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T1 maps by K-space reduced snapshot-FLASH MRI.

Journal of computer assisted tomography

Nekolla S, Gneiting T, Syha J, Deichmann R, Haase A.
PMID: 1545039
J Comput Assist Tomogr. 1992 Mar-Apr;16(2):327-32. doi: 10.1097/00004728-199203000-00031.

The T1 maps evaluated from k-space reduced Snapshot fast low angle shot (FLASH) images provide high contrast parameter images for tissue characterization in vivo of any body region. An algorithm for computing T1 values that allows a fast and...

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Nekolla S, Gneiting T, Syha J, et al. T1 maps by K-space reduced snapshot-FLASH MRI. J Comput Assist Tomogr. 1992;16(2):327-32doi: 10.1097/00004728-199203000-00031.
Nekolla, S., Gneiting, T., Syha, J., Deichmann, R., & Haase, A. (1992). T1 maps by K-space reduced snapshot-FLASH MRI. Journal of computer assisted tomography, 16(2), 327-32. https://doi.org/10.1097/00004728-199203000-00031
Nekolla, S, et al. "T1 maps by K-space reduced snapshot-FLASH MRI." Journal of computer assisted tomography vol. 16,2 (1992): 327-32. doi: https://doi.org/10.1097/00004728-199203000-00031
Nekolla S, Gneiting T, Syha J, Deichmann R, Haase A. T1 maps by K-space reduced snapshot-FLASH MRI. J Comput Assist Tomogr. 1992 Mar-Apr;16(2):327-32. doi: 10.1097/00004728-199203000-00031. PMID: 1545039.
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The Influence of Contrast-to-Noise Ratio on the Discrimination Between Cortical and Juxtacortical Lesions in Multiple Sclerosis: Erratum.

Journal of computer assisted tomography

[No authors listed]
PMID: 32195812
J Comput Assist Tomogr. 2020 Mar/Apr;44(2):310. doi: 10.1097/RCT.0000000000001015.

No abstract available.

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The Influence of Contrast-to-Noise Ratio on the Discrimination Between Cortical and Juxtacortical Lesions in Multiple Sclerosis: Erratum. J Comput Assist Tomogr. 2020;44(2):310doi: 10.1097/RCT.0000000000001015.
(2020). The Influence of Contrast-to-Noise Ratio on the Discrimination Between Cortical and Juxtacortical Lesions in Multiple Sclerosis: Erratum. Journal of computer assisted tomography, 44(2), 310. https://doi.org/10.1097/RCT.0000000000001015
"The Influence of Contrast-to-Noise Ratio on the Discrimination Between Cortical and Juxtacortical Lesions in Multiple Sclerosis: Erratum." Journal of computer assisted tomography vol. 44,2 (2020): 310. doi: https://doi.org/10.1097/RCT.0000000000001015
The Influence of Contrast-to-Noise Ratio on the Discrimination Between Cortical and Juxtacortical Lesions in Multiple Sclerosis: Erratum. J Comput Assist Tomogr. 2020 Mar/Apr;44(2):310. doi: 10.1097/RCT.0000000000001015. PMID: 32195812.
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Machine Learning and Deep Learning in Oncologic Imaging: Potential Hurdles, Opportunities for Improvement, and Solutions-Abdominal Imagers' Perspective.

Journal of computer assisted tomography

Yedururi S, Morani AC, Katabathina VS, Jo N, Rachamallu M, Prasad S, Marcal L.
PMID: 34270486
J Comput Assist Tomogr. 2021 Nov-Dec 01;45(6):805-811. doi: 10.1097/RCT.0000000000001183.

ABSTRACT: The applications of machine learning in clinical radiology practice and in particular oncologic imaging practice are steadily evolving. However, there are several potential hurdles for widespread implementation of machine learning in oncologic imaging, including the lack of availability...

