1
|
Kirita M, Himoto Y, Kurata Y, Kido A, Fujimoto K, Abe H, Matsumoto Y, Harada K, Morita S, Yamaguchi K, Nickel D, Mandai M, Nakamoto Y. Feasibility/clinical utility of half-Fourier single-shot turbo spin echo imaging combined with deep learning reconstruction in gynecologic magnetic resonance imaging. Abdom Radiol (NY) 2024:10.1007/s00261-024-04739-1. [PMID: 39692759 DOI: 10.1007/s00261-024-04739-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2024] [Revised: 11/26/2024] [Accepted: 11/30/2024] [Indexed: 12/19/2024]
Abstract
BACKGROUND When antispasmodics are unavailable, the periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER; called BLADE by Siemens Healthineers) or half Fourier single-shot turbo spin echo (HASTE) is clinically used in gynecologic MRI. However, their imaging qualities are limited compared to Turbo Spin Echo (TSE) with antispasmodics. Even with antispasmodics, TSE can be artifact-affected, necessitating a rapid backup sequence. PURPOSE This study aimed to investigate the utility of HASTE with deep learning reconstruction and variable flip angle evolution (iHASTE) compared to conventional sequences with and without antispasmodics. MATERIALS AND METHODS This retrospective study included MRI scans without antispasmodics for 79 patients who underwent iHASTE, HASTE, and BLADE and MRI scans with antispasmodics for 79 case-control matched patients who underwent TSE. Three radiologists qualitatively evaluated image quality, robustness to artifacts, tissue contrast, and uterine lesion margins. Tissue contrast was also quantitatively evaluated. RESULTS Quantitative evaluations revealed that iHASTE exhibited significantly superior tissue contrast to HASTE and BLADE. Qualitative evaluations indicated that iHASTE outperformed HASTE in overall quality. Two of three radiologists judged iHASTE to be significantly superior to BLADE, while two of three judged TSE to be significantly superior to iHASTE. iHASTE demonstrated greater robustness to artifacts than both BLADE and TSE. Lesion margins in iHASTE had lower scores than BLADE and TSE. CONCLUSION iHASTE is a viable clinical option in patients undergoing gynecologic MRI with anti-spasmodics. iHASTE may also be considered as a useful add-on sequence in patients undergoing MRI with antispasmodics.
Collapse
Affiliation(s)
| | | | | | - Aki Kido
- University of Toyama, Toyama, Japan
| | | | | | | | | | | | | | | | | | | |
Collapse
|
2
|
Tanabe M, Higashi M, Miyoshi K, Morooka R, Kiyoyama H, Ihara K, Kawano Y, Yamane M, Yamaguchi T, Ito K. Breath-hold diffusion-weighted MR imaging (DWI) using deep learning reconstruction: Comparison with navigator triggered DWI in patients with malignant liver tumors. Radiography (Lond) 2024; 31:275-280. [PMID: 39667265 DOI: 10.1016/j.radi.2024.11.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2024] [Revised: 11/27/2024] [Accepted: 11/30/2024] [Indexed: 12/14/2024]
Abstract
INTRODUCTION This study investigated the feasibility of single breath-hold (BH) diffusion-weighted MR imaging (DWI) using deep learning reconstruction (DLR) compared to navigator triggered (NT) DWI in patients with malignant liver tumors. METHODS This study included 91 patients who underwent both BH-DWI and NT-DWI with 3T MR system. Abdominal MR images were subjectively analyzed to compare visualization of liver edges, presence of ghosting artifacts, conspicuity of malignant liver tumors, and overall image quality. Then, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC) values of malignant liver tumors were objectively measured using regions of interest. RESULTS Image quality except conspicuity of malignant liver tumors were significantly better in BH-DW image than in NT-DW image (p < 0.01). Regarding the conspicuity of malignant liver tumors, there was no statistically significant difference between BH-DWI and NT-DWI (p = 0.67). The conspicuity score of 1 or 2 was rendered in 19 (21 %) patients in NT-DWI group. Conversely, BH-DWI showed a score of 3 or 4 in 11 (58 %) of these 19 patients. The SNR was significantly higher in BH-DWI than in NT-DWI (29.5 ± 14.0 vs. 27.3 ± 14.7, p < 0.047). No significant difference was observed between CNR and ADC values of malignant liver tumors between BH-DWI and NT-DWI (5.67 ± 3.57 vs. 5.78 ± 3.08, p = 0.243; 997.2 ± 207.0 vs. 1021.0 ± 253.1, p = 0.547). CONCLUSION The BH-DWI using DLR is feasible for liver MRI by improving the SNR and overall image quality, and may play a complementary role to NT-DWI by improving the conspicuity of malignant liver tumor in patients with image distortion in NT-DWI. IMPLICATIONS FOR PRACTICE BH-DWI with DLR would be a preferred approach to achieving sufficient image quality in patients with an irregular triggering pattern, as an alternative to NT-DWI. A further reduction in BH duration (<15 s) should be achieved, taking into account patient tolerance.
Collapse
Affiliation(s)
- M Tanabe
- Department of Radiology, Yamaguchi University Graduate School of Medicine, 1-1-1 Minami-Kogushi, Ube, Yamaguchi 755-8505, Japan.
| | - M Higashi
- Department of Radiology, Yamaguchi University Graduate School of Medicine, 1-1-1 Minami-Kogushi, Ube, Yamaguchi 755-8505, Japan
| | - K Miyoshi
- Department of Radiology, Yamaguchi University Graduate School of Medicine, 1-1-1 Minami-Kogushi, Ube, Yamaguchi 755-8505, Japan
| | - R Morooka
- Department of Radiology, Yamaguchi University Graduate School of Medicine, 1-1-1 Minami-Kogushi, Ube, Yamaguchi 755-8505, Japan
| | - H Kiyoyama
- Department of Radiology, Yamaguchi University Graduate School of Medicine, 1-1-1 Minami-Kogushi, Ube, Yamaguchi 755-8505, Japan
| | - K Ihara
- Department of Radiology, Yamaguchi University Graduate School of Medicine, 1-1-1 Minami-Kogushi, Ube, Yamaguchi 755-8505, Japan
| | - Y Kawano
- Department of Radiology, Yamaguchi University Graduate School of Medicine, 1-1-1 Minami-Kogushi, Ube, Yamaguchi 755-8505, Japan
| | - M Yamane
- Department of Radiological Technology, Yamaguchi University Hospital, Japan
| | - T Yamaguchi
- Department of Radiological Technology, Yamaguchi University Hospital, Japan
| | - K Ito
- Department of Radiology, Yamaguchi University Graduate School of Medicine, 1-1-1 Minami-Kogushi, Ube, Yamaguchi 755-8505, Japan
| |
Collapse
|
3
|
Chaika M, Brendel JM, Ursprung S, Herrmann J, Gassenmaier S, Brendlin A, Werner S, Nickel MD, Nikolaou K, Afat S, Almansour H. Deep Learning Reconstruction of Prospectively Accelerated MRI of the Pancreas: Clinical Evaluation of Shortened Breath-Hold Examinations With Dixon Fat Suppression. Invest Radiol 2024:00004424-990000000-00235. [PMID: 39043213 DOI: 10.1097/rli.0000000000001110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/25/2024]
Abstract
OBJECTIVE Deep learning (DL)-enabled magnetic resonance imaging (MRI) reconstructions can enable shortening of breath-hold examinations and improve image quality by reducing motion artifacts. Prospective studies with DL reconstructions of accelerated MRI of the upper abdomen in the context of pancreatic pathologies are lacking. In a clinical setting, the purpose of this study is to investigate the performance of a novel DL-based reconstruction algorithm in T1-weighted volumetric interpolated breath-hold examinations with partial Fourier sampling and Dixon fat suppression (hereafter, VIBE-DixonDL). The objective is to analyze its impact on acquisition time, image sharpness and quality, diagnostic confidence, pancreatic lesion conspicuity, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). METHODS This prospective single-center study included participants with various pancreatic pathologies who gave written consent from January 2023 to September 2023. During the same session, each participant underwent 2 MRI acquisitions using a 1.5 T scanner: conventional precontrast and postcontrast T1-weighted VIBE acquisitions with Dixon fat suppression (VIBE-Dixon, reference standard) using 4-fold parallel imaging acceleration and 6-fold accelerated VIBE-Dixon acquisitions with partial Fourier sampling utilizing a novel DL reconstruction tailored to the acquisition. A qualitative image analysis was performed by 4 readers. Acquisition time, image sharpness, overall image quality, image noise and artifacts, diagnostic confidence, as well as pancreatic lesion conspicuity and size were compared. Furthermore, a quantitative analysis of SNR and CNR was performed. RESULTS Thirty-two participants were evaluated (mean age ± SD, 62 ± 19 years; 20 men). The VIBE-DixonDL method enabled up to 52% reduction in average breath-hold time (7 seconds for VIBE-DixonDL vs 15 seconds for VIBE-Dixon, P < 0.001). A significant improvement of image sharpness, overall image quality, diagnostic confidence, and pancreatic lesion conspicuity was observed in the images recorded using VIBE-DixonDL (P < 0.001). Furthermore, a significant reduction of image noise and motion artifacts was noted in the images recorded using the VIBE-DixonDL technique (P < 0.001). In addition, for all readers, there was no evidence of a difference in lesion size measurement between VIBE-Dixon and VIBE-DixonDL. Interreader agreement between VIBE-Dixon and VIBE-DixonDL regarding lesion size was excellent (intraclass correlation coefficient, >90). Finally, a statistically significant increase of pancreatic SNR in VIBE-DIXONDL was observed in both the precontrast (P = 0.025) and postcontrast images (P < 0.001). Also, an increase of splenic SNR in VIBE-DIXONDL was observed in both the precontrast and postcontrast images, but only reaching statistical significance in the postcontrast images (P = 0.34 and P = 0.003, respectively). Similarly, an increase of pancreas CNR in VIBE-DIXONDL was observed in both the precontrast and postcontrast images, but only reaching statistical significance in the postcontrast images (P = 0.557 and P = 0.026, respectively). CONCLUSIONS The prospectively accelerated, DL-enhanced VIBE with Dixon fat suppression was clinically feasible. It enabled a 52% reduction in breath-hold time and provided superior image quality, diagnostic confidence, and pancreatic lesion conspicuity. This technique might be especially useful for patients with limited breath-hold capacity.
