植田 高弘

PURPOSE: The purpose of this study was to directly compare diagnostic capability of inguinal herniation between upright area-detector CT (ADCT) and conventional supine ADCT under the Valsalva maneuver. MATERIALS AND METHODS: This retrospective study included 209 patients with 360 inguinal herniations and 123 patients without inguinal hernias. All patients underwent supine and upright ADCT for the evaluation of abdominal wall hernias within one week between May 2023 and March 2024. From this cohort, a total of 120 of 360 inguinal hernias and 120 of 304 non-inguinal hernias were computationally selected, and the probability of hernia was visually assessed by two board-certified general and abdominal radiologists with 5-point scales to assess subtypes of herniation. The final score for each hernia was determined as consensus of two investigators. To determine the capability of diagnosis for inguinal herniation in selected lesion groups, diagnostic performance was compared between upright and supine ADCTs using an ROC analysis. Then, sensitivity (SE), specificity (SP), and accuracy (AC) for differentiation of inguinal from non-inguinal hernias were compared between the two methods using McNemar's test. RESULTS: The area under the curve (AUC) of upright ADCT (AUC = 0.96) was significantly larger than that of supine ADCT (AUC = 0.93, p < 0.0001). Sensitivity (SE) and accuracy (AC) of upright ADCT (SE: 87.5 %, AC: 93.8 %) were significantly higher than those of supine ADCT (SE: 73.3 %, p < 0.0001; AC: 86.7 %, p < 0.0001). CONCLUSION: Upright ADCT has better potential for the diagnosis and subtype classification of inguinal herniation than conventional supine ADCT when applied under the Valsalva maneuver.

OBJECTIVES: The purpose of this study was to determine the utility of conjugate gradient reconstruction (CG Recon) and deep learning reconstruction (DLR) for reducing scan time while maintaining the image quality and nodule detection capability on lung MRI with ultrashort TE (UTE-MRI) as compared with grid reconstruction (Grid Recon). MATERIALS AND METHODS: In the in vitro and in vivo studies, the NEMA phantom and 35 patients with pulmonary nodules were scanned by UTE-MRI with original (TEoriginal), 1/2 (UTE1/2), and 1/4 (UTE1/4) spoke numbers obtained by both methods and reconstructed with and without DLR. In this study, the standard protocol was UTEoriginal obtained by Grid Recon without DLR. Then, signal-to-noise ratios (SNR) of the phantom, lung and lesion were assessed. In the in vivo study, overall image quality and nodule detection capability were visually assessed on each UTE-MRI. Quantitative and qualitative indices were then compared between the standard protocol and others. Finally, a receiver operating characteristic (ROC) analysis was performed to compare the standard and other protocols. RESULTS: In in vitro and in vivo studies, all SNRs were significantly different between the standard protocol and each UTE-MRI with CG Recon and DLR (p < 0.05). Overall image quality of the standard protocol differed significantly from that of all UTE1/4s (p < 0.05). The area under the curve of each UTEOriginal obtained by CG Recon was significantly larger than that of the standard protocol (p < 0.05). CONCLUSION: CG Recon and DLR can reduce scan time while maintaining image quality and nodule detection capabilities on lung UTE-MRI. KEY POINTS: Question To determine the utility of conjugate gradient reconstruction (CG Recon) to reduce scan time without nodule detection capability on MRI with ultrashort TE (UTE-MRI). Findings Nodule detection capability was not significantly decreased by CG Recon with or without deep learning reconstruction when reducing scan time from the standard UTE-MRI protocol. Clinical relevance Conjugate gradient reconstruction (CG Recon) and deep learning reconstruction (DLR) have the potential to reduce scan time while maintaining image quality and nodule detection capability in lung MR imaging with ultrashort TE.

