Effect of Deep Learning Reconstruction on Respiratory-triggered T2-weighted MR Imaging of the Liver: A Comparison between the Single-shot Fast Spin-echo and Fast Spin-echo Sequences.

PURPOSE: To compare the effects of deep learning reconstruction (DLR) on respiratory-triggered T2-weighted MRI of the liver between single-shot fast spin-echo (SSFSE) and fast spin-echo (FSE) sequences. METHODS: Respiratory-triggered fat-suppressed liver T2-weighted MRI was obtained with the FSE and SSFSE sequences at the same spatial resolution in 55 patients. Conventional reconstruction (CR) and DLR were applied to each sequence, and the SNR and liver-to-lesion contrast were measured on FSE-CR, FSE-DLR, SSFSE-CR, and SSFSE-DLR images. Image quality was independently assessed by three radiologists. The results of the qualitative and quantitative analyses were compared among the four types of images using repeated-measures analysis of variance or Friedman's test for normally and non-normally distributed data, respectively, and a visual grading characteristics (VGC) analysis was performed to evaluate the image quality improvement by DLR on the FSE and SSFSE sequences. RESULTS: The liver SNR was lowest on SSFSE-CR and highest on FSE-DLR and SSFSE-DLR (P < 0.01). The liver-to-lesion contrast did not differ significantly among the four types of images. Qualitatively, noise scores were worst on SSFSE-CR but best on SSFSE-DLR because DLR significantly reduced noise (P < 0.01). In contrast, artifact scores were worst both on FSE-CR and FSE-DLR (P < 0.01) because DLR did not reduce the artifacts. Lesion conspicuity was significantly improved by DLR compared with CR in the SSFSE (P < 0.01) but not in FSE sequences for all readers. Overall image quality was significantly improved by DLR compared with CR for all readers in the SSFSE (P < 0.01) but only one reader in the FSE (P < 0.01). The mean area under the VGC curve values for the FSE-DLR and SSFSE-DLR sequences were 0.65 and 0.94, respectively. CONCLUSION: In liver T2-weighted MRI, DLR produced more marked improvements in image quality in SSFSE than in FSE.

High-resolution Diffusion-weighted Imaging of the Prostate Using Multiplexed Sensitivity-encoding: Comparison with the Conventional and Reduced Field-of-view Techniques.

PURPOSE: To compare objective and subjective image quality, lesion conspicuity, and apparent diffusion coefficient (ADC) of high-resolution multiplexed sensitivity-encoding diffusion-weighted imaging (MUSE-DWI) with conventional DWI (c-DWI) and reduced FOV DWI (rFOV-DWI) in prostate MRI. METHODS: Forty-seven patients who underwent prostate MRI, including c-DWI, rFOV-DWI, and MUSE-DWI, were retrospectively evaluated. SNR and ADC of normal prostate tissue and contrast-to-noise ratio (CNR) and ADC of prostate cancer (PCa) were measured and compared between the three sequences. Image quality and lesion conspicuity were independently graded by two radiologists using a 5-point scale and compared between the three sequences. RESULTS: The SNR of normal prostate tissue was significantly higher with rFOV-DWI than with the other two DWI techniques (P ≤ 0.01). The CNR of the PCa was significantly higher with rFOV-DWI than with MUSE-DWI (P < 0.05). The ADC of normal prostate tissue measured by rFOV-DWI was lower than that measured by MUSE-DWI and c-DWI (P < 0.01), while there was no difference in the ADC of cancers. In the qualitative analysis, MUSE-DWI showed significantly higher scores than rFOV-DWI and c-DWI for visibility of anatomy and overall image quality in both readers, and significantly higher scores for distortion in one of the two readers (P < 0.001). There was no difference in lesion conspicuity between the three sequences. CONCLUSION: High-resolution MUSE-DWI showed higher image quality and reduced distortion compared to c-DWI, while maintaining a wide FOV and similar ADC quantification, although no difference in lesion conspicuity was observed.

Image Quality and Lesion Detectability of Pancreatic Phase Thin-Slice Computed Tomography Images With a Deep Learning-Based Reconstruction Algorithm.

OBJECTIVE: To evaluate the image quality and lesion detectability of pancreatic phase thin-slice computed tomography (CT) images reconstructed with a deep learning-based reconstruction (DLR) algorithm compared with filtered-back projection (FBP) and hybrid iterative reconstruction (IR) algorithms. METHODS: Fifty-three patients who underwent dynamic contrast-enhanced CT including pancreatic phase were enrolled in this retrospective study. Pancreatic phase thin-slice (0.625 mm) images were reconstructed with each FBP, hybrid IR, and DLR. Objective image quality and signal-to-noise ratio of the pancreatic parenchyma, and contrast-to-noise ratio of pancreatic lesions were compared between the 3 reconstruction algorithms. Two radiologists independently assessed the image quality of all images. The diagnostic performance for the detection of pancreatic lesions was compared among the reconstruction algorithms using jackknife alternative free-response receiver operating characteristic analysis. RESULTS: Deep learning-based reconstruction resulted in significantly lower image noise and higher signal-to-noise ratio and contrast-to-noise ratio than hybrid IR and FBP ( P < 0.001). Deep learning-based reconstruction also yielded significantly higher visual scores than hybrid IR and FBP ( P < 0.01). The diagnostic performance of DLR for detecting pancreatic lesions was highest for both readers, although a significant difference was found only between DLR and FBP in one reader ( P = 0.02). CONCLUSIONS: Deep learning-based reconstruction showed improved objective and subjective image quality of pancreatic phase thin-slice CT relative to other reconstruction algorithms and has potential for improving lesion detectability.

