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Kretzler ME, Huang SS, Sun JEP, Bittencourt LK, Chen Y, Griswold MA, Boyacioglu R. Free-breathing qRF-MRF with pilot tone respiratory motion navigator for T 1, T 2, T 2*, and off-resonance mapping of the human body at 3 T. MAGMA (NEW YORK, N.Y.) 2024:10.1007/s10334-024-01209-z. [PMID: 39414686 DOI: 10.1007/s10334-024-01209-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/25/2024] [Revised: 09/18/2024] [Accepted: 09/26/2024] [Indexed: 10/18/2024]
Abstract
Standard quantitative abdominal MRI techniques are time consuming, require breath-holds, and are susceptible to patient motion artifacts. Magnetic resonance fingerprinting (MRF) is naturally multi-parametric and quantifies multiple tissue properties, including T1 and T2. This work includes T2* and off-resonance mapping into a free-breathing MRF framework utilizing a pilot tone navigator. The new acquisition and reconstruction are compared to current clinical standards. Prospective. Ten volunteers. 3 T scanner, Quadratic-RF MRF, Balanced SSFP, Inversion recovery spin-echo, LiverLab. MRI ROIs were evaluated in the liver, spleen, pancreas, kidney (cortex and medulla), and paravertebral muscle by two abdominal imaging investigators for ten healthy adult volunteers for clinical standard, breath-Hold (BH) qRF-MRF, and free-breathing qRF-MRF with pilot-tone (PT) acquisitions. Bland-Altman analysis as well as Student's T tests were used to evaluate and compare the respective ROI analyses. Quantitative values between breath-Hold (BH) and free-breathing qRF-MRF with pilot-tone (PT) results show good agreement with clinical standard T1 and T2 quantitative mapping, and Dixon q-VIBE (acquired using the Siemens LiverLAB). In this work, we show free-breathing abdominal MRF (T1, T2) with T2* results that are quantitatively comparable to current breath-hold MRF and clinical techniques.
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Affiliation(s)
- Madison E Kretzler
- Dept. of Radiology, Case Western Reserve University, 11100 Euclid Ave / Bolwell B115, Cleveland, OH, 44106, USA.
| | - Sherry S Huang
- Dept. of Radiology, Case Western Reserve University, 11100 Euclid Ave / Bolwell B115, Cleveland, OH, 44106, USA
| | - Jessie E P Sun
- Dept. of Radiology, Case Western Reserve University, 11100 Euclid Ave / Bolwell B115, Cleveland, OH, 44106, USA
| | | | - Yong Chen
- Dept. of Radiology, Case Western Reserve University, 11100 Euclid Ave / Bolwell B115, Cleveland, OH, 44106, USA
| | - Mark A Griswold
- Dept. of Radiology, Case Western Reserve University, 11100 Euclid Ave / Bolwell B115, Cleveland, OH, 44106, USA
| | - Rasim Boyacioglu
- Dept. of Radiology, Case Western Reserve University, 11100 Euclid Ave / Bolwell B115, Cleveland, OH, 44106, USA
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McTavish S, Van AT, Peeters JM, Weiss K, Harder FN, Makowski MR, Braren RF, Karampinos DC. Partial Fourier in the presence of respiratory motion in prostate diffusion-weighted echo planar imaging. MAGMA (NEW YORK, N.Y.) 2024; 37:621-636. [PMID: 38743376 PMCID: PMC11417066 DOI: 10.1007/s10334-024-01162-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 03/05/2024] [Accepted: 04/24/2024] [Indexed: 05/16/2024]
Abstract
PURPOSE To investigate the effect of respiratory motion in terms of signal loss in prostate diffusion-weighted imaging (DWI), and to evaluate the usage of partial Fourier in a free-breathing protocol in a clinically relevant b-value range using both single-shot and multi-shot acquisitions. METHODS A controlled breathing DWI acquisition was first employed at 3 T to measure signal loss from deep breathing patterns. Single-shot and multi-shot (2-shot) acquisitions without partial Fourier (no pF) and with partial Fourier (pF) factors of 0.75 and 0.65 were employed in a free-breathing protocol. The apparent SNR and ADC values were evaluated in 10 healthy subjects to measure if low pF factors caused low apparent SNR or overestimated ADC. RESULTS Controlled breathing experiments showed a difference in signal coefficient of variation between shallow and deep breathing. In free-breathing single-shot acquisitions, the pF 0.65 scan showed a significantly (p < 0.05) higher apparent SNR than pF 0.75 and no pF in the peripheral zone (PZ) of the prostate. In the multi-shot acquisitions in the PZ, pF 0.75 had a significantly higher apparent SNR than 0.65 pF and no pF. The single-shot pF 0.65 scan had a significantly lower ADC than single-shot no pF. CONCLUSION Deep breathing patterns can cause intravoxel dephasing in prostate DWI. For single-shot acquisitions at a b-value of 800 s/mm2, any potential risks of motion-related artefacts at low pF factors (pF 0.65) were outweighed by the increase in signal from a lower TE, as shown by the increase in apparent SNR. In multi-shot acquisitions however, the minimum pF factor should be larger, as shown by the lower apparent SNR at low pF factors.