OBJECTIVES: To determine the capability of dynamic contrast-enhanced (CE-) perfusion area-detector CT (ADCT) for detecting pathological structural changes in stage I non-small cell lung cancer (NSCLC) patients. MATERIALS AND METHODS: Sixty-three consecutive stage I NSCLC patients with progressive fibrosing interstitial lung disease (PF-ILD) underwent dynamic CE-perfusion ADCT analyzed by dual-input maximum slope (DMS) methods for total, pulmonary arterial and systemic arterial perfusion (TPDMS, PAPDMS and SAPDMS) maps, surgical treatment and pathological examination. Multicentric ROIs were then placed over sites assessed as normal lung, pulmonary emphysema, GGO or reticular pattern without traction bronchiectasis, reticular pattern with traction bronchiectasis and honeycombing in the resected lung. Next, an analysis of variance (ANOVA) followed by Tukey's honest significant difference (HSD) multiple comparison test was performed for a comparison of each of the perfusion parameters for five groups. Finally, discrimination accuracy for evaluation of lung parenchymal change was compared for all indexes and combined methods. RESULTS: PAPDMSs of abnormal lungs were significantly lower than that of normal lungs (p < 0.0001). SAPDMSs of normal or emphysematous lungs were significantly lower than those of others (p < 0.0001). SAPDMS of GGO or reticular pattern without traction bronchiectasis was significantly lower than that for reticular pattern with traction bronchiectasis and honeycombing (p < 0.0001). Discrimination accuracy of combined perfusion index was significantly higher than that of each index (p < 0.0001). CONCLUSION: Dynamic CE-perfusion ADCT is useful for detecting pathological structural changes in stage I NSCLC patients with PF-ILD. KEY POINTS: Question Can dynamic first-pass contrast-enhanced perfusion matrices evaluate parenchymal lung changes and disease severity of parenchymal diseases in stage I non-small cell lung cancer (NSCLC) patients? Findings Perfusion indexes differentiated significantly among normal lung, emphysema, GGO or reticular pattern without traction bronchiectasis, reticular pattern with traction bronchiectasis and honeycombing and significantly improved discrimination accuracy by combined methods. Clinical relevance Dynamic first-pass contrast-enhanced perfusion area-detector CT has the potential to assess underlying pathologies and pulmonary functional changes in stage I non-small cell carcinoma patients with progressive fibrosing interstitial lung disease.

OBJECTIVE: To directly compare coronary arterial stenosis evaluations by hybrid-type iterative reconstruction (IR), model-based IR (MBIR), deep learning reconstruction (DLR), and high-resolution deep learning reconstruction (HR-DLR) on coronary computed tomography angiography (CCTA) in both in vitro and in vivo studies. MATERIALS AND METHODS: For the in vitro study, a total of three-vessel tube phantoms with diameters of 3 mm, 4 mm, and 5 mm and with simulated non-calcified stepped stenosis plaques with degrees of 0%, 25%, 50%, and 75% stenosis were scanned with area-detector CT (ADCT) and ultra-high-resolution CT (UHR-CT). Then, ADCT data were reconstructed using all methods, although UHR-CT data were reconstructed with hybrid-type IR, MBIR, and DLR. For the in vivo study, patients who had undergone CCTA at ADCT were retrospectively selected, and each CCTA data set was reconstructed with all methods. To compare the image noise and measurement accuracy at each of the stenosis levels, image noise, and inner diameter were evaluated and statistically compared. To determine the effect of HR-DLR on CAD-RADS evaluation accuracy, the accuracy of CAD-RADS categorization of all CCTAs was compared by using McNemar's test. RESULTS: The image noise of HR-DLR was significantly lower than that of others on ADCT and UHR-CT (p < 0.0001). At a 50% and 75% stenosis level for each phantom, hybrid-type IR showed a significantly larger mean difference on ADCT than did others (p < 0.05). At in vivo study, 31 patients were included. Accuracy on HR-DLR was significantly higher than that on hybrid-type IR, MBIR, or DLR (p < 0.0001). CONCLUSION: HR-DLR is potentially superior for coronary arterial stenosis evaluations to hybrid-type IR, MBIR, or DLR shown on CCTA. KEY POINTS: Question How do coronary arterial stenosis evaluations by hybrid-type IR, MBIR, DLR, and HR-DLR compare to coronary CT angiography? Findings HR-DLR showed significantly lower image noise and more accurate coronary artery disease reporting and data system (CAD-RADS) evaluation than others. Clinical relevance HR-DLR is potentially superior to other reconstruction methods for coronary arterial stenosis evaluations, as demonstrated by coronary CT angiography results on ADCT and as shown in both in vitro and in vivo studies.