Alex de Crespigny

3.6k total citations
49 papers, 2.2k citations indexed

About

Alex de Crespigny is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Epidemiology. According to data from OpenAlex, Alex de Crespigny has authored 49 papers receiving a total of 2.2k indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Radiology, Nuclear Medicine and Imaging, 9 papers in Pulmonary and Respiratory Medicine and 8 papers in Epidemiology. Recurrent topics in Alex de Crespigny's work include Advanced MRI Techniques and Applications (21 papers), Advanced Neuroimaging Techniques and Applications (18 papers) and MRI in cancer diagnosis (11 papers). Alex de Crespigny is often cited by papers focused on Advanced MRI Techniques and Applications (21 papers), Advanced Neuroimaging Techniques and Applications (18 papers) and MRI in cancer diagnosis (11 papers). Alex de Crespigny collaborates with scholars based in United States, Switzerland and Germany. Alex de Crespigny's co-authors include Michael E. Moseley, Helen D’Arceuil, Kim Butts, John M. Pauly, Michael P. Marks, Andreas Kastrup, Gregory W. Albers, Midori A. Yenari, Christian Beaulieu and Tobias Neumann‐Haefelin and has published in prestigious journals such as Journal of Clinical Oncology, SHILAP Revista de lepidopterología and Blood.

In The Last Decade

Alex de Crespigny

49 papers receiving 2.2k citations

Peers

Alex de Crespigny
Daniel J. Tozer United Kingdom
Jan Sedlacik Germany
Mathias Hoehn‐Berlage Germany
Edward S. Hui Hong Kong
Sheng-Kwei Song United States
Akio Hiwatashi Japan
Wenzhen Zhu China
Mika Kitajima Japan
Joseph C. McGowan United States
Andreas Stadlbauer Germany
Daniel J. Tozer United Kingdom
Alex de Crespigny
Citations per year, relative to Alex de Crespigny Alex de Crespigny (= 1×) peers Daniel J. Tozer

Countries citing papers authored by Alex de Crespigny

Since Specialization
Citations

This map shows the geographic impact of Alex de Crespigny'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 Alex de Crespigny with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alex de Crespigny more than expected).

Fields of papers citing papers by Alex de Crespigny

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Alex de Crespigny. 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 Alex de Crespigny. The network helps show where Alex de Crespigny may publish in the future.

