【研究發展處訊】

發表期刊論文名單如下：

Ø   外語學院英文學系 鄭惠雯助理教授

Ø   理學院物理學系 梅長生教授

Ø   理學院物理學系 梅長生教授

Ø   外語學院英文學系 鄭惠雯助理教授

論文名稱：無法(不)翻譯：從「語言友好」論水村美苗與約翰妮森的混語書寫

作    者：鄭惠雯

期刊名稱：中外文學（THCI）

卷期數：55(1)

頁碼(文獻號碼) ：169-203

出版時間：2026.3

摘要：

本文旨在探討當代混語書寫內部及外部涉及之語言、文學、翻譯的深刻勾連關係，取徑於呂格爾「語言友好」論述，並援引沃蔻薇姿「生而為翻譯的小說」概念來思考今日文學多語主義實踐在英語霸權時代裡所代表的意義與樣貌。以日本小說家水村美苗的《私小說 from left to right》與瑞典裔青年小說家約翰妮森的《我們是如何被翻譯的》為例，思考這兩位英語為非母語作家如何藉混語書寫走出一條既能實踐語言友好又能保有流通的路徑，再現移／異／譯鄉人主體所面臨的（不）友好多語境況與倫理侷限，並探討翻譯如何穿透其小說作品的內外結構，使其成為生而為翻 譯的小說，在創作語言、閱讀效果、翻譯和流通等實際層面回應「無法（不）翻譯」的倫理呼求，以翻譯作為協商差異的範式。期望此探求能對翻譯與當代混語書寫之關聯性帶來些許啟發與貢獻。

Ø   理學院物理學系 梅長生教授

論文名稱：Accelerated proton resonance frequency-based magnetic resonance thermometry by optimized deep learning method

作    者：Xu, Sijie ; Zong, Shenyan ; Mei, Chang-Sheng ; Shen, Guofeng ; Zhao, Yueran ; Wang, He

期刊名稱：MEDICAL PHYSICS（SCI）

卷期數：52(7)

頁碼(文獻號碼)：e17909

出版時間：JUL 2025

摘要：

BackgroundProton resonance frequency (PRF)-based magnetic resonance (MR) thermometry plays a critical role in thermal ablation therapies through focused ultrasound (FUS). For clinical applications, accurate and rapid temperature feedback is essential to ensure both the safety and effectiveness of these treatments.PurposeThis work aims to improve temporal resolution in dynamic MR temperature map reconstructions using an enhanced deep-learning method, thereby supporting the real-time monitoring required for effective FUS treatments.MethodsFive classical neural network architectures-cascade net, complex-valued U-Net, shift window transformer for MRI, real-valued U-Net, and U-Net with residual blocks-along with training-optimized methods were applied to reconstruct temperature maps from 2-fold and 4-fold undersampled k-space data. The training enhancements included pre-training/training-phase data augmentations, knowledge distillation, and a novel amplitude-phase decoupling loss function. Phantom and ex vivo tissue heating experiments were conducted using a FUS transducer. Ground truth was the complex MR images with accurate temperature changes, and datasets were manually undersampled to simulate such acceleration here. Separate testing datasets were used to evaluate real-time performance and temperature accuracy. Furthermore, our proposed deep learning-based rapid reconstruction approach was validated on a clinical dataset obtained from patients with uterine fibroids, demonstrating its clinical applicability.ResultsAcceleration factors of 1.9 and 3.7 were achieved for 2x and 4x k-space under samplings, respectively. The deep learning-based reconstruction using ResUNet incorporating the four optimizations, showed superior performance. For 2-fold acceleration, the RMSE of temperature map patches were 0.89 degrees C and 1.15 degrees C for the phantom and ex vivo testing datasets, respectively. The DICE coefficient for the 43 degrees C isotherm-enclosed regions was 0.81, and the Bland-Altman analysis indicated a bias of -0.25 degrees C with limits of agreement of +/- 2.16 degrees C. In the 4-fold under-sampling case, these evaluation metrics showed approximately a 10% reduction in accuracy. Additionally, the DICE coefficient measuring the overlap between the reconstructed temperature maps (using the optimized ResUNet) and the ground truth, specifically in regions where the temperature exceeded the 43 degrees C threshold, were 0.77 and 0.74 for the 2x and 4x under-sampling scenarios, respectively.ConclusionThis study demonstrates that deep learning-based reconstruction significantly enhances the accuracy and efficiency of MR thermometry, particularly in the context of FUS-based clinical treatments for uterine fibroids. This approach could also be extended to other applications such as essential tremor and prostate cancer treatments where MRI-guided FUS plays a critical role.

Ø   理學院物理學系 梅長生教授

論文名稱：Focus correction in MR thermography for increased targeting precision during focused ultrasound procedures

作    者：Mei, Chang-Sheng; Zong, Shenyan ; Madore, Bruno ; Cosgrove, G. Rees ; McDannold, Nathan J.

期刊名稱：MAGNETIC RESONANCE IN MEDICINE（SCI）

卷期數：95(2)

頁碼(文獻號碼) ：951-961

出版時間：FEB 2026

摘要：

Purpose Accurate targeting during MR-guided focused ultrasound (FUS) procedures is essential for effective treatment to be achieved. However, spatial discrepancies frequently arise between the planned target and the observed thermal hotspot on proton resonance frequency (PRF)-based MR thermometry because of temperature-induced artifacts. This study aims to correct such displacements caused by chemical shift and k-space center offset.Methods Spatial misregistration was addressed using a two-step correction approach. The first step corrected pixel-wise displacements attributed to temperature-dependent resonance frequency shifts (chemical shift), based on local frequency offset maps. The second step compensated for TE errors induced by asymmetric phase gradients near the thermal focus, restoring accuracy in hotspot localization. Validation was performed in controlled phantom experiments, and the approach was retrospectively tested in vivo, in 121 sonications across seven essential tremor (ET) patients.Results Phantom experiments demonstrated that spatial shifts up to approximately 1.5 mm could be effectively corrected. Clinical analysis showed a strong correlation (R2 = 0.852) between temperature rise and spatial displacement, with a mean shift of 0.5 Mm per 10 degrees C. Combined correction significantly reduced temperature estimation bias, with the mean error decreasing from -0.11 degrees C to -0.05 degrees C, as evaluated by Bland-Altman analysis.Conclusion Temperature-related chemical shift and k-space offset substantially impact the spatial fidelity of PRF-based MR thermometry. The proposed correction framework improves thermal hotspot localization, enabling more accurate lesion targeting during FUS procedures.

研究事務組提醒：教師如有最新發表於AHCI、SSCI、SCI、TSSCI、THCI、「東吳大學外語學門獎勵名單」之期刊論文，歡迎隨時至GOOGLE表單填寫(網址為https://forms.gle/e6U4dL7pRNfgXnsHA)相關資訊，研究發展處將會公告於校園頭條及研究發展處首頁，以廣交流。

【文圖/研究事務組林洺瀠組員】