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Jacobs L, Mandija S, Liu H, van den Berg CAT, Sbrizzi A, Maspero M. Generalizable synthetic MRI with physics-informed convolutional networks. Med Phys 2024; 51:3348-3359. [PMID: 38063208 DOI: 10.1002/mp.16884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 11/20/2023] [Accepted: 11/28/2023] [Indexed: 05/08/2024] Open
Abstract
BACKGROUND Magnetic resonance imaging (MRI) provides state-of-the-art image quality for neuroimaging, consisting of multiple separately acquired contrasts. Synthetic MRI aims to accelerate examinations by synthesizing any desirable contrast from a single acquisition. PURPOSE We developed a physics-informed deep learning-based method to synthesize multiple brain MRI contrasts from a single 5-min acquisition and investigate its ability to generalize to arbitrary contrasts. METHODS A dataset of 55 subjects acquired with a clinical MRI protocol and a 5-min transient-state sequence was used. The model, based on a generative adversarial network, maps data acquired from the five-minute scan to "effective" quantitative parameter maps (q*-maps), feeding the generated PD, T1, and T2 maps into a signal model to synthesize four clinical contrasts (proton density-weighted, T1-weighted, T2-weighted, and T2-weighted fluid-attenuated inversion recovery), from which losses are computed. The synthetic contrasts are compared to an end-to-end deep learning-based method proposed by literature. The generalizability of the proposed method is investigated for five volunteers by synthesizing three contrasts unseen during training and comparing these to ground truth acquisitions via qualitative assessment and contrast-to-noise ratio (CNR) assessment. RESULTS The physics-informed method matched the quality of the end-to-end method for the four standard contrasts, with structural similarity metrics above0.75 ± 0.08 $0.75\pm 0.08$ ( ± $\pm$ std), peak signal-to-noise ratios above22.4 ± 1.9 $22.4\pm 1.9$ , representing a portion of compact lesions comparable to standard MRI. Additionally, the physics-informed method enabled contrast adjustment, and similar signal contrast and comparable CNRs to the ground truth acquisitions for three sequences unseen during model training. CONCLUSIONS The study demonstrated the feasibility of physics-informed, deep learning-based synthetic MRI to generate high-quality contrasts and generalize to contrasts beyond the training data. This technology has the potential to accelerate neuroimaging protocols.
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Affiliation(s)
- Luuk Jacobs
- Department of Radiotherapy, UMC Utrecht, Utrecht, The Netherlands
- Computational Imaging Group for MR Diagnostics and Therapy, UMC Utrecht, Utrecht, The Netherlands
| | - Stefano Mandija
- Department of Radiotherapy, UMC Utrecht, Utrecht, The Netherlands
- Computational Imaging Group for MR Diagnostics and Therapy, UMC Utrecht, Utrecht, The Netherlands
| | - Hongyan Liu
- Department of Radiotherapy, UMC Utrecht, Utrecht, The Netherlands
- Computational Imaging Group for MR Diagnostics and Therapy, UMC Utrecht, Utrecht, The Netherlands
| | - Cornelis A T van den Berg
- Department of Radiotherapy, UMC Utrecht, Utrecht, The Netherlands
- Computational Imaging Group for MR Diagnostics and Therapy, UMC Utrecht, Utrecht, The Netherlands
| | - Alessandro Sbrizzi
- Department of Radiotherapy, UMC Utrecht, Utrecht, The Netherlands
- Computational Imaging Group for MR Diagnostics and Therapy, UMC Utrecht, Utrecht, The Netherlands
| | - Matteo Maspero
- Department of Radiotherapy, UMC Utrecht, Utrecht, The Netherlands
- Computational Imaging Group for MR Diagnostics and Therapy, UMC Utrecht, Utrecht, The Netherlands
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Akdag O, Borman PTS, Mandija S, Woodhead PL, Uijtewaal P, Raaymakers BW, Fast MF. Experimental demonstration of real-time cardiac physiology-based radiotherapy gating for improved cardiac radioablation on an MR-linac. Med Phys 2024; 51:2354-2366. [PMID: 38477841 DOI: 10.1002/mp.17024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 02/09/2024] [Accepted: 02/23/2024] [Indexed: 03/14/2024] Open
Abstract
BACKGROUND Cardiac radioablation is a noninvasive stereotactic body radiation therapy (SBRT) technique to treat patients with refractory ventricular tachycardia (VT) by delivering a single high-dose fraction to the VT isthmus. Cardiorespiratory motion induces position uncertainties resulting in decreased dose conformality. Electocardiograms (ECG) are typically used during cardiac MRI (CMR) to acquire images in a predefined cardiac phase, thus mitigating cardiac motion during image acquisition. PURPOSE We demonstrate real-time cardiac physiology-based radiotherapy beam gating within a preset cardiac phase on an MR-linac. METHODS MR images were acquired in healthy volunteers (n = 5, mean age = 29.6 years, mean heart-rate (HR) = 56.2 bpm) on the 1.5 T Unity MR-linac (Elekta AB, Stockholm, Sweden) after obtaining written informed consent. The images were acquired using a single-slice balance steady-state free precession (bSSFP) sequence in the coronal or sagittal plane (TR/TE = 3/1.48 ms, flip angle = 48∘ $^{\circ }$ , SENSE = 1.5,field-of-view = 400 × 207 $\text{field-of-view} = {400}\times {207}$ mm 2 ${\text{mm}}^{2}$ , voxel size =3 × 3 × 15 $3\times 3\times 15$ mm 3 ${\rm mm}^{3}$ , partial Fourier factor = 0.65, frame rate = 13.3 Hz). In parallel, a 4-lead ECG-signal was acquired using MR-compatible equipment. The feasibility of ECG-based beam gating was demonstrated with a prototype gating workflow using a Quasar MRI4D motion phantom (IBA Quasar, London, ON, Canada), which was deployed in the bore of the MR-linac. Two volunteer-derived combined ECG-motion traces (n = 2, mean age = 26 years, mean HR = 57.4 bpm, peak-to-peak amplitude = 14.7 mm) were programmed into the phantom to mimic dose delivery on a cardiac target in breath-hold. Clinical ECG-equipment was connected to the phantom for ECG-voltage-streaming in real-time using research software. Treatment beam gating was performed