Co-authorship network of co-authors of Alex de Crespigny

This figure shows the co-authorship network connecting the top 25 collaborators of Alex de Crespigny. A scholar is included among the top collaborators of Alex de Crespigny 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 Alex de Crespigny. Alex de Crespigny 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.
Fredrickson, Jill, et al.. (2025). Comparative analysis of intestinal tumor segmentation in PET CT scans using organ based and whole body deep learning. BMC Medical Imaging. 25(1). 52–52. 1 indexed citations
2.
Wang, Xiaoyong, Tarec Christoffer El‐Galaly, Lale Kostakoglu, et al.. (2024). Automated Lugano Metabolic Response Assessment in 18 F-Fluorodeoxyglucose–Avid Non-Hodgkin Lymphoma With Deep Learning on 18 F-Fluorodeoxyglucose–Positron Emission Tomography. Journal of Clinical Oncology. 42(25). 2966–2977. 6 indexed citations
3.
Krishnan, Anitha Priya, Zhuang Song, David Clayton, et al.. (2023). Multi-arm U-Net with dense input and skip connectivity for T2 lesion segmentation in clinical trials of multiple sclerosis. Scientific Reports. 13(1). 4102–4102. 12 indexed citations
4.
Hou, Xuefeng, et al.. (2023). Weak to no correlation between quantitative high-resolution computed tomography metrics and lung function change in fibrotic diseases. ERJ Open Research. 9(5). 210–2023. 2 indexed citations
5.
Paulson, Joseph N., Martin Hutchings, Lale Kostakoglu, et al.. (2022). Full automation of total metabolic tumor volume from FDG-PET/CT in DLBCL for baseline risk assessments. Cancer Imaging. 22(1). 39–39. 18 indexed citations
6.
Clayton, David, Alexandre Coimbra, Farshid Faraji, et al.. (2021). Resting-state functional magnetic resonance imaging in a randomized clinical trial for Alzheimer's disease. SHILAP Revista de lepidopterología. 1(4). 100055–100055. 1 indexed citations
7.
Fredrickson, Jill, et al.. (2020). Tumor Segmentation and Feature Extraction from Whole-Body FDG-PET/CT Using Cascaded 2D and 3D Convolutional Neural Networks. Journal of Digital Imaging. 33(4). 888–894. 51 indexed citations
8.
Christian, Paul E., Simon‐Peter Williams, Andrei Iagaru, et al.. (2019). Optimization of 89Zr PET Imaging for Improved Multisite Quantification and Lesion Detection Using an Anthropomorphic Phantom. Journal of Nuclear Medicine Technology. 48(1). 54–57. 4 indexed citations
9.
Shapiro, Geoffrey I., Patricia LoRusso, Eunice L. Kwak, et al.. (2019). Phase Ib study of the MEK inhibitor cobimetinib (GDC-0973) in combination with the PI3K inhibitor pictilisib (GDC-0941) in patients with advanced solid tumors. Investigational New Drugs. 38(2). 419–432. 73 indexed citations
10.
Azadbakht, Hojjat, Laura M. Parkes, Hamied Haroon, et al.. (2015). Validation of High-Resolution Tractography AgainstIn VivoTracing in the Macaque Visual Cortex. Cerebral Cortex. 25(11). 4299–4309. 74 indexed citations
11.
12.
Leergaard, Trygve B., Nathan S. White, Alex de Crespigny, et al.. (2010). Quantitative Histological Validation of Diffusion MRI Fiber Orientation Distributions in the Rat Brain. PLoS ONE. 5(1). e8595–e8595. 139 indexed citations
13.
Crespigny, Alex de, Hani Bou-Reslan, Merry Nishimura, et al.. (2008). 3D micro-CT imaging of the postmortem brain. Journal of Neuroscience Methods. 171(2). 207–213. 76 indexed citations
14.
D’Arceuil, Helen & Alex de Crespigny. (2007). The effects of brain tissue decomposition on diffusion tensor imaging and tractography. NeuroImage. 36(1). 64–68. 148 indexed citations
15.
D’Arceuil, Helen, Christina Liu, Pat Levitt, et al.. (2007). Three-Dimensional High-Resolution Diffusion Tensor Imaging and Tractography of the Developing Rabbit Brain. Developmental Neuroscience. 30(4). 262–275. 32 indexed citations
16.
Liu, Yutong, Helen D’Arceuil, Julian He, et al.. (2006). A nonlinear mesh-warping technique for correcting brain deformation after stroke. Magnetic Resonance Imaging. 24(8). 1069–1075. 4 indexed citations
17.
Liu, Yutong, Helen D’Arceuil, Julian He, et al.. (2005). Dynamic susceptibility contrast perfusion imaging of cerebral ischemia in nonhuman primates: Comparison of Gd‐DTPA and NMS60. Journal of Magnetic Resonance Imaging. 22(4). 461–466. 4 indexed citations
18.
Neumann‐Haefelin, Tobias, Andreas Kastrup, Alex de Crespigny, et al.. (2001). MRI of subacute hemorrhagic transformation in the rat suture occlusion model. Neuroreport. 12(2). 309–311. 15 indexed citations
19.
Butts, Kim, Alex de Crespigny, John M. Pauly, & Michael E. Moseley. (1996). Diffusion‐weighted interleaved echo‐planar imaging with a pair of orthogonal navigator echoes. Magnetic Resonance in Medicine. 35(5). 763–770. 195 indexed citations
20.
Moseley, Michael E., Alex de Crespigny, & W Chew. (1992). DIFFUSION/PERFUSION MAGNETIC RESONANCE IMAGING. Neuroimaging Clinics of North America. 2(4). 693–718. 1 indexed citations

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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