Jo‐Chi Jao

564 total citations
25 papers, 470 citations indexed

About

Jo‐Chi Jao is a scholar working on Radiology, Nuclear Medicine and Imaging, Materials Chemistry and Hepatology. According to data from OpenAlex, Jo‐Chi Jao has authored 25 papers receiving a total of 470 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Radiology, Nuclear Medicine and Imaging, 6 papers in Materials Chemistry and 4 papers in Hepatology. Recurrent topics in Jo‐Chi Jao's work include Advanced MRI Techniques and Applications (15 papers), MRI in cancer diagnosis (9 papers) and Lanthanide and Transition Metal Complexes (5 papers). Jo‐Chi Jao is often cited by papers focused on Advanced MRI Techniques and Applications (15 papers), MRI in cancer diagnosis (9 papers) and Lanthanide and Transition Metal Complexes (5 papers). Jo‐Chi Jao collaborates with scholars based in Taiwan, United States and China. Jo‐Chi Jao's co-authors include Min‐Ying Su, Orhan Nalcioğlu, Chun‐Wei Li, Yu‐Ting Kuo, Ding‐Kwo Wu, Jui‐Sheng Hsu, Twei‐Shiun Jaw, Chiao‐Yun Chen, Gin‐Chung Liu and Meng-Fang Tsai and has published in prestigious journals such as Radiology, Magnetic Resonance in Medicine and Expert Systems with Applications.

In The Last Decade

Jo‐Chi Jao

23 papers receiving 461 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Jo‐Chi Jao Taiwan 8 358 128 80 46 41 25 470
M. A. D. Vente Netherlands 13 438 1.2× 414 3.2× 71 0.9× 36 0.8× 51 1.2× 18 744
Cindy F. Connell United States 4 178 0.5× 74 0.6× 27 0.3× 85 1.8× 12 0.3× 6 374
Maurice AAJ van den Bosch Netherlands 6 194 0.5× 140 1.1× 34 0.4× 4 0.1× 19 0.5× 6 310
Chikashi Negishi United States 4 212 0.6× 144 1.1× 140 1.8× 20 0.4× 123 3.0× 5 409
K.-P. Lodemann Germany 12 239 0.7× 110 0.9× 98 1.2× 9 0.2× 140 3.4× 15 405
Parviz Farshid Germany 8 83 0.2× 136 1.1× 71 0.9× 21 0.5× 8 0.2× 13 322
Yuelang Zhang China 14 99 0.3× 73 0.6× 60 0.8× 133 2.9× 5 0.1× 31 369
Lore Santoro France 11 310 0.9× 13 0.1× 78 1.0× 40 0.9× 24 0.6× 25 417
Tianpei Guan China 12 57 0.2× 73 0.6× 27 0.3× 84 1.8× 51 1.2× 20 375
Aurélie Desbrée France 12 293 0.8× 24 0.2× 13 0.2× 13 0.3× 18 0.4× 41 370

Countries citing papers authored by Jo‐Chi Jao

Since Specialization
Citations

This map shows the geographic impact of Jo‐Chi Jao's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jo‐Chi Jao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jo‐Chi Jao more than expected).

Fields of papers citing papers by Jo‐Chi Jao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jo‐Chi Jao. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jo‐Chi Jao. The network helps show where Jo‐Chi Jao may publish in the future.

