Taiga Matsumoto
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Taiga Matsumoto has a total of 13 co-authors. Here are the top co-authors by number of shared publications:
- Daisuke Sakabe
Central Radiology, Kumamoto University Hospital, Kumamoto, Japan
1 shared publication - Hiro Kiyosue
Department of Diagnostic Radiology, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
1 shared publication - Masafumi Kidoh
Kumamoto University
1 shared publication - Ryuya Yoshida
Department of Diagnostic Radiology, Kumamoto University, Kumamoto, Japan
1 shared publication - Seitaro Oda
Department of Diagnostic Radiology, Kumamoto University, Kumamoto, Japan
1 shared publication - Soichiro Ishiuchi
Department of Diagnostic Radiology, Kumamoto University, Kumamoto, Japan
1 shared publication
Latest Publications
- Open Access
- 18 August 2025
- Published by Springer Nature in Abdominal Radiology
- Vol. 51 (4) , 1974-1985
Dual-layer spectral detector CT for improved assessment of right adrenal vein in patients with primary aldosteronism
Purpose: To investigate whether virtual-monoenergetic images (VMI) from dual-layer spectral-detector computed tomography (DLCT) improve right adrenal vein (RAV) assessment before adrenal venous sampling (AVS), and portal-venous-phase (PVP) images alone can replace conventional multiphase CT combining late-arterial phase (LAP) and PVP. Methods: Sixty-three patients with primary aldosteronism who underwent LAP and PVP DLCT before AVS were retrospectively analyzed. Conventional polyenergetic-images (PEI) and VMI at 40–70 keV (VMI 40–70 ) were reconstructed. Image noise and contrast-to-noise ratio (CNR) of RAV were quantified. VMI with the highest CNR was predefined as the best-VMI. Two radiologists evaluated four image series (PEI and best-VMI at LAP and PVP) separately for RAV visibility, accessory hepatic vein (AHV) assessability, and image noise using five-point scales (1 = worst, 5 = best). RAV detection and AHV assessability rates were compared among image series. Results: In both enhancement phases, VMI 40–70 showed lower image noise and VMI 40–60 higher CNR than PEI (all p < 0.05), with VMI 40 providing the highest CNR and being designated as the best VMI. Compared to PEI at LAP, CNR was lower with PEI at PVP but higher with VMI 40 at PVP (both, p < 0.001). In subjective analysis, VMI 40 showed better scores than PEI for image noise (LAP: 4.0 ± 0.5 vs. 2.4 ± 0.6; PVP: 4.0 ± 0.6 vs. 2.5 ± 0.5) and RAV visibility (LAP: 4.5 ± 0.8 vs. 3.5 ± 0.8; PVP: 3.7 ± 1.0 vs. 2.6 ± 0.9) (all p < 0.001). With PEI, the RAV detection rate was lower at PVP than at LAP (54.8% vs. 89.7%, p < 0.001). Use of VMI 40 at PVP achieved the same detection rate as PEI at LAP (89.7%, p = 1.0) and higher AHV assessability rate than PEI at PVP (96.8% vs. 80.2%, p < 0.001). Conclusion: DLCT-VMI improved RAV assessments before AVS. Using VMI 40 for PVP provided RAV depiction equivalent to PEI at LAP and superior AHV assessability to PEI at PVP, suggesting its potential as a surrogate for dedicated multiphase CT.
- 18 August 2025
- Published by Springer Nature in Abdominal Radiology
- Vol. 51 (4) , 1974-1985
Open AccessDual-layer spectral detector CT for improved assessment of right adrenal vein in patients with primary aldosteronism
Purpose: To investigate whether virtual-monoenergetic images (VMI) from dual-layer spectral-detector computed tomography (DLCT) improve right adrenal vein (RAV) assessment before adrenal venous sampling (AVS), and portal-venous-phase (PVP) images alone can replace conventional multiphase CT combining late-arterial phase (LAP) and PVP. Methods: Sixty-three patients with primary aldosteronism who underwent LAP and PVP DLCT before AVS were retrospectively analyzed. Conventional polyenergetic-images (PEI) and VMI at 40–70 keV (VMI 40–70 ) were reconstructed. Image noise and contrast-to-noise ratio (CNR) of RAV were quantified. VMI with the highest CNR was predefined as the best-VMI. Two radiologists evaluated four image series (PEI and best-VMI at LAP and PVP) separately for RAV visibility, accessory hepatic vein (AHV) assessability, and image noise using five-point scales (1 = worst, 5 = best). RAV detection and AHV assessability rates were compared among image series. Results: In both enhancement phases, VMI 40–70 showed lower image noise and VMI 40–60 higher CNR than PEI (all p < 0.05), with VMI 40 providing the highest CNR and being designated as the best VMI. Compared to PEI at LAP, CNR was lower with PEI at PVP but higher with VMI 40 at PVP (both, p < 0.001). In subjective analysis, VMI 40 showed better scores than PEI for image noise (LAP: 4.0 ± 0.5 vs. 2.4 ± 0.6; PVP: 4.0 ± 0.6 vs. 2.5 ± 0.5) and RAV visibility (LAP: 4.5 ± 0.8 vs. 3.5 ± 0.8; PVP: 3.7 ± 1.0 vs. 2.6 ± 0.9) (all p < 0.001). With PEI, the RAV detection rate was lower at PVP than at LAP (54.8% vs. 89.7%, p < 0.001). Use of VMI 40 at PVP achieved the same detection rate as PEI at LAP (89.7%, p = 1.0) and higher AHV assessability rate than PEI at PVP (96.8% vs. 80.2%, p < 0.001). Conclusion: DLCT-VMI improved RAV assessments before AVS. Using VMI 40 for PVP provided RAV depiction equivalent to PEI at LAP and superior AHV assessability to PEI at PVP, suggesting its potential as a surrogate for dedicated multiphase CT.