Collapse
Affiliation(s)
- Marianna Chaika
- From the Department of Diagnostic and Interventional Radiology, Eberhard Karls University, Tübingen University Hospital, Tübingen, Germany (M.C., J.M.B., S.U., J.H., S.G., A.B., S.W., K.N., S.A., H.A.); MR Application Predevelopment, Siemens Healthineers AG, Forchheim, Germany (M.D.N.); and Cluster of Excellence iFIT (EXC 2180) "Image Guided and Functionally Instructed Tumor, Therapies," University of Tübingen, Tübingen, Germany (K.N.)
| | | | | | | | | | | | | | | | | | | | | |
Collapse
|
4
|
Shimada R, Sofue K, Ueno Y, Wakayama T, Yamaguchi T, Ueshima E, Kusaka A, Hori M, Murakami T. Utility of Thin-slice Fat-suppressed Single-shot T2-weighted MR Imaging with Deep Learning Image Reconstruction as a Protocol for Evaluating the Pancreas. Magn Reson Med Sci 2024:mp.2024-0017. [PMID: 38910138 DOI: 10.2463/mrms.mp.2024-0017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/25/2024] Open
Abstract
PURPOSE To compare the utility of thin-slice fat-suppressed single-shot T2-weighted imaging (T2WI) with deep learning image reconstruction (DLIR) and conventional fast spin-echo T2WI with DLIR for evaluating pancreatic protocol. METHODS This retrospective study included 42 patients (mean age, 70.2 years) with pancreatic cancer who underwent gadoxetic acid-enhanced MRI. Three fat-suppressed T2WI, including conventional fast-spin echo with 6 mm thickness (FSE 6 mm), single-shot fast-spin echo with 6 mm and 3 mm thickness (SSFSE 6 mm and SSFSE 3 mm), were acquired for each patient. For quantitative analysis, the SNRs of the upper abdominal organs were calculated between images with and without DLIR. The pancreas-to-lesion contrast on DLIR images was also calculated. For qualitative analysis, two abdominal radiologists independently scored the image quality on a 5-point scale in the FSE 6 mm, SSFSE 6 mm, and SSFSE 3 mm with DLIR. RESULTS The SNRs significantly improved among the three T2-weighted images with DLIR compared to those without DLIR in all patients (P < 0.001). The pancreas-to-lesion contrast of SSFSE 3 mm was higher than those of the FSE 6 mm (P < 0.001) and tended to be higher than SSFSE 6 mm (P = 0.07). SSFSE 3 mm had the highest image qualities regarding pancreas edge sharpness, pancreatic duct clarity, and overall image quality, followed by SSFSE 6 mm and FSE 6 mm (P < 0.0001). CONCLUSION SSFSE 3 mm with DLIR demonstrated significant improvements in SNRs of the pancreas, pancreas-to-lesion contrast, and image quality more efficiently than did SSFSE 6 mm and FSE 6 mm. Thin-slice fat-suppressed single-shot T2WI with DLIR can be easily implemented for pancreatic MR protocol.
Collapse
Affiliation(s)
- Ryuji Shimada
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
- Center for Radiology and Radiation Oncology, Kobe University Hospital, Kobe, Hyogo, Japan
| | - Keitaro Sofue
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
| | - Yoshiko Ueno
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
| | - Tetsuya Wakayama
- MR Collaborations and Development, GE Healthcare, Hino, Tokyo, Japan
| | - Takeru Yamaguchi
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
| | - Eisuke Ueshima
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
| | - Akiko Kusaka
- Center for Radiology and Radiation Oncology, Kobe University Hospital, Kobe, Hyogo, Japan
| | - Masatoshi Hori
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
| | - Takamichi Murakami
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
| |
Collapse
|
5
|
Mayer P, Hausen A, Steinle V, Bergmann F, Kauczor HU, Loos M, Roth W, Klauss M, Gaida MM. The radiomorphological appearance of the invasive margin in pancreatic cancer is associated with tumor budding. Langenbecks Arch Surg 2024; 409:167. [PMID: 38809279 PMCID: PMC11136832 DOI: 10.1007/s00423-024-03355-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Accepted: 05/16/2024] [Indexed: 05/30/2024]
Abstract
PURPOSE Pancreatic cancer (PDAC) is characterized by infiltrative, spiculated tumor growth into the surrounding non-neoplastic tissue. Clinically, its diagnosis is often established by magnetic resonance imaging (MRI). At the invasive margin, tumor buds can be detected by histology, an established marker associated with poor prognosis in different types of tumors. METHODS We analyzed PDAC by determining the degree of tumor spiculation on T2-weighted MRI using a 3-tier grading system. The grade of spiculation was correlated with the density of tumor buds quantified in histological sections of the respective surgical specimen according to the guidelines of the International Tumor Budding Consensus Conference (n = 28 patients). RESULTS 64% of tumors revealed intermediate to high spiculation on MRI. In over 90% of cases, tumor buds were detected. We observed a significant positive rank correlation between the grade of radiological tumor spiculation and the histopathological number of tumor buds (rs = 0.745, p < 0.001). The number of tumor buds was not significantly associated with tumor stage, presence of lymph node metastases, or histopathological grading (p ≥ 0.352). CONCLUSION Our study identifies a readily available radiological marker for non-invasive estimation of tumor budding, as a correlate for infiltrative tumor growth. This finding could help to identify PDAC patients who might benefit from more extensive peripancreatic soft tissue resection during surgery or stratify patients for personalized therapy concepts.
Collapse
Affiliation(s)
- Philipp Mayer
- Clinic for Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, 69120, Germany.
| | - Anne Hausen
- Institute of Pathology, University Medical Center Mainz, JGU-Mainz, Mainz, 55131, Germany.
| | - Verena Steinle
- Clinic for Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, 69120, Germany
| | - Frank Bergmann
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, 69120, Germany
- Clinical Pathology, Klinikum Darmstadt GmbH, Darmstadt, 64283, Germany
| | - Hans-Ulrich Kauczor
- Clinic for Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, 69120, Germany
| | - Martin Loos
- Department of General, Visceral, and Transplantation Surgery, University Hospital Heidelberg, Heidelberg, 69120, Germany
| | - Wilfried Roth
- Institute of Pathology, University Medical Center Mainz, JGU-Mainz, Mainz, 55131, Germany
| | - Miriam Klauss
- Clinic for Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, 69120, Germany
| | - Matthias M Gaida
- Institute of Pathology, University Medical Center Mainz, JGU-Mainz, Mainz, 55131, Germany
- Translational Oncology, TRON, the University Medical Center, JGU-Mainz, Mainz, 55131, Germany
- Research Center for Immunotherapy, University Medical Center Mainz, JGU-Mainz, Mainz, 55131, Germany
| |
Collapse
|
6
|
Kiso K, Tsuboyama T, Onishi H, Ogawa K, Nakamoto A, Tatsumi M, Ota T, Fukui H, Yano K, Honda T, Kakemoto S, Koyama Y, Tarewaki H, Tomiyama N. 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. Magn Reson Med Sci 2024; 23:214-224. [PMID: 36990740 PMCID: PMC11024712 DOI: 10.2463/mrms.mp.2022-0111] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 03/07/2023] [Indexed: 03/30/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Kengo Kiso
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Takahiro Tsuboyama
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Hiromitsu Onishi
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Kazuya Ogawa
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Atsushi Nakamoto
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Mitsuaki Tatsumi
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Takashi Ota
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Hideyuki Fukui
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Keigo Yano
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Toru Honda
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Shinji Kakemoto
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Yoshihiro Koyama
- Department of Radiology, Osaka University Hospital, Suita, Osaka, Japan
| | - Hiroyuki Tarewaki
- Department of Radiology, Osaka University Hospital, Suita, Osaka, Japan
| | - Noriyuki Tomiyama
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| |
Collapse
|
7
|
Liu K, Li Q, Wang X, Fu C, Sun H, Chen C, Zeng M. Feasibility of deep learning-reconstructed thin-slice single-breath-hold HASTE for detecting pancreatic lesions: A comparison with two conventional T2-weighted imaging sequences. RESEARCH IN DIAGNOSTIC AND INTERVENTIONAL IMAGING 2024; 9:100038. [PMID: 39076579 PMCID: PMC11265199 DOI: 10.1016/j.redii.2023.100038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Accepted: 12/26/2023] [Indexed: 07/31/2024]
Abstract
Objective The objective of this study was to evaluate the clinical feasibility of deep learning reconstruction-accelerated thin-slice single-breath-hold half-Fourier single-shot turbo spin echo imaging (HASTEDL) for detecting pancreatic lesions, in comparison with two conventional T2-weighted imaging sequences: compressed-sensing HASTE (HASTECS) and BLADE. Methods From March 2022 to January 2023, a total of 63 patients with suspected pancreatic-related disease underwent the HASTEDL, HASTECS, and BLADE sequences were enrolled in this retrospectively study. The acquisition time, the pancreatic lesion conspicuity (LCP), respiratory motion artifact (RMA), main pancreatic duct conspicuity (MPDC), overall image quality (OIQ), signal-to-noise ratio (SNR), and contrast-noise-ratio (CNR) of the pancreatic lesions were compared among the three sequences by two readers. Results The acquisition time of both HASTEDL and HASTECS was 16 s, which was significantly shorter than that of 102 s for BLADE. In terms of qualitative parameters, Reader 1 and Reader 2 assigned significantly higher scores to the LCP, RMA, MPDC, and OIQ for HASTEDL compared to HASTECS and BLADE sequences; As for the quantitative parameters, the SNR values of the pancreatic head, body, tail, and lesions, the CNR of the pancreatic lesion measured by the two readers were also significantly higher for HASTEDL than for HASTECS and BLADE sequences. Conclusions Compared to conventional T2WI sequences (HASTECS and BLADE), deep-learning reconstructed HASTE enables thin slice and single-breath-hold acquisition with clinical acceptable image quality for detection of pancreatic lesions.
