Improved detection of cholesterol gallstones using quasi-material decomposition images generated from single-energy computed tomography images via deep learning
© 2024. The Author(s), under exclusive licence to Japanese Society of Radiological Technology and Japan Society of Medical Physics..
In this study, we developed a method for generating quasi-material decomposition (quasi-MD) images from single-energy computed tomography (SECT) images using a deep convolutional neural network (DCNN). Our aim was to improve the detection of cholesterol gallstones and to determine the clinical utility of quasi-MD images. Four thousand pairs of virtual monochromatic images (70 keV) and MD images (fat/water) of the same section, obtained via dual-energy computed tomography (DECT), were used to train the DCNN. The trained DCNN can automatically generate quasi-MD images from the SECT images. Additional SECT images were obtained from 70 patients (40 with and 30 without cholesterol gallstones) to generate quasi-MD images for testing. The presence of gallstones in this dataset was confirmed by ultrasonography. We conducted a receiver operating characteristic (ROC) observer study with three radiologists to validate the clinical utility of the quasi-MD images for detecting cholesterol gallstones. The mean area under the ROC curve for the detection of cholesterol gallstones improved from 0.867 to 0.921 (p = 0.001) when quasi-MD images were added to SECT images. The clinical utility of quasi-MD imaging for detecting cholesterol gallstones was showed. This study demonstrated that the lesion detection capability of images obtained from SECT can be improved using a DCNN trained with DECT images obtained using high-end computed tomography systems.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2024 |
|---|---|
| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2024 |
|---|---|
| Enthalten in: |
Radiological physics and technology - (2024) vom: 23. Feb. |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Nishijima, Kojiro [VerfasserIn] |
|---|
| Links: |
|---|
| Anmerkungen: |
Date Revised 23.02.2024 published: Print-Electronic Citation Status Publisher |
|---|
| doi: |
10.1007/s12194-024-00783-0 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM368834808 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM368834808 | ||
| 003 | DE-627 | ||
| 005 | 20240229145521.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 240229s2024 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s12194-024-00783-0 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1303.xml |
| 035 | |a (DE-627)NLM368834808 | ||
| 035 | |a (NLM)38393491 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Nishijima, Kojiro |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Improved detection of cholesterol gallstones using quasi-material decomposition images generated from single-energy computed tomography images via deep learning |
| 264 | 1 | |c 2024 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Revised 23.02.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status Publisher | ||
| 520 | |a © 2024. The Author(s), under exclusive licence to Japanese Society of Radiological Technology and Japan Society of Medical Physics. | ||
| 520 | |a In this study, we developed a method for generating quasi-material decomposition (quasi-MD) images from single-energy computed tomography (SECT) images using a deep convolutional neural network (DCNN). Our aim was to improve the detection of cholesterol gallstones and to determine the clinical utility of quasi-MD images. Four thousand pairs of virtual monochromatic images (70 keV) and MD images (fat/water) of the same section, obtained via dual-energy computed tomography (DECT), were used to train the DCNN. The trained DCNN can automatically generate quasi-MD images from the SECT images. Additional SECT images were obtained from 70 patients (40 with and 30 without cholesterol gallstones) to generate quasi-MD images for testing. The presence of gallstones in this dataset was confirmed by ultrasonography. We conducted a receiver operating characteristic (ROC) observer study with three radiologists to validate the clinical utility of the quasi-MD images for detecting cholesterol gallstones. The mean area under the ROC curve for the detection of cholesterol gallstones improved from 0.867 to 0.921 (p = 0.001) when quasi-MD images were added to SECT images. The clinical utility of quasi-MD imaging for detecting cholesterol gallstones was showed. This study demonstrated that the lesion detection capability of images obtained from SECT can be improved using a DCNN trained with DECT images obtained using high-end computed tomography systems | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Deep convolutional neural network | |
| 650 | 4 | |a Dual-energy computed tomography | |
| 650 | 4 | |a Quasi-material decomposition image | |
| 650 | 4 | |a Receiver operating characteristic observer study | |
| 650 | 4 | |a Single-energy computed tomography | |
| 700 | 1 | |a Shiraishi, Junji |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Radiological physics and technology |d 2008 |g (2024) vom: 23. Feb. |w (DE-627)NLM201381834 |x 1865-0341 |7 nnns |
| 773 | 1 | 8 | |g year:2024 |g day:23 |g month:02 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1007/s12194-024-00783-0 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |j 2024 |b 23 |c 02 | ||