan="1" opera-tn-ra-cell="_$.pages:0.layers:0.comps:0.col1:0.col1:0.col1:1.col1:0.classicTable1:0.td@@3" width="20.0000%"><section powered-by="gulangu"><section><svg xmlns="http://www.w3.org/2000/svg" x="0px" y="0px" viewbox="0 0 27 27" width="100%"><g><path d="M13.5,27C6.06,27,0,20.94,0,13.5S6.06,0,13.5,0S27,6.06,27,13.5S20.94,27,13.5,27z M13.5,2 C7.16,2,2,7.16,2,13.5S7.16,25,13.5,25S25,19.84,25,13.5S19.84,2,13.5,2z" fill="rgba(118,178,124,0)"></path><circle cx="13.5" cy="13.5" r="6" fill="rgba(118,178,124,0)"></circle></g><text x="-10000" y="-10000">_</text></svg></section></section></td><td colspan="1" rowspan="1" opera-tn-ra-cell="_$.pages:0.layers:0.comps:0.col1:0.col1:0.col1:1.col1:0.classicTable1:0.td@@4" 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powered-by="gulangu"><section><svg xmlns="http://www.w3.org/2000/svg" x="0px" y="0px" viewbox="0 0 27 27" width="100%"><g><path d="M13.5,27C6.06,27,0,20.94,0,13.5S6.06,0,13.5,0S27,6.06,27,13.5S20.94,27,13.5,27z M13.5,2 C7.16,2,2,7.16,2,13.5S7.16,25,13.5,25S25,19.84,25,13.5S19.84,2,13.5,2z" fill="rgba(118,178,124,0)"></path><circle cx="13.5" cy="13.5" r="6" fill="rgb(237, 237, 237)"></circle></g><text x="-10000" y="-10000">_</text></svg></section></section></td></tr></tbody></table></section></section></section></section></section></section></section></section><p><br /></p><p><span>截止到目前佳能Precision CT已经发表的Peer-reviewed SCI论文将近20篇，2018年放射领域顶级杂志Radiology上发表了佳能Precision CT对肾动脉支架内再狭窄诊断的文章，证明超高清成像可以对1mm支架内再狭窄进行精确诊断。European Radiology和Circulation Journal上也都发表了Precision CT在肺部和冠脉的研究成果。</span></p><p><br /></p><p><span><strong><span>SEE POSSIBILITIES TOGETHER</span></strong></span><span>是RSNA 2019的主题。此次佳能医疗推出的业内首款深度学习图像重建系统DLR-AiCE和业内首款搭载4K采集影像链的超高分辨率CT Aquilion Precision，的确给我们带来了极大的想象空间。图像算法和影像采集的同时突破，将为CT的临床应用带来怎样的提升呢？“See possibilities together, Enjoy Precision together”让我们拭目以待。</span></p><p><span><br /></span></p><section data-role="outer" label="Powered by gulangu"><section data-role="paragraph" data-color="#757576"><section data-role="paragraph"><section><section><section data-brushtype="text"><strong>相关阅读</strong></section></section></section><section><section></section></section><section data-width="100%"><section><section><section><section data-width="100%"><section data-tools="gulangu" data-id="87578" data-color="#6aa9ad" data-custom="#59c3f9"><section><p><img src="image/20201014/2aac877ff9233ba5a66e7a5ff3a4febf_19.gif" /></p></section><section data-brushtype="text">戳一下，更有料！</section></section><section data-tools="gulangu" data-id="87578" data-color="#6aa9ad" data-custom="#59c3f9"></section><section data-tools="gulangu" data-id="87578" data-color="#6aa9ad" data-custom="#59c3f9"><section data-tools="gulangu" data-id="87578" 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发表于 2020-10-14 13:14:23