Collapse
Affiliation(s)
- Kai Liu
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Shanghai 200032, China
| | - Qing Li
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Shanghai 200032, China
| | - Xingxing Wang
- Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Caixia Fu
- Siemens (Shenzhen) Magnetic Resonance Ltd., Shenzhen, China
| | - Haitao Sun
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Shanghai 200032, China
| | - Caizhong Chen
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Shanghai 200032, China
| | - Mengsu Zeng
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Shanghai 200032, China
| |
Collapse
|
8
|
Herrmann J, Benkert T, Brendlin A, Gassenmaier S, Hölldobler T, Maennlin S, Almansour H, Lingg A, Weiland E, Afat S. Shortening Acquisition Time and Improving Image Quality for Pelvic MRI Using Deep Learning Reconstruction for Diffusion-Weighted Imaging at 1.5 T. Acad Radiol 2024; 31:921-928. [PMID: 37500416 DOI: 10.1016/j.acra.2023.06.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 06/28/2023] [Accepted: 06/30/2023] [Indexed: 07/29/2023]
Abstract
RATIONALE AND OBJECTIVES To determine the impact on acquisition time reduction and image quality of a deep learning (DL) reconstruction for accelerated diffusion-weighted imaging (DWI) of the pelvis at 1.5 T compared to standard DWI. MATERIALS AND METHODS A total of 55 patients (mean age, 61 ± 13 years; range, 27-89; 20 men, 35 women) were consecutively included in this retrospective, monocentric study between February and November 2022. Inclusion criteria were (1) standard DWI (DWIS) in clinically indicated magnetic resonance imaging (MRI) at 1.5 T and (2) DL-reconstructed DWI (DWIDL). All patients were examined using the institution's standard MRI protocol according to their diagnosis including DWI with two different b-values (0 and 800 s/mm2) and calculation of apparent diffusion coefficient (ADC) maps. Image quality was qualitatively assessed by four radiologists using a visual 5-point Likert scale (5 = best) for the following criteria: overall image quality, noise level, extent of artifacts, sharpness, and diagnostic confidence. The qualitative scores for DWIS and DWIDL were compared with the Wilcoxon signed-rank test. RESULTS The overall image quality was evaluated to be significantly superior in DWIDL compared to DWIS for b = 0 s/mm2, b = 800 s/mm2, and ADC maps by all readers (P < .05). The extent of noise was evaluated to be significantly less in DWIDL compared to DWIS for b = 0 s/mm2, b = 800 s/mm2, and ADC maps by all readers (P < .001). No significant differences were found regarding artifacts, lesion detectability, sharpness of organs, and diagnostic confidence (P > .05). Acquisition time for DWIS was 2:06 minutes, and simulated acquisition time for DWIDL was 1:12 minutes. CONCLUSION DL image reconstruction improves image quality, and simulation results suggest that a reduction in acquisition time for diffusion-weighted MRI of the pelvis at 1.5 T is possible.
Collapse
Affiliation(s)
- Judith Herrmann
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, 72076 Tuebingen, Germany
| | - Thomas Benkert
- MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Andreas Brendlin
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, 72076 Tuebingen, Germany
| | - Sebastian Gassenmaier
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, 72076 Tuebingen, Germany
| | - Thomas Hölldobler
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, 72076 Tuebingen, Germany
| | - Simon Maennlin
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, 72076 Tuebingen, Germany
| | - Haidara Almansour
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, 72076 Tuebingen, Germany
| | - Andreas Lingg
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, 72076 Tuebingen, Germany
| | - Elisabeth Weiland
- MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Saif Afat
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, 72076 Tuebingen, Germany.
| |
Collapse
|
9
|
Lee EJ, Hwang J, Park S, Bae SH, Lim J, Chang YW, Hong SS, Oh E, Nam BD, Jeong J, Sung JK, Nickel D. Utility of accelerated T2-weighted turbo spin-echo imaging with deep learning reconstruction in female pelvic MRI: a multi-reader study. Eur Radiol 2023; 33:7697-7706. [PMID: 37314472 DOI: 10.1007/s00330-023-09781-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 03/24/2023] [Accepted: 03/28/2023] [Indexed: 06/15/2023]
Abstract
OBJECTIVES To determine the clinical feasibility of T2-weighted turbo spin-echo (T2-TSE) imaging with deep learning reconstruction (DLR) in female pelvic MRI compared with conventional T2 TSE in terms of image quality and scan time. METHODS Between May 2021 and September 2021, 52 women (mean age, 44 years ± 12) who underwent 3-T pelvic MRI with additional T2-TSE using a DLR algorithm were included in this single-center prospective study with patient's informed consents. Conventional, DLR, and DLR T2-TSE images with reduced scan times were independently assessed and compared by four radiologists. The overall image quality, differentiation of anatomic details, lesion conspicuity, and artifacts were evaluated using a 5-point scale. Inter-observer agreement of the qualitative scores was compared and reader protocol preferences were then evaluated. RESULTS In the qualitative analysis of all readers, fast DLR T2-TSE showed significantly better overall image quality, differentiation of anatomic regions, lesion conspicuity, and lesser artifacts than conventional T2-TSE and DLR T2-TSE, despite approximately 50% reduction in scan time (all p < 0.05). The inter-reader agreement for the qualitative analysis was moderate to good. All readers preferred DLR over conventional T2-TSE regardless of scan time and preferred fast DLR T2-TSE (57.7-78.8%), except for one who preferred DLR over fast DLR T2-TSE (53.8% vs. 46.1%). CONCLUSION In female pelvic MRI, image quality and accelerated image acquisition for T2-TSE can be significantly improved by using DLR compared to conventional T2-TSE. Fast DLR T2-TSE was non-inferior to DLR T2-TSE in terms of reader preference and image quality. CLINICAL RELEVANCE STATEMENT DLR of T2-TSE in female pelvic MRI enables fast imaging along with maintaining optimal image quality compared with parallel imaging-based conventional T2-TSE. KEY POINTS • Conventional T2 turbo spin-echo based on parallel imaging has limitations for accelerated image acquisition while maintaining good image quality. • Deep learning image reconstruction showed better image quality in both images obtained using the same or accelerated image acquisition parameters compared with conventional T2 turbo spin-echo in female pelvic MRI. • Deep learning image reconstruction enables accelerated image acquisition while maintaining good image quality in the T2-TSE of female pelvic MRI.
Collapse
Affiliation(s)
- Eun Ji Lee
- Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, 59 Daesakwan-Ro, Yongsan-Ku, Seoul, 04401, Korea
| | - Jiyoung Hwang
- Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, 59 Daesakwan-Ro, Yongsan-Ku, Seoul, 04401, Korea.
| | - Suyeon Park
- Department of Biostatistics, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, Korea
| | - Sung Hwan Bae
- Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, 59 Daesakwan-Ro, Yongsan-Ku, Seoul, 04401, Korea
| | - Jiyun Lim
- Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, 59 Daesakwan-Ro, Yongsan-Ku, Seoul, 04401, Korea
| | - Yun-Woo Chang
- Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, 59 Daesakwan-Ro, Yongsan-Ku, Seoul, 04401, Korea
| | - Seong Sook Hong
- Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, 59 Daesakwan-Ro, Yongsan-Ku, Seoul, 04401, Korea
| | - Eunsun Oh
- Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, 59 Daesakwan-Ro, Yongsan-Ku, Seoul, 04401, Korea
| | - Bo Da Nam
- Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, 59 Daesakwan-Ro, Yongsan-Ku, Seoul, 04401, Korea
| | - Jewon Jeong
- Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, 59 Daesakwan-Ro, Yongsan-Ku, Seoul, 04401, Korea
| | | | - Dominik Nickel
- MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| |
Collapse
|
10
|
Wary P, Hossu G, Ambarki K, Nickel D, Arberet S, Oster J, Orry X, Laurent V. Deep learning HASTE sequence compared with T2-weighted BLADE sequence for liver MRI at 3 Tesla: a qualitative and quantitative prospective study. Eur Radiol 2023; 33:6817-6827. [PMID: 37188883 DOI: 10.1007/s00330-023-09693-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Revised: 02/26/2023] [Accepted: 03/11/2023] [Indexed: 05/17/2023]
Abstract
OBJECTIVES To qualitatively and quantitatively compare a single breath-hold fast half-Fourier single-shot turbo spin echo sequence with deep learning reconstruction (DL HASTE) with T2-weighted BLADE sequence for liver MRI at 3 T. METHODS From December 2020 to January 2021, patients with liver MRI were prospectively included. For qualitative analysis, sequence quality, presence of artifacts, conspicuity, and presumed nature of the smallest lesion were assessed using the chi-squared and McNemar tests. For quantitative analysis, number of liver lesions, size of the smallest lesion, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) in both sequences were assessed using the paired Wilcoxon signed-rank test. Intraclass correlation coefficients (ICCs) and kappa coefficients were used to assess agreement between the two readers. RESULTS One hundred and twelve patients were evaluated. Overall image quality (p = .006), artifacts (p < .001), and conspicuity of the smallest lesion (p = .001) were significantly better for the DL HASTE sequence than for the T2-weighted BLADE sequence. Significantly more liver lesions were detected with the DL HASTE sequence (356 lesions) than with the T2-weighted BLADE sequence (320 lesions; p < .001). CNR was significantly higher for the DL HASTE sequence (p < .001). SNR was higher for the T2-weighted BLADE sequence (p < .001). Interreader agreement was moderate to excellent depending on the sequence. Of the 41 supernumerary lesions visible only on the DL HASTE sequence, 38 (93%) were true-positives. CONCLUSION The DL HASTE sequence can be used to improve image quality and contrast and reduces artifacts, allowing the detection of more liver lesions than with the T2-weighted BLADE sequence. CLINICAL RELEVANCE STATEMENT The DL HASTE sequence is superior to the T2-weighted BLADE sequence for the detection of focal liver lesions and can be used in daily practice as a standard sequence. KEY POINTS • The half-Fourier acquisition single-shot turbo spin echo sequence with deep learning reconstruction (DL HASTE sequence) has better overall image quality, reduced artifacts (particularly motion artifacts), and improved contrast, allowing the detection of more liver lesions than with the T2-weighted BLADE sequence. • The acquisition time of the DL HASTE sequence is at least eight times faster (21 s) than that of the T2-weighted BLADE sequence (3-5 min). • The DL HASTE sequence could replace the conventional T2-weighted BLADE sequence to meet the growing indication for hepatic MRI in clinical practice, given its diagnostic and time-saving performance.