Co-authorship network of co-authors of Jo‐Chi Jao

This figure shows the co-authorship network connecting the top 25 collaborators of Jo‐Chi Jao. A scholar is included among the top collaborators of Jo‐Chi Jao based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jo‐Chi Jao. Jo‐Chi Jao is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Ho, Chih-Hao, et al.. (2020). Assessment of image quality and dose in contrast-enhanced head and neck CT angiography of New Zealand rabbit. Journal of X-Ray Science and Technology. 28(4). 739–750. 1 indexed citations
2.
Tsai, Meng-Fang & Jo‐Chi Jao. (2020). Evaluation of the effectiveness of student learning and teacher instruction on team-based learning during quality control of diagnostic imaging. Medical Education Online. 25(1). 1732159–1732159. 9 indexed citations
3.
Ben, Ren-Jy, et al.. (2019). Longitudinal investigation of ischemic stroke using magnetic resonance imaging: Animal model. Journal of X-Ray Science and Technology. 27(5). 935–947. 4 indexed citations
4.
Lin, Tzeng‐Jih, Yeou‐Lih Huang, Jung‐San Chang, et al.. (2018). Optimal dosage and early intervention of L-ascorbic acid inhibiting K2Cr2O7-induced renal tubular cell damage. Journal of Trace Elements in Medicine and Biology. 48. 1–7. 15 indexed citations
5.
Jao, Jo‐Chi, et al.. (2017). Evaluation of Rabbit VX2 Tumor Model Using Magnetic Resonance T1-Mapping and T2-Mapping Techniques at 1.5T. Journal of Medical and Biological Engineering. 38(4). 607–617. 1 indexed citations
6.
Jao, Jo‐Chi, et al.. (2015). Geometric distortion evaluation using a multi-orientated water-phantom at 0.2 T MRI. Bio-Medical Materials and Engineering. 26(1_suppl). S1431–8. 1 indexed citations
7.
Jao, Jo‐Chi, et al.. (2015). The Impact of Flip Angle and TR on the Enhancement Ratio of Dynamic Gadobutrol-enhanced MR Imaging: <i>In Vivo</i> VX2 Tumor Model and Computer Simulation. Magnetic Resonance in Medical Sciences. 14(3). 193–202. 2 indexed citations
8.
Hsiao, Pi‐Jung, et al.. (2013). Inorganic arsenic trioxide induces gap junction loss in association with the downregulation of connexin43 and E‐cadherin in rat hepatic “stem‐like” cells. The Kaohsiung Journal of Medical Sciences. 30(2). 57–67. 13 indexed citations
9.
Jao, Jo‐Chi, et al.. (2013). Correlation between SPGR MR signal intensity and iron concentration: Phantom study. 5(1-2). 39–43. 1 indexed citations
10.
11.
Jao, Jo‐Chi, et al.. (2012). Effect of gadobutrol on VX2 magnetic resonance diffusion-weighted imaging. PubMed. 2012. 384–387.
12.
Pan, Huay‐Ben, et al.. (2011). OPTIMAL CONCENTRATIONS OF GADOVIST IN T1-WEIGHTED MAGNETIC RESONANCE IMAGING: PHANTOM STUDY AND COMPUTER SIMULATION. Biomedical Engineering Applications Basis and Communications. 23(3). 237–244. 1 indexed citations
13.
Jao, Jo‐Chi, et al.. (2010). Investigation of Early Liver Radiation Injury Using Resovist-Enhanced MRI at 3T. International Conference on Bioinformatics and Biomedical Engineering. 1–4. 3 indexed citations
14.
Shen, Binghui, et al.. (2009). RecX is Involved In the Switch between DNA Damage Response and Normal Metabolism in D. radiodurans. The Journal of Biochemistry. 146(3). 337–342. 6 indexed citations
15.
Jao, Jo‐Chi, et al.. (2008). THE IMPACT OF FACTOR TE ON THE MEASUREMENT OF T1 RELAXIVITY. Biomedical Engineering Applications Basis and Communications. 20(5). 277–277. 2 indexed citations
16.
Chen, Chiao‐Yun, Chun‐Wei Li, Yu‐Ting Kuo, et al.. (2006). Early Response of Hepatocellular Carcinoma to Transcatheter Arterial Chemoembolization: Choline Levels and MR Diffusion Constants—Initial Experience. Radiology. 239(2). 448–456. 147 indexed citations
17.
Jao, Jo‐Chi, et al.. (2005). Effect of Intravenous Gadolinium-DTPA on Diffusion-Weighted Magnetic Resonance Images for Evaluation of Focal Hepatic Lesions. Journal of Computer Assisted Tomography. 29(2). 176–180. 54 indexed citations
18.
Jao, Jo‐Chi, et al.. (2005). Discovering conjecturable rules through tree-based clustering analysis. Expert Systems with Applications. 29(3). 493–505. 5 indexed citations
19.
Hsu, Jui‐Sheng, Twei‐Shiun Jaw, Gin‐Chung Liu, et al.. (2004). Evaluation of [Gd(Bz‐TTDA)]2– as a potential contrast agent in MR imaging of the hepatobiliary system: An animal study. Journal of Magnetic Resonance Imaging. 20(4). 632–639. 6 indexed citations
20.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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