TY - JOUR
T1 - Pulmonary functional imaging
T2 - Part 1—state-of-the-art technical and physiologic underpinnings
AU - Ohno, Yoshiharu
AU - Seo, Joon Beom
AU - Parraga, Grace
AU - Lee, Kyung Soo
AU - Gefter, Warren B.
AU - Fain, Sean B.
AU - Schiebler, Mark L.
AU - Hatabu, Hiroto
N1 - Funding Information:
Disclosures of Conflicts of Interest: Y.O. Activities related to the present article: disclosed money to author’s institution for grant from Canon Medical Systems. Activities not related to the present article: disclosed money to author’s institution for grants/grants pending from Bayer Pharma; disclosed grants-in-aid for scientific research from the Japanese Ministry of Education, Culture, Sports, Science and Technology; research grant from Smoking Research Foundation; research grant from Daiichi Sankyo. Other relationships: disclosed no relevant relationships. J.B.S. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: disclosed money to author’s institution for multiple issued patents in Korea; disclosed royaltiy for license of patents to Coreline Soft; disclosed stock/stock options from Coreline Soft and Premedius. Other relationships: disclosed that author is a licensee for Coreline Soft. G.P. disclosed no relevant relationships. K.S.L. disclosed no relevant relationships. W.B.G. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: disclosed consultancy from Imbio; grants/grants pending from Siemens Medical Solutions. Other relationships: disclosed no relevant relationships. S.B.F. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: disclosed consultancy for Sanofi/Regen-eron, Polarean; disclosed grants from NIH, GE Healthcare; disclosed payment for lectures from Sanofi/Regeneron. Other relationships: disclosed no relevant relationships. M.L.S. disclosed no relevant relationships. H.H. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: disclosed consultancy from Mitsubishi Chemical, Canon Medical Systems; grants/grants pending from Canon Medical Systems, Konica-Minolta. Other relationships: disclosed no relevant relationships.
Funding Information:
H.H. supported by the National Instutitues of Health (R01CA203636, 5U01CA209414-03, R01HL135142) and the National Heart, Lung, and Blood Institute (1R01HL130974, 2R01HL111024-06); M.L.S. supported by National Heart, Lung, and Blood Institute (1U01 HL102225-01, R01 HL091762, P01 HL07083); S.B.F. supported by Pulmonary Imaging Center, Office of the Vice Chancellor for Research and Graduate Education at the University of Wisconsin–Madison with funding from the Wisconsin Alumni Research Foundation (S10 OD016394, R01 EB021314, U01 HL146002, UG1 HL139118, R01 HL126771); G.P. supported by Canadian Institutes of Health Research (PJT 148624, HEV 440431), Natural Sciences and Engineering Research Council of Canada, Canada Research Chair Program.
Publisher Copyright:
© RSNA, 2021.
PY - 2021/6
Y1 - 2021/6
N2 - Over the past few decades, pulmonary imaging technologies have advanced from chest radiography and nuclear medicine methods to high-spatial-resolution or low-dose chest CT and MRI. It is currently possible to identify and measure pulmonary pathologic changes before these are obvious even to patients or depicted on conventional morphologic images. Here, key technological advances are described, including multiparametric CT image processing methods, inhaled hyperpolarized and fluorinated gas MRI, and four-dimensional free-breathing CT and MRI methods to measure regional ventilation, perfusion, gas exchange, and biomechanics. The basic anatomic and physiologic underpinnings of these pulmonary functional imaging techniques are explained. In addition, advances in image analysis and computational and artificial intelligence (machine learning) methods pertinent to functional lung imaging are discussed. The clinical applications of pulmonary functional imaging, including both the opportunities and challenges for clinical translation and deployment, will be discussed in part 2 of this review. Given the technical advances in these sophisticated imaging methods and the wealth of information they can provide, it is anticipated that pulmonary functional imaging will be increasingly used in the care of patients with lung disease.
AB - Over the past few decades, pulmonary imaging technologies have advanced from chest radiography and nuclear medicine methods to high-spatial-resolution or low-dose chest CT and MRI. It is currently possible to identify and measure pulmonary pathologic changes before these are obvious even to patients or depicted on conventional morphologic images. Here, key technological advances are described, including multiparametric CT image processing methods, inhaled hyperpolarized and fluorinated gas MRI, and four-dimensional free-breathing CT and MRI methods to measure regional ventilation, perfusion, gas exchange, and biomechanics. The basic anatomic and physiologic underpinnings of these pulmonary functional imaging techniques are explained. In addition, advances in image analysis and computational and artificial intelligence (machine learning) methods pertinent to functional lung imaging are discussed. The clinical applications of pulmonary functional imaging, including both the opportunities and challenges for clinical translation and deployment, will be discussed in part 2 of this review. Given the technical advances in these sophisticated imaging methods and the wealth of information they can provide, it is anticipated that pulmonary functional imaging will be increasingly used in the care of patients with lung disease.
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U2 - 10.1148/radiol.2021203711
DO - 10.1148/radiol.2021203711
M3 - Review article
C2 - 33825513
AN - SCOPUS:85106910569
SN - 0033-8419
VL - 299
SP - 508
EP - 523
JO - Radiology
JF - Radiology
IS - 3
ER -