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Yedururi S, Morani AC, Katabathina VS, et al. Machine Learning and Deep Learning in Oncologic Imaging: Potential Hurdles, Opportunities for Improvement, and Solutions-Abdominal Imagers' Perspective. J Comput Assist Tomogr. 2021;45(6):805-811doi: 10.1097/RCT.0000000000001183.
Yedururi, S., Morani, A. C., Katabathina, V. S., Jo, N., Rachamallu, M., Prasad, S., & Marcal, L. (2021). Machine Learning and Deep Learning in Oncologic Imaging: Potential Hurdles, Opportunities for Improvement, and Solutions-Abdominal Imagers' Perspective. Journal of computer assisted tomography, 45(6), 805-811. https://doi.org/10.1097/RCT.0000000000001183
Yedururi, Sireesha, et al. "Machine Learning and Deep Learning in Oncologic Imaging: Potential Hurdles, Opportunities for Improvement, and Solutions-Abdominal Imagers' Perspective." Journal of computer assisted tomography vol. 45,6 (2021): 805-811. doi: https://doi.org/10.1097/RCT.0000000000001183
Yedururi S, Morani AC, Katabathina VS, Jo N, Rachamallu M, Prasad S, Marcal L. Machine Learning and Deep Learning in Oncologic Imaging: Potential Hurdles, Opportunities for Improvement, and Solutions-Abdominal Imagers' Perspective. J Comput Assist Tomogr. 2021 Nov-Dec 01;45(6):805-811. doi: 10.1097/RCT.0000000000001183. PMID: 34270486.
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Monosodium Urate Crystal Deposition in Coronary Artery Plaque by 128-Slice Dual-Energy Computed Tomography: An Ex Vivo Phantom and In Vivo Study.

Journal of computer assisted tomography

Feuchtner GM, Plank F, Beyer C, Schwabl C, Held J, Bellmann-Weiler R, Weiss G, Gruber J, Widmann G, Klauser AS.
PMID: 34469909
J Comput Assist Tomogr. 2021 Nov-Dec 01;45(6):856-862. doi: 10.1097/RCT.0000000000001222.

OBJECTIVE: Monosodium uric acid (MSU) crystals may accumulate in the coronary plaque. The objective was to assess whether dual-energy computed tomography (DECT) allows for detection of MSU in coronary plaque.METHODS: Patients were examined with 128-slice DECT applying a cardiac...

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Feuchtner GM, Plank F, Beyer C, et al. Monosodium Urate Crystal Deposition in Coronary Artery Plaque by 128-Slice Dual-Energy Computed Tomography: An Ex Vivo Phantom and In Vivo Study. J Comput Assist Tomogr. 2021;45(6):856-862doi: 10.1097/RCT.0000000000001222.
Feuchtner, G. M., Plank, F., Beyer, C., Schwabl, C., Held, J., Bellmann-Weiler, R., Weiss, G., Gruber, J., Widmann, G., & Klauser, A. S. (2021). Monosodium Urate Crystal Deposition in Coronary Artery Plaque by 128-Slice Dual-Energy Computed Tomography: An Ex Vivo Phantom and In Vivo Study. Journal of computer assisted tomography, 45(6), 856-862. https://doi.org/10.1097/RCT.0000000000001222
Feuchtner, Gudrun M, et al. "Monosodium Urate Crystal Deposition in Coronary Artery Plaque by 128-Slice Dual-Energy Computed Tomography: An Ex Vivo Phantom and In Vivo Study." Journal of computer assisted tomography vol. 45,6 (2021): 856-862. doi: https://doi.org/10.1097/RCT.0000000000001222
Feuchtner GM, Plank F, Beyer C, Schwabl C, Held J, Bellmann-Weiler R, Weiss G, Gruber J, Widmann G, Klauser AS. Monosodium Urate Crystal Deposition in Coronary Artery Plaque by 128-Slice Dual-Energy Computed Tomography: An Ex Vivo Phantom and In Vivo Study. J Comput Assist Tomogr. 2021 Nov-Dec 01;45(6):856-862. doi: 10.1097/RCT.0000000000001222. PMID: 34469909.
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A Metric for Quantification of Iodine Contrast Enhancement (Q-ICE) in Computed Tomography.

Journal of computer assisted tomography

Szczykutowicz TP, Viggiano B, Rose S, Pickhardt PJ, Lubner MG.
PMID: 34469906
J Comput Assist Tomogr. 2021 Nov-Dec 01;45(6):870-876. doi: 10.1097/RCT.0000000000001215.

BACKGROUND: Poor contrast enhancement is related to issues with examination execution, contrast prescription, computed tomography (CT) protocols, and patient conditions. Currently, our community has no metric to monitor true enhancement on routine single-phase examinations because this requires knowledge of...