Collapse
Affiliation(s)
- Pierre Wary
- Department of Adult Radiology, CHRU de Nancy, 5 Rue du Morvan, 54500, Vandoeuvre-lès-Nancy, France.
| | - Gabriela Hossu
- Clinical Investigation Center Technological Innovation of Nancy, Inserm, CHRU de Nancy, Vandoeuvre-lès-Nancy, France
- Adaptive Diagnostic and Interventional Imaging, Inserm, CHRU de Nancy, Vandoeuvre-lès-Nancy, France
| | - Khalid Ambarki
- Siemens Healthcare, Siemens Healthcare SAS, Saint Denis, France
| | - Dominik Nickel
- Siemens Healthcare GmbH, MR Application Predevelopment, Erlangen, Germany
| | - Simon Arberet
- Siemens Healthineers, Digital Technology & Innovation, Princeton, NJ, USA
| | - Julien Oster
- Clinical Investigation Center Technological Innovation of Nancy, Inserm, CHRU de Nancy, Vandoeuvre-lès-Nancy, France
- Adaptive Diagnostic and Interventional Imaging, Inserm, CHRU de Nancy, Vandoeuvre-lès-Nancy, France
| | - Xavier Orry
- Department of Adult Radiology, CHRU de Nancy, 5 Rue du Morvan, 54500, Vandoeuvre-lès-Nancy, France
| | - Valérie Laurent
- Department of Adult Radiology, CHRU de Nancy, 5 Rue du Morvan, 54500, Vandoeuvre-lès-Nancy, France
- Adaptive Diagnostic and Interventional Imaging, Inserm, CHRU de Nancy, Vandoeuvre-lès-Nancy, France
| |
Collapse
|
11
|
Ichinohe F, Oyama K, Yamada A, Hayashihara H, Adachi Y, Kitoh Y, Kanki Y, Maruyama K, Nickel MD, Fujinaga Y. Usefulness of Breath-Hold Fat-Suppressed T2-Weighted Images With Deep Learning-Based Reconstruction of the Liver: Comparison to Conventional Free-Breathing Turbo Spin Echo. Invest Radiol 2023; 58:373-379. [PMID: 36728880 DOI: 10.1097/rli.0000000000000943] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
OBJECTIVES The aim of this study was to evaluate the usefulness of breath-hold turbo spin echo with deep learning-based reconstruction (BH-DL-TSE) in acquiring fat-suppressed T2-weighted images (FS-T2WI) of the liver by comparing this method with conventional free-breathing turbo spin echo (FB-TSE) and breath-hold half Fourier single-shot turbo spin echo with deep learning-based reconstruction (BH-DL-HASTE). MATERIALS AND METHODS The study cohort comprised 111 patients with suspected liver disease who underwent 3 T magnetic resonance imaging. Fifty-eight focal solid liver lesions ≥10 mm were also evaluated. Three sets of FS-T2WI were acquired using FB-TSE, prototypical BH-DL-TSE, and prototypical BH-DL-HASTE, respectively. In the qualitative analysis, 2 radiologists evaluated the image quality using a 5-point scale. In the quantitative analysis, we calculated the lesion-to-liver signal intensity ratio (LEL-SIR). Friedman test and Dunn multiple comparison test were performed to assess differences among 3 types of FS-T2WI with respect to image quality and LEL-SIR. RESULTS The mean acquisition time was 4 minutes and 43 seconds ± 1 minute and 21 seconds (95% confidence interval, 4 minutes and 28 seconds to 4 minutes and 58 seconds) for FB-TSE, 40 seconds for BH-DL-TSE, and 20 seconds for BH-DL-HASTE. In the qualitative analysis, BH-DL-HASTE resulted in the fewest respiratory motion artifacts ( P < 0.0001). BH-DL-TSE and FB-TSE exhibited significantly less motion-related signal loss and clearer intrahepatic vessels than BH-DL-HASTE ( P < 0.0001). Regarding the edge sharpness of the left lobe, BH-DL-HASTE scored the highest ( P < 0.0001), and BH-DL-TSE scored higher than FB-TSE ( P = 0.0290). There were no significant differences among 3 types of FS-T2WI with respect to the edge sharpness of the right lobe ( P = 0.1290), lesion conspicuity ( P = 0.5292), and LEL-SIR ( P = 0.6026). CONCLUSIONS BH-DL-TSE provides a shorter acquisition time and comparable or better image quality than FB-TSE, and could replace FB-TSE in acquiring FS-T2WI of the liver. BH-DL-TSE and BH-DL-HASTE have their own advantages and may be used complementarily.
Collapse
Affiliation(s)
- Fumihito Ichinohe
- From the Department of Radiology, Shinshu University School of Medicine
| | - Kazuki Oyama
- From the Department of Radiology, Shinshu University School of Medicine
| | - Akira Yamada
- From the Department of Radiology, Shinshu University School of Medicine
| | | | - Yasuo Adachi
- Radiology Division, Shinshu University Hospital, Matsumoto
| | | | | | - Katsuya Maruyama
- MR Research and Collaboration Department, Siemens Healthcare K.K., Tokyo, Japan
| | | | - Yasunari Fujinaga
- From the Department of Radiology, Shinshu University School of Medicine
| |
Collapse
|
12
|
Afat S, Herrmann J, Almansour H, Benkert T, Weiland E, Hölldobler T, Nikolaou K, Gassenmaier S. Acquisition time reduction of diffusion-weighted liver imaging using deep learning image reconstruction. Diagn Interv Imaging 2023; 104:178-184. [PMID: 36787419 DOI: 10.1016/j.diii.2022.11.002] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 11/01/2022] [Accepted: 11/08/2022] [Indexed: 12/12/2022]
Abstract
PURPOSE The purpose of this study was to investigate the impact of deep learning accelerated diffusion-weighted imaging (DWIDL) in 1.5-T liver MRI on image quality, sharpness, and diagnostic confidence. MATERIALS AND METHODS One-hundred patients who underwent liver MRI at 1.5-T including DWI with two different b-values (50 and 800 s/mm²) between February and April 2022 were retrospectively included. There were 54 men and 46 women, with a mean age of 59 ± 14 (SD) years (range: 21-88 years). The single average raw data were retrospectively processed using a deep learning (DL) image reconstruction algorithm leading to a simulated acquisition time of 1 min 28 s for DWIDL as compared to 2 min 31 s for standard DWI (DWIStd) via reduction of signal averages. All DWI datasets were reviewed by four radiologists using a Likert scale ranging from 1-4 using the following criteria: noise level, extent of artifacts, sharpness, overall image quality, and diagnostic confidence. Furthermore, quantitative assessment of noise and signal-to-noise ratio (SNR) was performed via regions of interest. RESULTS No significant differences were found regarding artifacts and overall image quality (P > 0.05). Noise measurements for the spleen, liver, and erector spinae muscles revealed significantly lower noise for DWIDL versus DWIStd (P < 0.001). SNR measurements in the above-mentioned tissues also showed significantly superior results for DWIDL versus DWIStd for b = 50 s/mm² and ADC maps (all P < 0.001). For b = 800 s/mm², significantly superior results were found for the spleen, right hemiliver, and erector spinae muscles. CONCLUSIONS DL image reconstruction of liver DWI at 1.5-T is feasible including significant reduction of acquisition time without compromised image quality.
Collapse
Affiliation(s)
- Saif Afat
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, Tuebingen 72076, Germany.
| | - Judith Herrmann
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, Tuebingen 72076, Germany
| | - Haidara Almansour
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, Tuebingen 72076, Germany
| | - Thomas Benkert
- MR Applications Predevelopment, Siemens Healthcare GmbH, Allee am Roethelheimpark 2, Erlangen 91052, Germany
| | - Elisabeth Weiland
- MR Applications Predevelopment, Siemens Healthcare GmbH, Allee am Roethelheimpark 2, Erlangen 91052, Germany
| | - Thomas Hölldobler
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, Tuebingen 72076, Germany
| | - Konstantin Nikolaou
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, Tuebingen 72076, Germany; Cluster of Excellence iFIT (EXC 2180) "Image Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tuebingen, Germany
| | - Sebastian Gassenmaier
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, Tuebingen 72076, Germany
| |
Collapse
|
13
|
Chaika M, Afat S, Wessling D, Afat C, Nickel D, Kannengiesser S, Herrmann J, Almansour H, Männlin S, Othman AE, Gassenmaier S. Deep learning-based super-resolution gradient echo imaging of the pancreas: Improvement of image quality and reduction of acquisition time. Diagn Interv Imaging 2023; 104:53-59. [PMID: 35843839 DOI: 10.1016/j.diii.2022.06.006] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 06/16/2022] [Accepted: 06/17/2022] [Indexed: 02/07/2023]
Abstract
PURPOSE The purpose of this study was to evaluate the impact of a deep learning-based super-resolution technique on T1-weighted gradient-echo acquisitions (volumetric interpolated breath-hold examination; VIBE) on the assessment of pancreatic MRI at 1.5 T compared to standard VIBE imaging (VIBESTD). MATERIALS AND METHODS This retrospective single-center study was conducted between April 2021 and October 2021. Fifty patients with a total of 50 detectable pancreatic lesion entities were included in this study. There were 27 men and 23 women, with a mean age of 69 ± 13 (standard deviation [SD]) years (age range: 33-89 years). VIBESTD (precontrast, dynamic, postcontrast) was retrospectively processed with a deep learning-based super-resolution algorithm including a more aggressive partial Fourier setting leading to a simulated acquisition time reduction (VIBESR). Image analysis was performed by two radiologists regarding lesion detectability, noise levels, sharpness and contrast of pancreatic edges, as well as regarding diagnostic confidence using a 5-point Likert-scale with 5 being the best. RESULTS VIBESR was rated better than VIBESTD by both readers regarding lesion detectability (5 [IQR: 5, 5] vs. 5 [IQR: 4, 5], for reader 1; 5 [IQR: 5, 5] vs. 4 [IQR: 4, 5]) for reader 2; both P <0.001), noise levels (5 [IQR: 5, 5] vs. 5 [IQR: 4, 5] for reader 1; 5 [IQR: 5, 5] vs. 4 [IQR: 4, 5] for reader 2; both P <0.001), sharpness and contrast of pancreatic edges (5 [IQR: 5, 5] vs. 5 [IQR: 4, 5] for reader 1; 5 [IQR: 5, 5] vs. 4 [IQR: 4, 5] for reader 2; both P <0.001), as well as regarding diagnostic confidence (5 [IQR: 5, 5] vs. 5 [IQR: 4, 5] for reader 1; 5 [IQR: 5, 5] vs. 4 [IQR: 4, 5] for reader 2; both P <0.001). There were no significant differences between lesion sizes as measured by the two readers on VIBESR and VIBESTD images (P > 0.05). The mean acquisition time for VIBESTD (15 ± 1 [SD] s; range: 11-16 s) was longer than that for VIBESR (13 ± 1 [SD] s; range: 11-14 s) (P < 0.001). CONCLUSION Our results indicate that the newly developed deep learning-based super-resolution algorithm adapted to partial Fourier acquisitions has a positive influence not only on shortening the examination time but also on improvement of image quality in pancreatic MRI.
