Deep Learning Reconstruction for DWIs by EPI and FASE Sequences for Head and Neck Tumors.

BACKGROUND: Diffusion-weighted images (DWI) obtained by echo-planar imaging (EPI) are frequently degraded by susceptibility artifacts. It has been suggested that DWI obtained by fast advanced spin-echo (FASE) or reconstructed with deep learning reconstruction (DLR) could be useful for image quality improvements. The purpose of this investigation using in vitro and in vivo studies was to determine the influence of sequence difference and of DLR for DWI on image quality, apparent diffusion coefficient (ADC) evaluation, and differentiation of malignant from benign head and neck tumors. METHODS: For the in vitro study, a DWI phantom was scanned by FASE and EPI sequences and reconstructed with and without DLR. Each ADC within the phantom for each DWI was then assessed and correlated for each measured ADC and standard value by Spearman's rank correlation analysis. For the in vivo study, DWIs obtained by EPI and FASE sequences were also obtained for head and neck tumor patients. Signal-to-noise ratio (SNR) and ADC were then determined based on ROI measurements, while SNR of tumors and ADC were compared between all DWI data sets by means of Tukey's Honest Significant Difference test. RESULTS: For the in vitro study, all correlations between measured ADC and standard reference were significant and excellent (0.92 ≤ ρ ≤ 0.99, p < 0.0001). For the in vivo study, the SNR of FASE with DLR was significantly higher than that of FASE without DLR (p = 0.02), while ADC values for benign and malignant tumors showed significant differences between each sequence with and without DLR (p < 0.05). CONCLUSION: In comparison with EPI sequence, FASE sequence and DLR can improve image quality and distortion of DWIs without significantly influencing ADC measurements or differentiation capability of malignant from benign head and neck tumors.

Deep Learning Reconstruction to Improve the Quality of MR Imaging: Evaluating the Best Sequence for T-category Assessment in Non-small Cell Lung Cancer Patients.

PURPOSE: Deep learning reconstruction (DLR) has been recommended as useful for improving image quality. Moreover, compressed sensing (CS) or DLR has been proposed as useful for improving temporal resolution and image quality on MR sequences in different body fields. However, there have been no reports regarding the utility of DLR for image quality and T-factor assessment improvements on T2-weighted imaging (T2WI), short inversion time (TI) inversion recovery (STIR) imaging, and unenhanced- and contrast-enhanced (CE) 3D fast spoiled gradient echo (GRE) imaging with and without CS in comparison with thin-section multidetector-row CT (MDCT) for non-small cell lung cancer (NSCLC) patients. The purpose of this study was to determine the utility of DLR for improving image quality and the appropriate sequence for T-category assessment for NSCLC patients. METHODS: As subjects for this study, 213 pathologically diagnosed NSCLC patients who underwent thin-section MDCT and MR imaging as well as T-factor diagnosis were retrospectively enrolled. SNR of each tumor was calculated and compared by paired t-test for each sequence with and without DLR. T-factor for each patient was assessed with thin-section MDCT and all MR sequences, and the accuracy for T-factor diagnosis was compared among all sequences and thin-section CT by means of McNemar's test. RESULTS: SNRs of T2WI, STIR imaging, unenhanced thin-section Quick 3D imaging, and CE-thin-section Quick 3D imaging with DLR were significantly higher than SNRs of those without DLR (P < 0.05). Diagnostic accuracy of STIR imaging and CE-thick- or thin-section Quick 3D imaging was significantly higher than that of thin-section CT, T2WI, and unenhanced thick- or thin-section Quick 3D imaging (P < 0.05). CONCLUSION: DLR is thus considered useful for image quality improvement on MR imaging. STIR imaging and CE-Quick 3D imaging with or without CS were validated as appropriate MR sequences for T-factor evaluation in NSCLC patients.

Three-Dimensional Gradient-Echo-Based Amide Proton Transfer-Weighted Imaging of Brain Tumors: Comparison With Two-Dimensional Spin-Echo-Based Amide Proton Transfer-Weighted Imaging.