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Szczykutowicz TP, Viggiano B, Rose S, et al. A Metric for Quantification of Iodine Contrast Enhancement (Q-ICE) in Computed Tomography. J Comput Assist Tomogr. 2021;45(6):870-876doi: 10.1097/RCT.0000000000001215.
Szczykutowicz, T. P., Viggiano, B., Rose, S., Pickhardt, P. J., & Lubner, M. G. (2021). A Metric for Quantification of Iodine Contrast Enhancement (Q-ICE) in Computed Tomography. Journal of computer assisted tomography, 45(6), 870-876. https://doi.org/10.1097/RCT.0000000000001215
Szczykutowicz, Timothy P, et al. "A Metric for Quantification of Iodine Contrast Enhancement (Q-ICE) in Computed Tomography." Journal of computer assisted tomography vol. 45,6 (2021): 870-876. doi: https://doi.org/10.1097/RCT.0000000000001215
Szczykutowicz TP, Viggiano B, Rose S, Pickhardt PJ, Lubner MG. A Metric for Quantification of Iodine Contrast Enhancement (Q-ICE) in Computed Tomography. J Comput Assist Tomogr. 2021 Nov-Dec 01;45(6):870-876. doi: 10.1097/RCT.0000000000001215. PMID: 34469906.
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Diffusion-Weighted Breast Magnetic Resonance Imaging: A Semiautomated Voxel Selection Technique Improves Interreader Reproducibility of Apparent Diffusion Coefficient Measurements: Erratum.

Journal of computer assisted tomography

[No authors listed]
PMID: 30640824
J Comput Assist Tomogr. 2019 Jan/Feb;43(1):163. doi: 10.1097/RCT.0000000000000849.

No abstract available.

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Diffusion-Weighted Breast Magnetic Resonance Imaging: A Semiautomated Voxel Selection Technique Improves Interreader Reproducibility of Apparent Diffusion Coefficient Measurements: Erratum. J Comput Assist Tomogr. 2019;43(1):163doi: 10.1097/RCT.0000000000000849.
(2019). Diffusion-Weighted Breast Magnetic Resonance Imaging: A Semiautomated Voxel Selection Technique Improves Interreader Reproducibility of Apparent Diffusion Coefficient Measurements: Erratum. Journal of computer assisted tomography, 43(1), 163. https://doi.org/10.1097/RCT.0000000000000849
"Diffusion-Weighted Breast Magnetic Resonance Imaging: A Semiautomated Voxel Selection Technique Improves Interreader Reproducibility of Apparent Diffusion Coefficient Measurements: Erratum." Journal of computer assisted tomography vol. 43,1 (2019): 163. doi: https://doi.org/10.1097/RCT.0000000000000849
Diffusion-Weighted Breast Magnetic Resonance Imaging: A Semiautomated Voxel Selection Technique Improves Interreader Reproducibility of Apparent Diffusion Coefficient Measurements: Erratum. J Comput Assist Tomogr. 2019 Jan/Feb;43(1):163. doi: 10.1097/RCT.0000000000000849. PMID: 30640824.
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Texture Analysis of Computed Tomography Images in the Lung of Patients With Breast Cancer.

Journal of computer assisted tomography

Han M, Qi Y, Cui X, Li R, Hou R, Dun A.
PMID: 34347709
J Comput Assist Tomogr. 2021 Nov-Dec 01;45(6):837-842. doi: 10.1097/RCT.0000000000001198.

OBJECTIVE: The aim of this study was to investigate whether the texture features of lung computed tomography images were altered by primary breast cancer without pulmonary metastasis.METHODS: Texture analysis was performed on the regions of interest of lung computed...

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Han M, Qi Y, Cui X, et al. Texture Analysis of Computed Tomography Images in the Lung of Patients With Breast Cancer. J Comput Assist Tomogr. 2021;45(6):837-842doi: 10.1097/RCT.0000000000001198.
Han, M., Qi, Y., Cui, X., Li, R., Hou, R., & Dun, A. (2021). Texture Analysis of Computed Tomography Images in the Lung of Patients With Breast Cancer. Journal of computer assisted tomography, 45(6), 837-842. https://doi.org/10.1097/RCT.0000000000001198
Han, Meng, et al. "Texture Analysis of Computed Tomography Images in the Lung of Patients With Breast Cancer." Journal of computer assisted tomography vol. 45,6 (2021): 837-842. doi: https://doi.org/10.1097/RCT.0000000000001198
Han M, Qi Y, Cui X, Li R, Hou R, Dun A. Texture Analysis of Computed Tomography Images in the Lung of Patients With Breast Cancer. J Comput Assist Tomogr. 2021 Nov-Dec 01;45(6):837-842. doi: 10.1097/RCT.0000000000001198. PMID: 34347709.
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Commentary: Recollections From the Early Days of Computed Tomography.

Journal of computer assisted tomography

Tamm EP, Stanley RJ.
PMID: 34787386
J Comput Assist Tomogr. 2021 Nov-Dec 01;45(6):803-804. doi: 10.1097/RCT.0000000000001255.