Collapse
Affiliation(s)
- Maryanna Chaika
- Department of Diagnostic and Interventional Radiology, Hoppe-Seyler-Strasse 3, Eberhard Karls University Tuebingen, 72076 Tuebingen, Germany
| | - Saif Afat
- Department of Diagnostic and Interventional Radiology, Hoppe-Seyler-Strasse 3, Eberhard Karls University Tuebingen, 72076 Tuebingen, Germany
| | - Daniel Wessling
- Department of Diagnostic and Interventional Radiology, Hoppe-Seyler-Strasse 3, Eberhard Karls University Tuebingen, 72076 Tuebingen, Germany
| | - Carmen Afat
- Department of Internal Medicine I, Otfried-Müller-Straße 10, Eberhard Karls University Tuebingen, 72076, Tuebingen, Germany
| | - Dominik Nickel
- MR Applications Predevelopment, Siemens Healthcare GmbH, Allee am Roethelheimpark 2, 91052, Erlangen, Germany
| | - Stephan Kannengiesser
- MR Applications Predevelopment, Siemens Healthcare GmbH, Allee am Roethelheimpark 2, 91052, Erlangen, Germany
| | - Judith Herrmann
- Department of Diagnostic and Interventional Radiology, Hoppe-Seyler-Strasse 3, Eberhard Karls University Tuebingen, 72076 Tuebingen, Germany
| | - Haidara Almansour
- Department of Diagnostic and Interventional Radiology, Hoppe-Seyler-Strasse 3, Eberhard Karls University Tuebingen, 72076 Tuebingen, Germany
| | - Simon Männlin
- Department of Diagnostic and Interventional Radiology, Hoppe-Seyler-Strasse 3, Eberhard Karls University Tuebingen, 72076 Tuebingen, Germany
| | - Ahmed E Othman
- Department of Diagnostic and Interventional Radiology, Hoppe-Seyler-Strasse 3, Eberhard Karls University Tuebingen, 72076 Tuebingen, Germany; Department of Neuroradiology, University Medical Center, 55131, Mainz, Germany
| | - Sebastian Gassenmaier
- Department of Diagnostic and Interventional Radiology, Hoppe-Seyler-Strasse 3, Eberhard Karls University Tuebingen, 72076 Tuebingen, Germany.
| |
Collapse
|
14
|
Deep Learning-Based Automatic Detection and Grading of Motion-Related Artifacts on Gadoxetic Acid-Enhanced Liver MRI. Invest Radiol 2023; 58:166-172. [PMID: 36070544 DOI: 10.1097/rli.0000000000000914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
OBJECTIVES The aim of this study was to develop and validate a deep learning-based algorithm (DLA) for automatic detection and grading of motion-related artifacts on arterial phase liver magnetic resonance imaging (MRI). MATERIALS AND METHODS Multistep DLA for detection and grading of motion-related artifacts, based on the modified ResNet-101 and U-net, were trained using 336 arterial phase images of gadoxetic acid-enhanced liver MRI examinations obtained in 2017 (training dataset; mean age, 68.6 years [range, 18-95]; 254 men). Motion-related artifacts were evaluated in 4 different MRI slices using a 3-tier grading system. In the validation dataset, 313 images from the same institution obtained in 2018 (internal validation dataset; mean age, 67.2 years [range, 21-87]; 228 men) and 329 from 3 different institutions (external validation dataset; mean age, 64.0 years [range, 23-90]; 214 men) were included, and the per-slice and per-examination performances for the detection of motion-related artifacts were evaluated. RESULTS The per-slice sensitivity and specificity of the DLA for detecting grade 3 motion-related artifacts were 91.5% (97/106) and 96.8% (1134/1172) in the internal validation dataset and 93.3% (265/284) and 91.6% (948/1035) in the external validation dataset. The per-examination sensitivity and specificity were 92.0% (23/25) and 99.7% (287/288) in the internal validation dataset and 90.0% (72/80) and 96.0% (239/249) in the external validation dataset, respectively. The processing time of the DLA for automatic grading of motion-related artifacts was from 4.11 to 4.22 seconds per MRI examination. CONCLUSIONS The DLA enabled automatic and instant detection and grading of motion-related artifacts on arterial phase gadoxetic acid-enhanced liver MRI.
Collapse
|
15
|
Gassenmaier S, Warm V, Nickel D, Weiland E, Herrmann J, Almansour H, Wessling D, Afat S. Thin-Slice Prostate MRI Enabled by Deep Learning Image Reconstruction. Cancers (Basel) 2023; 15:578. [PMID: 36765539 PMCID: PMC9913660 DOI: 10.3390/cancers15030578] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 01/08/2023] [Accepted: 01/13/2023] [Indexed: 01/20/2023] Open
Abstract
OBJECTIVES Thin-slice prostate MRI might be beneficial for prostate cancer diagnostics. However, prolongation of acquisition time is a major drawback of thin-slice imaging. Therefore, the purpose of this study was to investigate the impact of a thin-slice deep learning accelerated T2-weighted (w) TSE imaging sequence (T2DLR) of the prostate as compared to conventional T2w TSE imaging (T2S). MATERIALS AND METHODS Thirty patients were included in this prospective study at one university center after obtaining written informed consent. T2S (3 mm slice thickness) was acquired first in three orthogonal planes followed by thin-slice T2DLR (2 mm slice thickness) in axial plane. Acquisition time of axial conventional T2S was 4:12 min compared to 4:37 min for T2DLR. Imaging datasets were evaluated by two radiologists using a Likert-scale ranging from 1-4, with 4 being the best regarding the following parameters: sharpness, lesion detectability, artifacts, overall image quality, and diagnostic confidence. Furthermore, preference of T2S versus T2DLR was evaluated. RESULTS The mean patient age was 68 ± 8 years. Sharpness of images and lesion detectability were rated better in T2DLR with a median of 4 versus a median of 3 in T2S (p < 0.001 for both readers). Image noise was evaluated to be significantly worse in T2DLR as compared to T2S (p < 0.001 and p = 0.021, respectively). Overall image quality was also evaluated to be superior in T2DLR versus T2S with a median of 4 versus 3 (p < 0.001 for both readers). Both readers chose T2DLR in 29 cases as their preference. CONCLUSIONS Thin-slice T2DLR of the prostate provides a significant improvement of image quality without significant prolongation of acquisition time.
Collapse
Affiliation(s)
- Sebastian Gassenmaier
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, 72076 Tuebingen, Germany
| | - Verena Warm
- Institute for Pathology and Neuropathology, University Hospital of Tuebingen, Eberhard Karls University Tuebingen, 72076 Tuebingen, Germany
| | - Dominik Nickel
- MR Applications Predevelopment, Siemens Healthcare GmbH, Allee am Roethelheimpark 2, 91052 Erlangen, Germany
| | - Elisabeth Weiland
- MR Applications Predevelopment, Siemens Healthcare GmbH, Allee am Roethelheimpark 2, 91052 Erlangen, Germany
| | - Judith Herrmann
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, 72076 Tuebingen, Germany
| | - Haidara Almansour
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, 72076 Tuebingen, Germany
| | - Daniel Wessling
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, 72076 Tuebingen, Germany
| | - Saif Afat
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, 72076 Tuebingen, Germany
| |
Collapse
|
16
|
Herrmann J, Wessling D, Nickel D, Arberet S, Almansour H, Afat C, Afat S, Gassenmaier S, Othman AE. Comprehensive Clinical Evaluation of a Deep Learning-Accelerated, Single-Breath-Hold Abdominal HASTE at 1.5 T and 3 T. Acad Radiol 2023; 30:93-102. [PMID: 35469719 DOI: 10.1016/j.acra.2022.03.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 03/12/2022] [Accepted: 03/20/2022] [Indexed: 11/01/2022]
Abstract
To evaluate the clinical performance of a deep learning-accelerated single-breath-hold half-Fourier acquisition single-shot turbo spin echo (HASTEDL)-sequence for T2-weighted fat-suppressed MRI of the abdomen at 1.5 T and 3 T in comparison to standard T2-weighted fat-suppressed multi-shot turbo spin echo-sequence. A total of 320 patients who underwent a clinically indicated liver MRI at 1.5 T and 3 T between August 2020 and February 2021 were enrolled in this single-center, retrospective study. HASTEDL and standard sequences were assessed regarding overall and organ-based image quality, noise, contrast, sharpness, artifacts, diagnostic confidence, as well as lesion detectability using a Likert scale ranging from 1 to 4 (4 = best). The number of visible lesions of each organ was counted and the largest diameter of the major lesion was measured. HASTEDL showed excellent image quality (median 4, interquartile range 3-4), although BLADE (median 4, interquartile range 4-4) was rated significantly higher for overall and organ-based image quality of the adrenal gland (P < .001), contrast (P < 0.001), sharpness (P < 0.001), artifacts (P < 0.001), as well as diagnostic confidence (P < .001). No significant differences were found concerning noise (P = 0.886), organ-based image quality of the liver, pancreas, spleen, and kidneys (P = 0.120-0.366), number and measured diameter of the detected lesions (ICC = 0.972-1.0). Reduction of the aquisition time (TA) was at least 89% for 1.5 T images and 86% for 3 T images. HASTEDL provided excellent image quality, good diagnostic confidence and lesion detection compared to a standard T2-sequences, allowing an eminent reduction of the acquisition time.