OBJECTIVE: Although amide proton transfer-weighted (APTw) imaging is reported by 2-dimensional (2D) spin-echo-based sequencing, 3-dimensional (3D) APTw imaging can be obtained by gradient-echo-based sequencing. The purpose of this study was to compare the efficacy of APTw imaging between 2D and 3D imaging in patients with various brain tumors. METHODS: A total of 49 patients who had undergone 53 examinations [5 low-grade gliomas (LGG), 16 high-grade gliomas (HGG), 6 malignant lymphomas, 4 metastases, and 22 meningiomas] underwent APTw imaging using 2D and 3D sequences. The magnetization transfer ratio asymmetry (MTR asym ) was assessed by means of region of interest measurements. Pearson correlation was performed to determine the relationship between MTR asym for the 2 methods, and Student's t test to compare MTR asym for LGG and HGG. The diagnostic accuracy to differentiate HGG from LGG of the 2 methods was compared by means of the McNemar test. RESULTS: Three-dimensional APTw imaging showed a significant correlation with 2D APTw imaging ( r = 0.79, P < 0.0001). The limits of agreement between the 2 methods were -0.021 ± 1.42%. The MTR asym of HGG (2D: 1.97 ± 0.96, 3D: 2.11 ± 0.95) was significantly higher than those of LGG (2D: 0.46 ± 0.89%, P < 0.01; 3D: 0.15 ± 1.09%, P < 0.001). The diagnostic performance of the 2 methods to differentiate HGG from LGG was not significantly different ( P = 1). CONCLUSIONS: The potential capability of 3D APTw imaging is equal to or greater than that of 2D APTw imaging and is considered at least as valuable in patients with brain tumors.

Comparison of ocular morphological measurement by wide-angle echography and magnetic resonance imaging.

Objectives: To compare the eye axial length (AL), equatorial horizontal diameter (HD), and equatorial vertical diameter (VD) of normal eyes using a novel wide-angle, arc-scanning, ultrasound diagnostic device for wide-angle B-mode echography. Methods: In this cross-sectional study, wide-angle B-mode echography and magnetic resonance imaging (MRI) were conducted on 22 normal eyes; the AL, HD, and VD were measured. Results: The mean ALs were as follows: wide-angle B-mode echography, 25.22±1.47 mm and MRI, 25.24±1.46 mm; a significant correlation was observed between the two measurements (ß=0.995 [0.976, 1.013]; p<0.001; 95% R2=1.00). The mean HDs were as follows: wide-angle B-mode echography, 22.33±0.84 mm and MRI, 22.55±0.90 mm; a significant correlation was observed between the two measurements (ß=0.902 [0.750, 1.179]; p<0.001; 95% R2=0.81). The mean VDs were as follows: wide-angle B-mode echography, 22.77±0.91 mm; and MRI, 22.88±0.92 mm; a significant correlation was observed between the two measurements (ß=0.966 [0.853, 1.097]; p<0.001; 95% R2=0.93). Conclusions: There were no significant differences in the measurements for each parameter by wide-angle B-mode echography and MRI. Therefore, wide-angle B-mode echography permits accurate visualization of ocular morphology.

Comparison of utility of deep learning reconstruction on 3D MRCPs obtained with three different k-space data acquisitions in patients with IPMN.

OBJECTIVE: To compare the utility of deep learning reconstruction (DLR) for improving acquisition time, image quality, and intraductal papillary mucinous neoplasm (IPMN) evaluation for 3D MRCP obtained with parallel imaging (PI), multiple k-space data acquisition for each repetition time (TR) technique (Fast 3D mode multiple: Fast 3Dm) and compressed sensing (CS) with PI. MATERIALS AND METHODS: A total of 32 IPMN patients who had undergone 3D MRCPs obtained with PI, Fast 3Dm, and CS with PI and reconstructed with and without DLR were retrospectively included in this study. Acquisition time, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) obtained with all protocols were compared using Tukey's HSD test. Results of endoscopic ultrasound, ERCP, surgery, or pathological examination were determined as standard reference, and distribution classifications were compared among all 3D MRCP protocols by McNemar's test. RESULTS: Acquisition times of Fast 3Dm and CS with PI with and without DLR were significantly shorter than those of PI with and without DLR (p < 0.05). Each MRCP sequence with DLR showed significantly higher SNRs and CNRs than those without DLR (p < 0.05). IPMN distribution accuracy of PI with and without DLR and Fast 3Dm with DLR was significantly higher than that of Fast 3Dm without DLR and CS with PI without DLR (p < 0.05). CONCLUSION: DLR is useful for improving image quality and IPMN evaluation capability on 3D MRCP obtained with PI, Fast 3Dm, or CS with PI. Moreover, Fast 3Dm and CS with PI may play as substitution to PI for MRCP in patients with IPMN. KEY POINTS: • Mean examination times of multiple k-space data acquisitions for each TR and compressed sensing with parallel imaging were significantly shorter than that of parallel imaging (p < 0.0001). • When comparing image quality of 3D MRCPs with and without deep learning reconstruction, deep learning reconstruction significantly improved signal-to-noise ratio and contrast-to-noise ratio (p < 0.05). • IPMN distribution accuracies of parallel imaging with and without deep learning reconstruction (with vs. without: 88.0% vs. 88.0%) and multiple k-space data acquisitions for each TR with deep learning reconstruction (86.0%) were significantly higher than those of others (p < 0.05).

State-of-the-art MR Imaging for Thoracic Diseases.