No abstract available.

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Tamm EP, Stanley RJ. Commentary: Recollections From the Early Days of Computed Tomography. J Comput Assist Tomogr. 2021;45(6):803-804doi: 10.1097/RCT.0000000000001255.
Tamm, E. P., & Stanley, R. J. (2021). Commentary: Recollections From the Early Days of Computed Tomography. Journal of computer assisted tomography, 45(6), 803-804. https://doi.org/10.1097/RCT.0000000000001255
Tamm, Eric P, and Stanley, Robert J. "Commentary: Recollections From the Early Days of Computed Tomography." Journal of computer assisted tomography vol. 45,6 (2021): 803-804. doi: https://doi.org/10.1097/RCT.0000000000001255
Tamm EP, Stanley RJ. Commentary: Recollections From the Early Days of Computed Tomography. J Comput Assist Tomogr. 2021 Nov-Dec 01;45(6):803-804. doi: 10.1097/RCT.0000000000001255. PMID: 34787386.
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Grading Trigone Meningiomas Using Conventional Magnetic Resonance Imaging With Susceptibility-Weighted Imaging and Perfusion-Weighted Imaging.

Journal of computer assisted tomography

Yang X, Xiao Z, Xing Z, Lin X, Wang F, Cao D.
PMID: 35027521
J Comput Assist Tomogr. 2022 Jan 12; doi: 10.1097/RCT.0000000000001256. Epub 2022 Jan 12.

OBJECTIVE: To compare conventional magnetic resonance imaging (MRI), susceptibility-weighted imaging (SWI), and perfusion-weighted imaging (PWI) characteristics in different grades of trigone meningiomas.METHODS: Thirty patients with trigone meningiomas were enrolled in this retrospective study. Conventional MRI was performed in all...

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Yang X, Xiao Z, Xing Z, et al. Grading Trigone Meningiomas Using Conventional Magnetic Resonance Imaging With Susceptibility-Weighted Imaging and Perfusion-Weighted Imaging. J Comput Assist Tomogr. 2022;doi: 10.1097/RCT.0000000000001256.
Yang, X., Xiao, Z., Xing, Z., Lin, X., Wang, F., & Cao, D. (2022). Grading Trigone Meningiomas Using Conventional Magnetic Resonance Imaging With Susceptibility-Weighted Imaging and Perfusion-Weighted Imaging. Journal of computer assisted tomography, . https://doi.org/10.1097/RCT.0000000000001256
Yang, Xinyi, et al. "Grading Trigone Meningiomas Using Conventional Magnetic Resonance Imaging With Susceptibility-Weighted Imaging and Perfusion-Weighted Imaging." Journal of computer assisted tomography vol. (2022). doi: https://doi.org/10.1097/RCT.0000000000001256
Yang X, Xiao Z, Xing Z, Lin X, Wang F, Cao D. Grading Trigone Meningiomas Using Conventional Magnetic Resonance Imaging With Susceptibility-Weighted Imaging and Perfusion-Weighted Imaging. J Comput Assist Tomogr. 2022 Jan 12; doi: 10.1097/RCT.0000000000001256. Epub 2022 Jan 12. PMID: 35027521.
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Ultrahigh-Resolution Computed Tomography Improves Preoperative Computed Tomography Angiography for Deep Inferior Epigastric Artery Perforator Flap Reconstruction.

Journal of computer assisted tomography

Ide S, Urikura A, Yoshida T, Nakaya Y, Endo M, Aramaki T.
PMID: 34581707
J Comput Assist Tomogr. 2021 Sep 23; doi: 10.1097/RCT.0000000000001235. Epub 2021 Sep 23.

OBJECTIVE: The aim of the study was to compare computed tomography (CT) angiography (CTA) imaging of deep inferior epigastric artery perforator (DIEP) using the ultrahigh-resolution CT (UHRCT) and conventional multidetector CT (MDCT).METHODS: This retrospective study enrolled 20 patients who...