Collapse
Affiliation(s)
- Judith Herrmann
- Department of Diagnostic and Interventional Radiology, Hoppe-Seyler-Strasse 3, Eberhard Karls University Tuebingen, 72076 Tuebingen, Germany
| | - Daniel Wessling
- Department of Diagnostic and Interventional Radiology, Hoppe-Seyler-Strasse 3, Eberhard Karls University Tuebingen, 72076 Tuebingen, Germany
| | - Dominik Nickel
- MR Applications Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Simon Arberet
- Digital Technology & Innovation, Siemens Healthineers, Princeton, NJ, USA
| | - Haidara Almansour
- Department of Diagnostic and Interventional Radiology, Hoppe-Seyler-Strasse 3, Eberhard Karls University Tuebingen, 72076 Tuebingen, Germany
| | - Carmen Afat
- Department of Diagnostic and Interventional Radiology, Hoppe-Seyler-Strasse 3, Eberhard Karls University Tuebingen, 72076 Tuebingen, Germany
| | - Saif Afat
- Department of Diagnostic and Interventional Radiology, Hoppe-Seyler-Strasse 3, Eberhard Karls University Tuebingen, 72076 Tuebingen, Germany
| | - Sebastian Gassenmaier
- Department of Diagnostic and Interventional Radiology, Hoppe-Seyler-Strasse 3, Eberhard Karls University Tuebingen, 72076 Tuebingen, Germany
| | - Ahmed E Othman
- Department of Diagnostic and Interventional Radiology, Hoppe-Seyler-Strasse 3, Eberhard Karls University Tuebingen, 72076 Tuebingen, Germany; Department of Neuroradiology, University Medical Center, Mainz, Germany.
| |
Collapse
|
17
|
Nepal P, Bagga B, Feng L, Chandarana H. Respiratory Motion Management in Abdominal MRI: Radiology In Training. Radiology 2023; 306:47-53. [PMID: 35997609 PMCID: PMC9792710 DOI: 10.1148/radiol.220448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
A 96-year-old woman had a suboptimal evaluation of liver observations at abdominal MRI due to significant respiratory motion. State-of-the-art strategies to minimize respiratory motion during clinical abdominal MRI are discussed.
Collapse
Affiliation(s)
- Pankaj Nepal
- From the Department of Radiology, Massachusetts General Hospital, 55
Fruit St, Boston, MA 02114 (P.N.); Department of Radiology, New York University
School of Medicine, New York, NY (B.B., H.C.); and Biomedical Engineering and
Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount
Sinai, New York, NY (L.F.)
| | - Barun Bagga
- From the Department of Radiology, Massachusetts General Hospital, 55
Fruit St, Boston, MA 02114 (P.N.); Department of Radiology, New York University
School of Medicine, New York, NY (B.B., H.C.); and Biomedical Engineering and
Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount
Sinai, New York, NY (L.F.)
| | - Li Feng
- From the Department of Radiology, Massachusetts General Hospital, 55
Fruit St, Boston, MA 02114 (P.N.); Department of Radiology, New York University
School of Medicine, New York, NY (B.B., H.C.); and Biomedical Engineering and
Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount
Sinai, New York, NY (L.F.)
| | - Hersh Chandarana
- From the Department of Radiology, Massachusetts General Hospital, 55
Fruit St, Boston, MA 02114 (P.N.); Department of Radiology, New York University
School of Medicine, New York, NY (B.B., H.C.); and Biomedical Engineering and
Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount
Sinai, New York, NY (L.F.)
| |
Collapse
|
18
|
Ren J, Li Y, Liu FS, Liu C, Zhu JX, Nickel MD, Wang XY, Liu XY, Zhao J, He YL, Jin ZY, Xue HD. Comparison of a deep learning-accelerated T2-weighted turbo spin echo sequence and its conventional counterpart for female pelvic MRI: reduced acquisition times and improved image quality. Insights Imaging 2022; 13:193. [PMID: 36512158 DOI: 10.1186/s13244-022-01321-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 10/29/2022] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVES To investigate the feasibility of a deep learning-accelerated T2-weighted turbo spin echo (TSE) sequence (T2DL) applied to female pelvic MRI, using standard T2-weighted TSE (T2S) as reference. METHODS In total, 24 volunteers and 48 consecutive patients with benign uterine diseases were enrolled. Patients in the menstrual phase were excluded. T2S and T2DL sequences in three planes were performed for each participant. Quantitative image evaluation was conducted by calculating the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Image geometric distortion was evaluated by measuring the diameters in all three directions of the uterus and lesions. Qualitative image evaluation including overall image quality, artifacts, boundary sharpness of the uterine zonal layers, and lesion conspicuity were assessed by three radiologists using a 5-point Likert scale, with 5 indicating the best quality. Comparative analyses were conducted for the two sequences. RESULTS T2DL resulted in a 62.7% timing reduction (1:54 min for T2DL and 5:06 min for T2S in axial, sagittal, and coronal imaging, respectively). Compared to T2S, T2DL had significantly higher SNR (p ≤ 0.001) and CNR (p ≤ 0.007), and without geometric distortion (p = 0.925-0.981). Inter-observer agreement regarding qualitative evaluation was excellent (Kendall's W > 0.75). T2DL provided superior image quality (all p < 0.001), boundary sharpness of the uterine zonal layers (all p < 0.001), lesion conspicuity (p = 0.002, p < 0.001, and p = 0.021), and fewer artifacts (all p < 0.001) in sagittal, axial, and coronal imaging. CONCLUSIONS Compared with standard TSE, deep learning-accelerated T2-weighted TSE is feasible to reduce acquisition time of female pelvic MRI with significant improvement of image quality.
Collapse
Affiliation(s)
- Jing Ren
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuai Fu Yuan Road, Dongcheng Dist., Beijing, 100730, People's Republic of China
| | - Yuan Li
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, National Clinical Research Center for Obstetric and Gynecologic Diseases, Beijing, People's Republic of China
| | - Fei-Shi Liu
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuai Fu Yuan Road, Dongcheng Dist., Beijing, 100730, People's Republic of China
| | - Chong Liu
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuai Fu Yuan Road, Dongcheng Dist., Beijing, 100730, People's Republic of China
| | - Jin-Xia Zhu
- MR Collaboration, Siemens Healthineers Ltd., Beijing, People's Republic of China
| | | | - Xiao-Ye Wang
- MR Clinical Marketing, Siemens Healthineers Ltd., Beijing, People's Republic of China
| | - Xin-Yu Liu
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuai Fu Yuan Road, Dongcheng Dist., Beijing, 100730, People's Republic of China
| | - Jia Zhao
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuai Fu Yuan Road, Dongcheng Dist., Beijing, 100730, People's Republic of China
| | - Yong-Lan He
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuai Fu Yuan Road, Dongcheng Dist., Beijing, 100730, People's Republic of China.
| | - Zheng-Yu Jin
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuai Fu Yuan Road, Dongcheng Dist., Beijing, 100730, People's Republic of China.
| | - Hua-Dan Xue
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuai Fu Yuan Road, Dongcheng Dist., Beijing, 100730, People's Republic of China.
| |
Collapse
|
19
|
Deep Learning-Enhanced Parallel Imaging and Simultaneous Multislice Acceleration Reconstruction in Knee MRI. Invest Radiol 2022; 57:826-833. [PMID: 35776434 DOI: 10.1097/rli.0000000000000900] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
OBJECTIVES This study aimed to examine various combinations of parallel imaging (PI) and simultaneous multislice (SMS) acceleration imaging using deep learning (DL)-enhanced and conventional reconstruction. The study also aimed at comparing the diagnostic performance of the various combinations in internal knee derangement and provided a quantitative evaluation of image sharpness and noise using edge rise distance (ERD) and noise power (NP), respectively. MATERIALS AND METHODS The data from adult patients who underwent knee magnetic resonance imaging using various DL-enhanced acquisitions between June 2021 and January 2022 were retrospectively analyzed. The participants underwent conventional 2-fold PI and DL protocols with 4- to 8-fold acceleration imaging (P2S2 [2-fold PI with 2-fold SMS], P3S2, and P4S2). Three readers evaluated the internal knee derangement and the overall image quality. The diagnostic performance was calculated using consensus reading as a standard reference, and we conducted comparative evaluations. We calculated the ERD and NP for quantitative evaluations of image sharpness and noise, respectively. Interreader and intermethod agreements were calculated using Fleiss κ. RESULTS A total of 33 patients (mean age, 49 ± 19 years; 20 women) were included in this study. The diagnostic performance for internal knee derangement and the overall image quality were similar among the evaluated protocols. The NP values were significantly lower using the DL protocols than with conventional imaging ( P < 0.001), whereas the ERD values were similar among these methods ( P > 0.12). Interreader and intermethod agreements were moderate-to-excellent (κ = 0.574-0.838) and good-to-excellent (κ = 0.755-1.000), respectively. In addition, the mean acquisition time was reduced by 47% when using DL with P2S2, by 62% with P3S2, and by 71% with P4S2, compared with conventional P2 imaging (2 minutes and 55 seconds). CONCLUSIONS The combined use of DL-enhanced 8-fold acceleration imaging (4-fold PI with 2-fold SMS) showed comparable performance with conventional 2-fold PI for the evaluation of internal knee derangement, with a 71% reduction in acquisition time.
Collapse
|
20
|
Ji Lee E, Chang YW, Kon Sung J, Thomas B. Feasibility of deep learning k-space-to-image reconstruction for diffusion weighted imaging in patients with breast cancers: focus on image quality and reduced scan time. Eur J Radiol 2022; 157:110608. [DOI: 10.1016/j.ejrad.2022.110608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 10/29/2022] [Accepted: 11/08/2022] [Indexed: 11/14/2022]
|
21
|
Improved Single Breath-Hold SSFSE Sequence for Liver MRI Based on Compressed Sensing: Evaluation of Image Quality Compared with Conventional T2-Weighted Sequences. Diagnostics (Basel) 2022; 12:diagnostics12092164. [PMID: 36140565 PMCID: PMC9497881 DOI: 10.3390/diagnostics12092164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 09/03/2022] [Accepted: 09/04/2022] [Indexed: 11/16/2022] Open
Abstract
The purpose of this study was to evaluate the image quality of compressed-sensing accelerated single-shot fast spin-echo (SSFSECS) sequences acquired within a single breath-hold in comparison with conventional SSFSE (SSFSECONV) and multishot TSE (mTSE). A total of 101 patients who underwent liver MRI at 3 T, including SSFSECONV (acquisition time (TA) = 58−62 s), mTSE (TA = 108 s), and SSFSECS (TA = 18 s), were included in this retrospective study. Two radiologists assessed the three sequences with respect to artifacts, organ sharpness, small structure visibility, overall image quality, and conspicuity of main lesions of liver and pancreas using a five-point evaluation scale system. Descriptive statistics and the Wilcoxon signed-rank test were used for statistical analysis. SSFSECS was significantly better than SSFSECONV and mTSE for artifacts, small structure visibility, overall image quality, and conspicuity of main lesions (p < 0.005). Regarding organ sharpness, mTSE and SSFSECS did not significantly differ (p = 0.554). Conspicuity of liver lesion did not significantly differ between SSFSECONV and mTSE (p = 0.404). SSFSECS showed superior image quality compared with SSFSECONV and mTSE despite a more than three-fold reduction in TA, suggesting a remarkable potential for saving time in liver imaging.