Noninvasive Three-dimensional Assessment of Single Molecular Crystals Using Multiphoton Microscopic Observation and Their Deformation-induced Emission Characteristics.

Distinguishing the luminescence contribution from the surface and bulk of a crystal is a long-standing challenge in crystal materials. Herein, three-dimensional, multiphoton, luminescence microscope imaging of the elastic molecular single crystal 1,4-bis(4-methylthien-2-yl)-2,3,5,6-tetrafluorobenzene, was conducted. Further, the luminescence contribution from the surface and bulk of the crystal was experimentally distinguished. Strong luminescence was observed only from the surface of the crystal, while the bulk did not emit strongly. Furthermore, the surface and bulk luminescence behavior responded well to the mechanical shape change of the crystal; i.e., strong luminescence was observed for the elongated side of the crystal.

Diagnostic accuracy of MRI for evaluating myometrial invasion in endometrial cancer: a comparison of MUSE-DWI, rFOV-DWI, and DCE-MRI.

OBJECTIVES: To compare the image quality of high-resolution diffusion-weighted imaging (DWI) using multiplexed sensitivity encoding (MUSE) versus reduced field-of-view (rFOV) techniques in endometrial cancer (EC) and to compare the diagnostic performance of these techniques with that of dynamic contrast-enhanced (DCE) MRI for assessing myometrial invasion of EC. METHODS: MUSE-DWI and rFOV-DWI were obtained preoperatively in 58 women with EC. Three radiologists assessed the image quality of MUSE-DWI and rFOV-DWI. For 55 women who underwent DCE-MRI, the same radiologists assessed the superficial and deep myometrial invasion using MUSE-DWI, rFOV-DWI, and DCE-MRI. Qualitative scores were compared using the Wilcoxon signed-rank test. Receiver operating characteristic analysis was performed to compare the diagnostic performance. RESULTS: Artifacts, sharpness, lesion conspicuity, and overall quality were significantly better with MUSE-DWI than with rFOV-DWI (p < 0.05). The area under the curve (AUC) of MUSE-DWI, rFOV-DWI, and DCE-MRI for the assessment of myometrial invasion were not significantly different except for significantly higher AUC of MUSE-DWI than that of DCE-MRI for superficial myometrial invasion (0.76 for MUSE-DWI and 0.64 for DCE-MRI, p = 0.049) and for deep myometrial invasion (0.92 for MUSE-DWI and 0.80 for DCE-MRI, p = 0.022) in one observer, and that of rFOV-DWI for deep myometrial invasion in another observer (0.96 for MUSE-DWI and 0.89 for rFOV-MRI, p = 0.048). CONCLUSION: MUSE-DWI exhibits better image quality than rFOV-DWI. MUSE-DWI and rFOV-DWI shows almost equivalent diagnostic performance compared to DCE-MRI for assessing superficial and deep myometrial invasion in EC although MUSE-DWI may be helpful for some radiologists.

Noninvasive Liver Fibrosis Staging: Comparison of MR Elastography with Extracellular Volume Fraction Analysis Using Contrast-Enhanced CT.

Purpose: To compare the accuracy of liver fibrosis staging with MR elastography and of staging with extracellular volume fraction (fECV) analysis using contrast-enhanced CT. Methods: This retrospective study included 60 patients who underwent both MR elastography and contrast-enhanced CT before liver surgery between October 2013 and July 2020. Two radiologists independently measured liver stiffness of MR elastography and fECV of CT images. Accuracy for liver fibrosis staging was assessed using receiver operating characteristic (ROC) analysis. Correlations between liver stiffness or fECV and liver fibrosis were also evaluated by means of the Spearman rank correlation coefficient. Results: The areas under the ROC curves for MR elastography for each stage differentiation of ≥F1 (0.85, 0.82 for the two radiologists), ≥F2 (0.88, 0.89), ≥F3 (0.87, 0.86), and F4 (0.84, 0.83) were greater than those for fECV analysis with CT (0.64, p = 0.06, 0.69, p = 0.2; 0.62, p < 0.005, 0.63, p < 0.005; 0.62, p < 0.005, 0.62, p < 0.01; and 0.70, p = 0.08, 0.71, p = 0.2, respectively). The correlation coefficients between liver stiffness and liver fibrosis in A0 (0.67, 0.69 for the two radiologists), A1 (0.64, 0.66) and A2 group (0.58, 0.51) were significantly higher than those between fECV and liver fibrosis (0.28, 0.30; 0.27, 0.31; and 0.23, 0.07; p < 0.05 for all comparisons). Conclusion: MR elastography allows for more accurate liver fibrosis staging compared with fECV analysis with CT. In addition, MR elastography may be less affected than fECV analysis by the inflammatory condition.