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Ide S, Urikura A, Yoshida T, et al. Ultrahigh-Resolution Computed Tomography Improves Preoperative Computed Tomography Angiography for Deep Inferior Epigastric Artery Perforator Flap Reconstruction. J Comput Assist Tomogr. 2021;doi: 10.1097/RCT.0000000000001235.
Ide, S., Urikura, A., Yoshida, T., Nakaya, Y., Endo, M., & Aramaki, T. (2021). Ultrahigh-Resolution Computed Tomography Improves Preoperative Computed Tomography Angiography for Deep Inferior Epigastric Artery Perforator Flap Reconstruction. Journal of computer assisted tomography, . https://doi.org/10.1097/RCT.0000000000001235
Ide, Sayaka, et al. "Ultrahigh-Resolution Computed Tomography Improves Preoperative Computed Tomography Angiography for Deep Inferior Epigastric Artery Perforator Flap Reconstruction." Journal of computer assisted tomography vol. (2021). doi: https://doi.org/10.1097/RCT.0000000000001235
Ide S, Urikura A, Yoshida T, Nakaya Y, Endo M, Aramaki T. Ultrahigh-Resolution Computed Tomography Improves Preoperative Computed Tomography Angiography for Deep Inferior Epigastric Artery Perforator Flap Reconstruction. J Comput Assist Tomogr. 2021 Sep 23; doi: 10.1097/RCT.0000000000001235. Epub 2021 Sep 23. PMID: 34581707.
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AI-Based Quantitative CT Analysis of Temporal Changes According to Disease Severity in COVID-19 Pneumonia.

Journal of computer assisted tomography

Ardali Duzgun S, Durhan G, Basaran Demirkazik F, Irmak I, Karakaya J, Akpinar E, Gulsun Akpinar M, Inkaya AC, Ocal S, Topeli A, Ariyurek OM.
PMID: 34581706
J Comput Assist Tomogr. 2021 Nov-Dec 01;45(6):970-978. doi: 10.1097/RCT.0000000000001224.

OBJECTIVE: To quantitatively evaluate computed tomography (CT) parameters of coronavirus disease 2019 (COVID-19) pneumonia an artificial intelligence (AI)-based software in different clinical severity groups during the disease course.METHODS: From March 11 to April 15, 2020, 51 patients (age, 18-84...

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Ardali Duzgun S, Durhan G, Basaran Demirkazik F, et al. AI-Based Quantitative CT Analysis of Temporal Changes According to Disease Severity in COVID-19 Pneumonia. J Comput Assist Tomogr. 2021;45(6):970-978doi: 10.1097/RCT.0000000000001224.
Ardali Duzgun, S., Durhan, G., Basaran Demirkazik, F., Irmak, I., Karakaya, J., Akpinar, E., Gulsun Akpinar, M., Inkaya, A. C., Ocal, S., Topeli, A., & Ariyurek, O. M. (2021). AI-Based Quantitative CT Analysis of Temporal Changes According to Disease Severity in COVID-19 Pneumonia. Journal of computer assisted tomography, 45(6), 970-978. https://doi.org/10.1097/RCT.0000000000001224
Ardali Duzgun, Selin, et al. "AI-Based Quantitative CT Analysis of Temporal Changes According to Disease Severity in COVID-19 Pneumonia." Journal of computer assisted tomography vol. 45,6 (2021): 970-978. doi: https://doi.org/10.1097/RCT.0000000000001224
Ardali Duzgun S, Durhan G, Basaran Demirkazik F, Irmak I, Karakaya J, Akpinar E, Gulsun Akpinar M, Inkaya AC, Ocal S, Topeli A, Ariyurek OM. AI-Based Quantitative CT Analysis of Temporal Changes According to Disease Severity in COVID-19 Pneumonia. J Comput Assist Tomogr. 2021 Nov-Dec 01;45(6):970-978. doi: 10.1097/RCT.0000000000001224. PMID: 34581706; PMCID: PMC8607923.
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Registration.

Neuromethods

Joshi AA.
PMID: 30842692
Neuromethods. 2018;136:3-12. doi: 10.1007/978-1-4939-7647-8_1. Epub 2018 Feb 28.

The goal of image registration is to find a 1-1 point-wise correspondence between two images, a subject image and a target image. Knowing the pointwise correspondence between two brain images allows comparison of structural and functional imaging data such...

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Joshi AA. Registration. Neuromethods. 2018;136:3-12doi: 10.1007/978-1-4939-7647-8_1.
Joshi, A. A. (2018). Registration. Neuromethods, 1363-12. https://doi.org/10.1007/978-1-4939-7647-8_1
Joshi, Anand A. "Registration." Neuromethods vol. 136 (2018): 3-12. doi: https://doi.org/10.1007/978-1-4939-7647-8_1
Joshi AA. Registration. Neuromethods. 2018;136:3-12. doi: 10.1007/978-1-4939-7647-8_1. Epub 2018 Feb 28. PMID: 30842692; PMCID: PMC6398434.
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