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Affiliation(s)
- Sean McTavish
- Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany.
| | - Anh T Van
- Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
| | | | | | - Felix N Harder
- Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
| | - Marcus R Makowski
- Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
| | - Rickmer F Braren
- Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
| | - Dimitrios C Karampinos
- Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
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Huang H, Liu B, Xu Y, Zhou W. Synthetic-to-real domain adaptation with deep learning for fitting the intravoxel incoherent motion model of diffusion-weighted imaging. Med Phys 2023; 50:1614-1622. [PMID: 36308503 DOI: 10.1002/mp.16031] [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: 04/04/2022] [Revised: 10/03/2022] [Accepted: 10/03/2022] [Indexed: 01/15/2023] Open
Abstract
BACKGROUND Intravoxel incoherent motion (IVIM) is a type of diffusion-weighted imaging (DWI), and IVIM model parameters (water molecule diffusion rate Dt , pseudo-diffusion coefficient Dp , and tissue perfusion fraction Fp ) have been widely used in the diagnosis and characterization of malignant lesions. PURPOSE This study proposes a deep-learning model with synthetic-to-real domain adaptation to fit the IVIM model parameters of DWI. METHODS Ninety-eight consecutive patients diagnosed with hepatocellular carcinoma between January 2017 and September 2020 were included in the study, and routine IVIM-DWI serial examinations were performed using a 3.0 T magnetic resonance imaging system in preoperative MR imaging. The proposed method is mainly composed of two modules: a convolutional neural network-based IVIM model fitting network to map b-value images to the IVIM parameter maps and a domain discriminator to improve the accuracy of the IVIM parameter maps in the real data. The proposed method was compared with previously reported fitting methods, including the nonlinear least squares (NLSs), IVIM-NEToptim , and self-supervised U-network methods. The IVIM parameter-fitting performance was assessed by measuring the DWI reconstruction performance and testing the robustness of each method against noise using noise-corrupted data. RESULTS The DWI reconstruction performance demonstrates that the proposed method has better reconstruction accuracy for DWI with a low signal-to-noise ratio, which implies that the proposed method improves the fitting accuracy of the IVIM parameters. Noise-corrupt experiments show that the proposed method is more robust against noise-corrupted signals. With the proposed method, no outliers were found in Dt , and outliers were reduced for Fp in the abnormal regions (proposed method: 1.85%; NLS: 5.90%; IVIM-NEToptim : 6.61%; and self-U-net: 25.36%). Moreover, experiments show that the proposed method has a more stable parameter estimation performance than the existing methods in the absence of real data. CONCLUSIONS IVIM parameters can be estimated using a synthetic-to-real domain-adaptation framework with deep learning, and the proposed method outperforms previously reported methods.
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Affiliation(s)
- Haoyuan Huang
- School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Baoer Liu
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Yikai Xu
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Wu Zhou
- School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, China
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Gadjimuradov F, Benkert T, Nickel MD, Führes T, Saake M, Maier A. Deep Learning-Guided Weighted Averaging for Signal Dropout Compensation in DWI of the Liver. Magn Reson Med 2022; 88:2679-2693. [PMID: 35916385 DOI: 10.1002/mrm.29380] [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: 02/21/2022] [Revised: 06/03/2022] [Accepted: 06/15/2022] [Indexed: 11/11/2022]
Abstract
PURPOSE To develop an algorithm for the retrospective correction of signal dropout artifacts in abdominal DWI resulting from cardiac motion. METHODS Given a set of image repetitions for a slice, a locally adaptive weighted averaging is proposed that aims to suppress the contribution of image regions affected by signal dropouts. Corresponding weight maps were estimated by a sliding-window algorithm, which analyzed signal deviations from a patch-wise reference. In order to ensure the computation of a robust reference, repetitions were filtered by a classifier that was trained to detect images corrupted by signal dropouts. The proposed method, named Deep Learning-guided Adaptive Weighted Averaging (DLAWA), was evaluated in terms of dropout suppression capability, bias reduction in the ADC, and noise characteristics. RESULTS In the case of uniform averaging, motion-related dropouts caused signal attenuation and ADC overestimation in parts of the liver, with the left lobe being affected particularly. Both effects could be substantially mitigated by DLAWA while preventing global penalties with respect to SNR due to local signal suppression. Performing evaluations on patient data, the capability to recover lesions concealed by signal dropouts was demonstrated as well. Further, DLAWA allowed for transparent control of the trade-off between SNR and signal dropout suppression by means of a few hyperparameters. CONCLUSION This work presents an effective and flexible method for the local compensation of signal dropouts resulting from motion and pulsation. Because DLAWA follows a retrospective approach, no changes to the acquisition are required.
