Masahiko Nomura

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.