Since thoracic MR imaging was first used in a clinical setting, it has been suggested that MR imaging has limited clinical utility for thoracic diseases, especially lung diseases, in comparison with x-ray CT and positron emission tomography (PET)/CT. However, in many countries and states and for specific indications, MR imaging has recently become practicable. In addition, recently developed pulmonary MR imaging with ultra-short TE (UTE) and zero TE (ZTE) has enhanced the utility of MR imaging for thoracic diseases in routine clinical practice. Furthermore, MR imaging has been introduced as being capable of assessing pulmonary function. It should be borne in mind, however, that these applications have so far been academically and clinically used only for healthy volunteers, but not for patients with various pulmonary diseases in Japan or other countries. In 2020, the Fleischner Society published a new report, which provides consensus expert opinions regarding appropriate clinical indications of pulmonary MR imaging for not only oncologic but also pulmonary diseases. This review article presents a brief history of MR imaging for thoracic diseases regarding its technical aspects and major clinical indications in Japan 1) in terms of what is currently available, 2) promising but requiring further validation or evaluation, and 3) developments warranting research investigations in preclinical or patient studies. State-of-the-art MR imaging can non-invasively visualize lung structural and functional abnormalities without ionizing radiation and thus provide an alternative to CT. MR imaging is considered as a tool for providing unique information. Moreover, prospective, randomized, and multi-center trials should be conducted to directly compare MR imaging with conventional methods to determine whether the former has equal or superior clinical relevance. The results of these trials together with continued improvements are expected to update or modify recommendations for the use of MRI in near future.

Compressed sensing and parallel imaging accelerated T2 FSE sequence for head and neck MR imaging: Comparison of its utility in routine clinical practice.

PURPOSE: To directly compare the capability of compressed sensing (CS) and parallel imaging (PI) accelerated T2 FSE (Fast Spin Echo) sequence with PI for head and neck MR imaging. METHODS: Thirty consecutive patients with various head and neck diseases (15 men and 15 women, mean age 53 ± 22 years) underwent MR imaging by PI with CS and by PI. Reduction factors were as follows: PI with CS, 3 and PI, 1.5. Examination times for PI with CS and PI were all recorded. For quantitative image quality assessment, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. For qualitative assessment, two investigators assessed overall image quality, artifacts and diagnostic confidence level using a 5-point scoring system, and final scores were determined by consensus of two readers. Mean examination time and all indexes were compared by means of paired t-test and Wilcoxon signed-rank test. Inter-observer agreement for each qualitative index was assessed in terms of kappa statistics. RESULTS: Mean examination time for PI with CS (83.5 ± 11.0 s) was significantly shorter than that for PI (173.0 ± 54.4 s, p < 0.0001). SNR and CNR of PI with CS were significantly better than those with PI (mean SNR; 11.2 ± 3.6 vs 8.9 ± 2.6, median of CNR; 7.4 vs. 6.1, p < 0.0001). All inter-observer agreements were assessed as significant and substantial (0.62 < κ < 0.81). CONCLUSION: PI with CS accelerated T2 weighted sequence performs equally well or even slightly better than its PI accelerated, conventional counterpart at reduced scan times in the context of head and neck MR imaging.

Compressed sensing and deep learning reconstruction for women's pelvic MRI denoising: Utility for improving image quality and examination time in routine clinical practice.

PURPOSE: To demonstrate the utility of compressed sensing with parallel imaging (Compressed SPEEDER) and AiCE compared with that of conventional parallel imaging (SPEEDER) for shortening examination time and improving image quality of women's pelvic MRI. METHOD: Thirty consecutive patients with women's pelvic diseases (mean age 50 years) underwent T2-weighted imaging using Compressed SPEEDER as well as conventional SPEEDER reconstructed with and without AiCE. The examination times were recorded, and signal-to-noise ratio (SNR) was calculated for every patient. Moreover, overall image quality was assessed using a 5-point scoring system, and final scores for all patients were determined by consensus of two readers. Mean examination time, SNR and overall image quality were compared among the four data sets by Wilcoxon signed-rank test. RESULTS: Examination times for Compressed SPEEDER with and without AiCE were significantly shorter than those for conventional SPEEDER with and without AiCE (with AiCE: p < 0.0001, without AiCE: p < 0.0001). SNR of Compressed SPEEDER and of SPEEDER with AiCE was significantly superior to that of Compressed SPEEDER without AiCE (vs. Compressed SPEEDER, p = 0.01; vs. SPEEDER, p = 0.009). Overall image quality of Compressed SPEEDER with AiCE and of SPEEDER with and without AiCE was significantly higher than that of Compressed SPEEDER without AiCE (vs. Compressed SPEEDER with AiCE, p < 0.0001; vs. SPEEDER with AiCE, p < 0.0001; SPEEDER without AiCE, p = 0.0003). CONCLUSION: Image quality and shorten examination time for T2-weighted imaging in women's pelvic MRI can be significantly improved by using Compressed SPEEDER with AiCE in comparison with conventional SPEEDER, although other sequences were not tested.