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Affiliation(s)
- Fasil Gadjimuradov
- Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.,MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Thomas Benkert
- MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | | | - Tobit Führes
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Marc Saake
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Andreas Maier
- Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
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Liu B, Zeng Q, Huang J, Zhang J, Zheng Z, Liao Y, Deng K, Zhou W, Xu Y. IVIM using convolutional neural networks predicts microvascular invasion in HCC. Eur Radiol 2022; 32:7185-7195. [PMID: 35713662 DOI: 10.1007/s00330-022-08927-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 05/13/2022] [Accepted: 05/19/2022] [Indexed: 12/11/2022]
Abstract
OBJECTIVES The study aimed to investigate the diagnostic performance of intravoxel incoherent motion (IVIM) diffusion-weighted magnetic resonance imaging for prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) using convolutional neural networks (CNNs). METHODS This retrospective study included 114 patients with pathologically confirmed HCC from December 2014 to August 2021. All patients underwent MRI examination including IVIM sequence with 9 b-values preoperatively. First, 9 b-value images were superimposed in the channel dimension, and a b-value volume with a shape of 32 × 32 × 9 dimension was obtained. Secondly, an image resampling method was performed for data augmentation to generate more samples for training. Finally, deep features to predict MVI in HCC were directly derived from a b-value volume based on the CNN. Moreover, a deep learning model based on parameter maps and a fusion model combined with deep features of IVIM, clinical characteristics, and IVIM parameters were also constructed. Receiver operating characteristic (ROC) curve analysis was performed to assess the diagnostic performance for MVI prediction in HCC. RESULTS Deep features directly extracted from IVIM-DWI (0.810 (range 0.760, 0.829)) using CNN yielded better performance for prediction of MVI than those from IVIM parameter maps (0.590 (range 0.555, 0.643)). Furthermore, the performance of the fusion model combined with deep features of IVIM-DWI, clinical features (α-fetoprotein (AFP) level and tumor size), and apparent diffusion coefficient (ADC) (0.829 (range 0.776, 0.848)) was slightly improved. CONCLUSIONS Deep learning with CNN based on IVIM-DWI can be conducive to preoperative prediction of MVI in patients with HCC. KEY POINTS • Deep learning assessment of IVIM data for prediction of MVI in HCC can overcome the unstable and low performance of IVIM parameters. • Deep learning model based on IVIM performs better than parameter values, clinical features, and deep learning model based on parameter maps. • The fusion model combined with deep features of IVIM, clinical characteristics, and ADC yields better performance for prediction of MVI than the model only based on IVIM.
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Affiliation(s)
- Baoer Liu
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, No.1838 Guangzhou Avenue North, Guangzhou, 510515, People's Republic of China
| | - Qingyuan Zeng
- School of Medical Information Engineering, Guangzhou University of Chinese Medicine, 232 Wide Ring East Road, Panyu District, Guangzhou, 510006, People's Republic of China
| | - Jianbin Huang
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, No.1838 Guangzhou Avenue North, Guangzhou, 510515, People's Republic of China
| | - Jing Zhang
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, No.1838 Guangzhou Avenue North, Guangzhou, 510515, People's Republic of China
| | - Zeyu Zheng
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, No.1838 Guangzhou Avenue North, Guangzhou, 510515, People's Republic of China
| | - Yuting Liao
- GE Healthcare, 10/F, GE Tower, No.87 Hua Cheng Avenue, Pearl River New City, Tianhe District, Guangzhou, 510623, People's Republic of China
| | - Kan Deng
- Philips Healthcare, 18F, Block B, China International Center, No.33 Zhongshan 3rd Road, Guangzhou, 510055, People's Republic of China
| | - Wu Zhou
- School of Medical Information Engineering, Guangzhou University of Chinese Medicine, 232 Wide Ring East Road, Panyu District, Guangzhou, 510006, People's Republic of China.
| | - Yikai Xu
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, No.1838 Guangzhou Avenue North, Guangzhou, 510515, People's Republic of China.
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