Collapse
|
22
|
|
23
|
Mulé S, Kharrat R, Zerbib P, Massire A, Nickel MD, Ambarki K, Reizine E, Baranes L, Zegai B, Pigneur F, Kobeiter H, Luciani A. Fast T2-weighted liver MRI: Image quality and solid focal lesions conspicuity using a deep learning accelerated single breath-hold HASTE fat-suppressed sequence. Diagn Interv Imaging 2022; 103:479-485. [PMID: 35597761 DOI: 10.1016/j.diii.2022.05.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 04/27/2022] [Accepted: 05/02/2022] [Indexed: 11/03/2022]
Abstract
PURPOSE Acceleration of MRI acquisitions and especially of T2-weighted sequences is essential to reduce the duration of MRI examinations but also kinetic artifacts in liver imaging. The purpose of this study was to compare the acquisition time and the image quality of a single-shot fat-suppressed turbo spin-echo (TSE) T2-weighted sequence with deep learning reconstruction (HASTEDL) with that of a fat-suppressed T2-weighted BLADE TSE sequence in patients with focal liver lesions. MATERIALS AND METHODS Ninety-five patients (52 men, 43 women; mean age: 61 ± 14 [SD]; age range: 28-87 years) with 42 focal liver lesions (17 hepatocellular carcinomas, 10 sarcoidosis lesions, 9 myeloma lesions, 3 liver metastases and 3 focal nodular hyperplasias) who underwent liver MRI at 1.5 T including HASTEDL and BLADE sequences were retrospectively included. Overall image quality, noise level in the liver, lesion conspicuity and sharpness of liver lesion contours were assessed by two independent readers. Liver signal-to-noise ratio (SNR) and lesion contrast-to-noise ratio (CNR) were measured and compared between the two sequences, as well as the mean duration of the sequences (Student t-test or Wilcoxon test for paired data). RESULTS Median overall quality on HASTEDL images (3; IQR: 3, 3) was significantly greater than that on BLADE images (2; IQR: 1, 3) (P < 0.001). Median noise level in the liver on HASTEDL images (0; IQR: 0, 0.5) was significantly lower than that on BLADE images (1; IQR: 1, 2) (P < 0.001). On HASTEDL images, mean liver SNR (107.3 ± 39.7 [SD]) and mean focal liver lesion CNR (87.0 ± 76.6 [SD]) were significantly greater than those on BLADE images (67.1 ± 23.8 [SD], P < 0.001 and 48.6 ± 43.9 [SD], P = 0.027, respectively). Acquisition time was significantly shorter with the HASTEDL sequence (18 ± [0] s; range: 18-18 s) compared to BLADE sequence (152 ± 47 [SD] s; range: 87-263 s) (P < 0.001). CONCLUSION By comparison with the BLADE sequence, HASTEDL sequence significantly reduces acquisition time while improving image quality, liver SNR and focal liver lesions CNR.
Collapse
Affiliation(s)
- Sébastien Mulé
- Service d'Imagerie Médicale, AP-HP, Hôpitaux Universitaires Henri Mondor, Créteil 94000, France; Faculté de Santé, Université Paris Est Créteil, Créteil 94000, France; INSERM IMRB, U 955, Equipe 18, Créteil 94000, France.
| | - Rym Kharrat
- Service d'Imagerie Médicale, AP-HP, Hôpitaux Universitaires Henri Mondor, Créteil 94000, France
| | - Pierre Zerbib
- Service d'Imagerie Médicale, AP-HP, Hôpitaux Universitaires Henri Mondor, Créteil 94000, France
| | | | | | | | - Edouard Reizine
- Service d'Imagerie Médicale, AP-HP, Hôpitaux Universitaires Henri Mondor, Créteil 94000, France; Faculté de Santé, Université Paris Est Créteil, Créteil 94000, France; INSERM IMRB, U 955, Equipe 18, Créteil 94000, France
| | - Laurence Baranes
- Service d'Imagerie Médicale, AP-HP, Hôpitaux Universitaires Henri Mondor, Créteil 94000, France
| | - Benhalima Zegai
- Service d'Imagerie Médicale, AP-HP, Hôpitaux Universitaires Henri Mondor, Créteil 94000, France
| | - Frederic Pigneur
- Service d'Imagerie Médicale, AP-HP, Hôpitaux Universitaires Henri Mondor, Créteil 94000, France
| | - Hicham Kobeiter
- Service d'Imagerie Médicale, AP-HP, Hôpitaux Universitaires Henri Mondor, Créteil 94000, France; Faculté de Santé, Université Paris Est Créteil, Créteil 94000, France
| | - Alain Luciani
- Service d'Imagerie Médicale, AP-HP, Hôpitaux Universitaires Henri Mondor, Créteil 94000, France; Faculté de Santé, Université Paris Est Créteil, Créteil 94000, France; INSERM IMRB, U 955, Equipe 18, Créteil 94000, France
| |
Collapse
|
24
|
HASTE und Deep Learning-Rekonstruktion zur Darstellung von Leberläsionen. ROFO-FORTSCHR RONTG 2022. [DOI: 10.1055/a-1692-1852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
|
25
|
Impact of Deep Learning Reconstruction Combined With a Sharpening Filter on Single-Shot Fast Spin-Echo T2-Weighted Magnetic Resonance Imaging of the Uterus. Invest Radiol 2022; 57:379-386. [PMID: 34999668 DOI: 10.1097/rli.0000000000000847] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVE This study aimed to evaluate the effects of deep learning (DL) reconstruction and a postprocessing sharpening filter on the image quality of single-shot fast spin-echo (SSFSE) T2-weighted imaging (T2WI) of the uterus. MATERIALS AND METHODS Fifty consecutive patients who underwent pelvic magnetic resonance imaging were included. Parasagittal T2WI with a slice thickness of 4 mm was obtained with the periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) and SSFSE sequences (mean scan time, 204 and 22 seconds, respectively). The following 3 types of SSFSE images were reconstructed, and the signal-to-noise ratio (SNR) and tissue contrast were assessed: conventional reconstruction (SSFSE-C), DL reconstruction (SSFSE-DL), and DL with a sharpening filter (SSFSE-DLF). Three radiologists independently assessed image quality, and area under the visual grading characteristics curve (AUCVGC) analysis was performed to compare the SSFSE and PROPELLER images. RESULTS Compared with that of the PROPELLER images, the SNR of the SSFSE-C, SSFSE-DL, and SSFSE-DLF images was significantly lower (P < 0.05), significantly higher (P < 0.05), and equivalent, respectively. The SSFSE-DL images exhibited significantly lower contrast between the junctional zone and myometrium than those obtained with the other sequences (P < 0.05). In qualitative comparisons with the PROPELLER images, all 3 SSFSE sequences, SSFSE-DL, and SSFSE-DLF demonstrated significantly higher scores for artifacts, noise, and sharpness, respectively (P < 0.01). The overall image quality of SSFSE-C (mean AUCVGC, 0.03; P < 0.01) and SSFSE-DL (mean AUCVGC, 0.23; P < 0.01) was rated as significantly inferior, whereas that of SSFSE-DLF (mean AUCVGC, 0.69) was equivalent or significantly higher (P < 0.01). CONCLUSION Using a combination of DL and a sharpening filter markedly increases the image quality of SSFSE of the uterus to the level of the PROPELLER sequence.
Collapse
|
26
|
Feasibility of an accelerated 2D-multi-contrast knee MRI protocol using deep-learning image reconstruction: a prospective intraindividual comparison with a standard MRI protocol. Eur Radiol 2022; 32:6215-6229. [PMID: 35389046 PMCID: PMC9381615 DOI: 10.1007/s00330-022-08753-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 03/17/2022] [Accepted: 03/18/2022] [Indexed: 01/19/2023]
Abstract
OBJECTIVES The aim of this study was to evaluate the image quality and diagnostic performance of a deep-learning (DL)-accelerated two-dimensional (2D) turbo spin echo (TSE) MRI of the knee at 1.5 and 3 T in clinical routine in comparison to standard MRI. MATERIAL AND METHODS Sixty participants, who underwent knee MRI at 1.5 and 3 T between October/2020 and March/2021 with a protocol using standard 2D-TSE (TSES) and DL-accelerated 2D-TSE sequences (TSEDL), were enrolled in this prospective institutional review board-approved study. Three radiologists assessed the sequences regarding structural abnormalities and evaluated the images concerning overall image quality, artifacts, noise, sharpness, subjective signal-to-noise ratio, and diagnostic confidence using a Likert scale (1-5, 5 = best). RESULTS Overall image quality for TSEDL was rated to be excellent (median 5, IQR 4-5), significantly higher compared to TSES (median 5, IQR 4 - 5, p < 0.05), showing significantly lower extents of noise and improved sharpness (p < 0.001). Inter- and intra-reader agreement was almost perfect (κ = 0.92-1.00) for the detection of internal derangement and substantial to almost perfect (κ = 0.58-0.98) for the assessment of cartilage defects. No difference was found concerning the detection of bone marrow edema and fractures. The diagnostic confidence of TSEDL was rated to be comparable to that of TSES (median 5, IQR 5-5, p > 0.05). Time of acquisition could be reduced to 6:11 min using TSEDL compared to 11:56 min for a protocol using TSES. CONCLUSION TSEDL of the knee is clinically feasible, showing excellent image quality and equivalent diagnostic performance compared to TSES, reducing the acquisition time about 50%. KEY POINTS • Deep-learning reconstructed TSE imaging is able to almost halve the acquisition time of a three-plane knee MRI with proton density and T1-weighted images, from 11:56 min to 6:11 min at 3 T. • Deep-learning reconstructed TSE imaging of the knee provided significant improvement of noise levels (p < 0.001), providing higher image quality (p < 0.05) compared to conventional TSE imaging. • Deep-learning reconstructed TSE imaging of the knee had similar diagnostic performance for internal derangement of the knee compared to standard TSE.