Pancreatic fibrosis by extracellular volume fraction using Contrast-enhanced computed tomography and relationship with pancreatic cancer.

PURPOSE: This study aimed to assess the relationship between pancreatic fibrosis measured by the extracellular volume fraction (ECV) using contrast-enhanced computed tomography (CT) and the histologic pancreatic fibrosis fraction and investigate the relationship between pancreatic fibrosis and pancreatic cancer. METHOD: The study included 88 consecutive patients (48 males, 40 females; median age, 69 years; range, 17-89 years); 47 had pancreatic cancer, and 41 had other diseases. Fifty-two cases were evaluated pathologically for pancreatic fibrosis. The histologic pancreatic fibrosis fraction was quantified using image analysis software in nontumorous pancreatic tissue at the resection stump using 2-µm-thick Azan-stained slides. Two board-certified radiologists measured ECV in the pancreatic parenchyma at an estimated transection line. The correlation between histologic pancreatic fibrosis fraction and ECV was investigated, and whether the ECV value could be used as a biomarker for pancreatic cancer was investigated. RESULTS: The histologic pancreatic fibrosis fraction was significantly correlated with the ECV (r = 0.64, P < 0.01). Pancreatic fibrosis evaluated by ECV was higher in pancreatic cancer patients than in other patients (P < 0.01). On receiver-operating characteristic curve analysis, the ECV had good diagnostic accuracy for the development of pancreatic cancer (cut-off value 32.8%; sensitivity 61.0%, specificity 85.1%). ECV was identified on multivariate analysis as an independent risk factor for pancreatic cancer (odds ratio 1.16; P < 0.01). CONCLUSIONS: Extracellular volume fraction was strongly related to the histologic pancreatic fibrosis fraction, which was independently associated with pancreatic cancer. Thus, extracellular volume fraction is an imaging biomarker that reflects the progression of pancreatic fibrosis and may potentially help predict the development of pancreatic cancer, although further investigation will be needed.

Impact of adaptive image receive coil technology for liver MR imaging at 3.0 Tesla: Intraindividual comparison with use of conventional coil.

PURPOSE: A newly developed Adaptive Image Receive (AIR) coil is designed to be more flexible to conform to the human body habitus, and may improve image quality by reducing the distance between the coil element and the imaging subject. This study evaluated the AIR coil's usefulness for liver MR imaging at 3.0 T in comparison with that of a conventional coil retrospectively. METHOD: The study population comprised 50 consecutive patients, who underwent follow-up liver MR examinations with a 3.0-T MR system using both an AIR coil and a conventional coil to evaluate hepatocellular carcinoma. Three-dimensional fat-suppressed T1-weighted gradient-echo images before and after injection of an MRI contrast agent, T2-weighted single-shot fast spin-echo (SSFSE) images, and diffusion-weighted (DW) images obtained with the AIR coil were compared with corresponding images obtained with the conventional coil. One radiologist measured signal-to-noise ratios (SNRs), while two other radiologists used a 3-point scale to independently assess subjective image noise, artifacts, signal uniformity, and overall image quality. RESULTS: SNRs for the AIR coil were significantly higher than those for the conventional coil (P <.05). Subjective image noise for the AIR coil on pre- and postcontrast T1-weighted and DW images was lower than for the conventional coil for both readers (P <.05). Overall image quality on pre- and postcontrast T1-weighted and DW images for the AIR coil was better than that for the conventional coil for at least one reader (P <.05). CONCLUSIONS: In comparison with the conventional coil, AIR coil improved SNR and image quality of liver MR imaging.

Ultra-high-resolution CT urography: Importance of matrix size and reconstruction technique on image quality.

PURPOSE: To evaluate the image quality of CT urography (CTU) obtained with ultra-high-resolution CT (U-HRCT) reconstructed with hybrid iterative reconstruction (IR) and model-based IR algorithms. METHOD: Forty-eight patients who underwent CTU using the U-HRCT system were enrolled in this retrospective study. Excretory phase images were reconstructed with three protocols: Protocol A: 1024-matrix, 0.25 mm-thickness, and model-based IR; Protocol B: 1024-matrix, 0.25 mm-thickness, and hybrid IR; Protocol C: 512-matrix, 0.5 mm-thickness, and model-based IR. Objective image noise and contrast-to-noise ratio (CNR) of the renal pelvis were compared among the protocols. Three-dimensional maximum intensity projection CTU images were generated from each image data set, and image quality was evaluated by two radiologists. RESULTS: Protocol C yielded the lowest objective image noise and highest CNR, whereas Protocol A had highest image noise and lowest CNR (P < 0.01). Regarding the detailed delineation of urinary tract structures on the images, the mean visual score was significantly higher for Protocol A than for Protocols B and C (P < 0.001), and the mean score for subjective image noise was significantly lower for Protocol A than for Protocols B and C (P < 0.001). CONCLUSIONS: CTU with a 1024-matrix and model-based IR depicted the structures of the urinary system in the most detail.