Collapse
|
27
|
Park HJ, Seo N, Kim SY. Current Landscape and Future Perspectives of Abbreviated MRI for Hepatocellular Carcinoma Surveillance. Korean J Radiol 2022; 23:598-614. [PMID: 35434979 PMCID: PMC9174497 DOI: 10.3348/kjr.2021.0896] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 01/18/2022] [Accepted: 02/10/2022] [Indexed: 11/15/2022] Open
Abstract
While ultrasound (US) is considered an important tool for hepatocellular carcinoma (HCC) surveillance, it has limited sensitivity for detecting early-stage HCC. Abbreviated MRI (AMRI) has recently gained popularity owing to better sensitivity in its detection of early-stage HCC than US, while also minimizing the time and cost in comparison to complete contrast-enhanced MRI, as AMRI includes only a few essential sequences tailored for detecting HCC. Currently, three AMRI protocols exist, namely gadoxetic acid-enhanced hepatobiliary-phase AMRI, dynamic contrast-enhanced AMRI, and non-enhanced AMRI. In this study, we discussed the rationale and technical details of AMRI techniques for achieving optimal surveillance performance. The strengths, weaknesses, and current issues of each AMRI protocol were also elucidated. Moreover, we scrutinized previously performed AMRI studies regarding clinical and technical factors. Reporting and recall strategies were discussed while considering the differences in AMRI protocols. A risk-stratified approach for the target population should be taken to maximize the benefits of AMRI and the cost-effectiveness should be considered. In the era of multiple HCC surveillance tools, patients need to be fully informed about their choices for better adherence to a surveillance program.
Collapse
Affiliation(s)
- Hyo Jung Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Nieun Seo
- Department of Radiology, Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - So Yeon Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| |
Collapse
|
28
|
Clinical Evaluation of an Abbreviated Contrast-Enhanced Whole-Body MRI for Oncologic Follow-Up Imaging. Diagnostics (Basel) 2021; 11:diagnostics11122368. [PMID: 34943604 PMCID: PMC8700680 DOI: 10.3390/diagnostics11122368] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Revised: 12/06/2021] [Accepted: 12/14/2021] [Indexed: 12/29/2022] Open
Abstract
Over the last decades, overall survival for most cancer types has increased due to earlier diagnosis and more effective treatments. Simultaneously, whole-body MRI-(WB-MRI) has gained importance as a radiation free staging alternative to computed tomography. The aim of this study was to evaluate the diagnostic confidence and reproducibility of a novel abbreviated 20-min WB-MRI for oncologic follow-up imaging in patients with melanoma. In total, 24 patients with melanoma were retrospectively included in this institutional review board-approved study. All patients underwent three consecutive staging examinations via WB-MRI in a clinical 3 T MR scanner with an abbreviated 20-min protocol. Three radiologists independently evaluated the images in a blinded, random order regarding image quality (overall image quality, organ-based image quality, sharpness, noise, and artifacts) and regarding its diagnostic confidence on a 5-point-Likert-Scale (5 = excellent). Inter-reader agreement and reproducibility were assessed. Overall image quality and diagnostic confidence were rated to be excellent (median 5, interquartile range [IQR] 5–5). The sharpness of anatomic structures, and the extent of noise and artifacts, as well as the assessment of lymph nodes, liver, bone, and the cutaneous system were rated to be excellent (median 5, IQR 4–5). The image quality of the lung was rated to be good (median 4, IQR 4–5). Therefore, our study demonstrated that the novel accelerated 20-min WB-MRI protocol is feasible, providing high image quality and diagnostic confidence with reliable reproducibility for oncologic follow-up imaging.
Collapse
|
29
|
Gassenmaier S, Küstner T, Nickel D, Herrmann J, Hoffmann R, Almansour H, Afat S, Nikolaou K, Othman AE. Deep Learning Applications in Magnetic Resonance Imaging: Has the Future Become Present? Diagnostics (Basel) 2021; 11:2181. [PMID: 34943418 PMCID: PMC8700442 DOI: 10.3390/diagnostics11122181] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 11/18/2021] [Accepted: 11/22/2021] [Indexed: 12/11/2022] Open
Abstract
Deep learning technologies and applications demonstrate one of the most important upcoming developments in radiology. The impact and influence of these technologies on image acquisition and reporting might change daily clinical practice. The aim of this review was to present current deep learning technologies, with a focus on magnetic resonance image reconstruction. The first part of this manuscript concentrates on the basic technical principles that are necessary for deep learning image reconstruction. The second part highlights the translation of these techniques into clinical practice. The third part outlines the different aspects of image reconstruction techniques, and presents a review of the current literature regarding image reconstruction and image post-processing in MRI. The promising results of the most recent studies indicate that deep learning will be a major player in radiology in the upcoming years. Apart from decision and diagnosis support, the major advantages of deep learning magnetic resonance imaging reconstruction techniques are related to acquisition time reduction and the improvement of image quality. The implementation of these techniques may be the solution for the alleviation of limited scanner availability via workflow acceleration. It can be assumed that this disruptive technology will change daily routines and workflows permanently.
Collapse
Affiliation(s)
- Sebastian Gassenmaier
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University Tuebingen, 72076 Tuebingen, Germany; (S.G.); (J.H.); (R.H.); (H.A.); (S.A.); (K.N.)
| | - Thomas Küstner
- Department of Diagnostic and Interventional Radiology, Medical Image and Data Analysis (MIDAS.lab), Eberhard-Karls-University Tuebingen, 72076 Tuebingen, Germany;
| | - Dominik Nickel
- MR Applications Predevelopment, Siemens Healthcare GmbH, Allee am Roethelheimpark 2, 91052 Erlangen, Germany;
| | - Judith Herrmann
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University Tuebingen, 72076 Tuebingen, Germany; (S.G.); (J.H.); (R.H.); (H.A.); (S.A.); (K.N.)
| | - Rüdiger Hoffmann
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University Tuebingen, 72076 Tuebingen, Germany; (S.G.); (J.H.); (R.H.); (H.A.); (S.A.); (K.N.)
| | - Haidara Almansour
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University Tuebingen, 72076 Tuebingen, Germany; (S.G.); (J.H.); (R.H.); (H.A.); (S.A.); (K.N.)
| | - Saif Afat
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University Tuebingen, 72076 Tuebingen, Germany; (S.G.); (J.H.); (R.H.); (H.A.); (S.A.); (K.N.)
| | - Konstantin Nikolaou
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University Tuebingen, 72076 Tuebingen, Germany; (S.G.); (J.H.); (R.H.); (H.A.); (S.A.); (K.N.)
| | - Ahmed E. Othman
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University Tuebingen, 72076 Tuebingen, Germany; (S.G.); (J.H.); (R.H.); (H.A.); (S.A.); (K.N.)
- Department of Neuroradiology, University Medical Center, 55131 Mainz, Germany
| |
Collapse
|
30
|
Gassenmaier S, Afat S, Nickel MD, Mostapha M, Herrmann J, Almansour H, Nikolaou K, Othman AE. Accelerated T2-Weighted TSE Imaging of the Prostate Using Deep Learning Image Reconstruction: A Prospective Comparison with Standard T2-Weighted TSE Imaging. Cancers (Basel) 2021; 13:cancers13143593. [PMID: 34298806 PMCID: PMC8303682 DOI: 10.3390/cancers13143593] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 07/07/2021] [Accepted: 07/15/2021] [Indexed: 12/22/2022] Open
Abstract
Multiparametric MRI (mpMRI) of the prostate has become the standard of care in prostate cancer evaluation. Recently, deep learning image reconstruction (DLR) methods have been introduced with promising results regarding scan acceleration. Therefore, the aim of this study was to investigate the impact of deep learning image reconstruction (DLR) in a shortened acquisition process of T2-weighted TSE imaging, regarding the image quality and diagnostic confidence, as well as PI-RADS and T2 scoring, as compared to standard T2 TSE imaging. Sixty patients undergoing 3T mpMRI for the evaluation of prostate cancer were prospectively enrolled in this institutional review board-approved study between October 2020 and March 2021. After the acquisition of standard T2 TSE imaging (T2S), the novel T2 TSE sequence with DLR (T2DLR) was applied in three planes. Overall, the acquisition time for T2S resulted in 10:21 min versus 3:50 min for T2DLR. The image evaluation was performed by two radiologists independently using a Likert scale ranging from 1-4 (4 best) applying the following criteria: noise levels, artifacts, overall image quality, diagnostic confidence, and lesion conspicuity. Additionally, T2 and PI-RADS scoring were performed. The mean patient age was 69 ± 9 years (range, 49-85 years). The noise levels and the extent of the artifacts were evaluated to be significantly improved in T2DLR versus T2S by both readers (p < 0.05). Overall image quality was also evaluated to be superior in T2DLR versus T2S in all three acquisition planes (p = 0.005-<0.001). Both readers evaluated the item lesion conspicuity to be superior in T2DLR with a median of 4 versus a median of 3 in T2S (p = 0.001 and <0.001, respectively). T2-weighted TSE imaging of the prostate in three planes with an acquisition time reduction of more than 60% including DLR is feasible with a significant improvement of image quality.
Collapse
Affiliation(s)
- Sebastian Gassenmaier
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University Tuebingen, Hoppe-Seyler-Straße 3, 72076 Tuebingen, Germany; (S.G.); (S.A.); (J.H.); (H.A.); (K.N.)
| | - Saif Afat
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University Tuebingen, Hoppe-Seyler-Straße 3, 72076 Tuebingen, Germany; (S.G.); (S.A.); (J.H.); (H.A.); (K.N.)
| | | | - Mahmoud Mostapha
- Digital Technology & Innovation, Siemens Medical Solutions USA, Inc., Princeton, NJ 08540, USA;
| | - Judith Herrmann
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University Tuebingen, Hoppe-Seyler-Straße 3, 72076 Tuebingen, Germany; (S.G.); (S.A.); (J.H.); (H.A.); (K.N.)
| | - Haidara Almansour
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University Tuebingen, Hoppe-Seyler-Straße 3, 72076 Tuebingen, Germany; (S.G.); (S.A.); (J.H.); (H.A.); (K.N.)
| | - Konstantin Nikolaou
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University Tuebingen, Hoppe-Seyler-Straße 3, 72076 Tuebingen, Germany; (S.G.); (S.A.); (J.H.); (H.A.); (K.N.)
- Cluster of Excellence iFIT (EXC 2180) “Image Guided and Functionally Instructed Tumor Therapies”, University of Tuebingen, 72076 Tuebingen, Germany
| | - Ahmed E. Othman
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University Tuebingen, Hoppe-Seyler-Straße 3, 72076 Tuebingen, Germany; (S.G.); (S.A.); (J.H.); (H.A.); (K.N.)
- Department of Neuroradiology, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany
- Correspondence: ; Tel.: +49-7071-29-68624; Fax: +49-7071-29-5845
| |
Collapse
|