in the quiescent phase (end-diastole). System latencies were compensated by delay time correction. A previously developed MRI-based gating workflow was used as a benchmark in this study. A 15-beam intensity-modulated radiotherapy (IMRT) plan (1 × 6.25 ${1}\times {6.25}$ Gy) was delivered for different motion scenarios onto radiochromic films. Next, cardiac motion was then estimated at the basal anterolateral myocardial wall via normalized cross-correlation-based template matching. The estimated motion signal was temporally aligned with the ECG-signal, which were then used for position- and ECG-based gating simulations in the cranial-caudal (CC), anterior-posterior (AP), and right-left (RL) directions. The effect of gating was investigated by analyzing the differences in residual motion at 30, 50, and 70% treatment beam duty cycles. RESULTS ECG-based (MRI-based) beam gating was performed with effective duty cycles of 60.5% (68.8%) and 47.7% (50.4%) with residual motion reductions of 62.5% (44.7%) and 43.9% (59.3%). Local gamma analyses (1%/1 mm) returned pass rates of 97.6% (94.1%) and 90.5% (98.3%) for gated scenarios, which exceed the pass rates of 70.3% and 82.0% for nongated scenarios, respectively. In average, the gating simulations returned maximum residual motion reductions of 88%, 74%, and 81% at 30%, 50%, and 70% duty cycles, respectively, in favor of MRI-based gating. CONCLUSIONS Real-time ECG-based beam gating is a feasible alternative to MRI-based gating, resulting in improved dose delivery in terms of highγ -pass $\gamma {\text{-pass}}$ rates, decreased dose deposition outside the PTV and residual motion reduction, while by-passing cardiac MRI challenges.
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Affiliation(s)
- Osman Akdag
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Pim T S Borman
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Stefano Mandija
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
- Computational Imaging Group for MR Diagnostics and Therapy, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Peter L Woodhead
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
- Elekta AB, Stockholm, Sweden
| | - Prescilla Uijtewaal
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Bas W Raaymakers
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Martin F Fast
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
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Meerbothe TG, Meliado EF, Stijnman PRS, van den Berg CAT, Mandija S. A database for MR-based electrical properties tomography with in silico brain data-ADEPT. Magn Reson Med 2024; 91:1190-1199. [PMID: 37876351 DOI: 10.1002/mrm.29904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 10/06/2023] [Accepted: 10/06/2023] [Indexed: 10/26/2023]
Abstract
PURPOSE Several reconstruction methods for MR-based electrical properties tomography (EPT) have been developed. However, the lack of common data makes it difficult to objectively compare their performances. This is, however, a necessary precursor for standardizing and introducing this technique in the clinical setting. To enable objective comparison of the performances of reconstruction methods and provide common data for their training and testing, we created ADEPT, a database of simulated data for brain MR-EPT reconstructions. METHODS ADEPT is a database containing in silico data for brain EPT reconstructions. This database was created from 25 different brain models, with and without tumors. Rigid geometric augmentations were applied, and different electrical properties were assigned to white matter, gray matter, CSF, and tumors to generate 120 different brain models. These models were used as input for finite-difference time-domain simulations in Sim4Life, used to compute the electromagnetic fields needed for MR-EPT reconstructions. RESULTS Electromagnetic fields from 84 healthy and 36 tumor brain models were simulated. The simulated fields relevant for MR-EPT reconstructions (transmit and receive RF fields and transceive phase) and their ground-truth electrical properties are made publicly available through ADEPT. Additionally, nonattainable fields such as the total magnetic field and the electric field are available upon request. CONCLUSION ADEPT will serve as reference database for objective comparisons of reconstruction methods and will be a first step toward standardization of MR-EPT reconstructions. Furthermore, it provides a large amount of data that can be exploited to train data-driven methods. It can be accessed from https://doi.org/10.34894/V0HBJ8.
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Affiliation(s)
- T G Meerbothe
- Department of Radiotherapy, Division of Imaging and Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
- Computational Imaging Group for MR Therapy and Diagnostics, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
| | - E F Meliado
- Department of Radiotherapy, Division of Imaging and Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
- Computational Imaging Group for MR Therapy and Diagnostics, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
| | - P R S Stijnman
- Department of Radiotherapy, Division of Imaging and Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
- Computational Imaging Group for MR Therapy and Diagnostics, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
| | - C A T van den Berg
- Department of Radiotherapy, Division of Imaging and Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
- Computational Imaging Group for MR Therapy and Diagnostics, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
| | - S Mandija
- Department of Radiotherapy, Division of Imaging and Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
- Computational Imaging Group for MR Therapy and Diagnostics, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
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Stevens RRF, Hazelaar C, Bogowicz M, Ter Bekke RMA, Volders PGA, Verhoeven K, de Ruysscher D, Verhoeff JJC, Fast MF, Mandija S, Cvek J, Knybel L, Dvorak P, Blanck O, van Elmpt W. A Framework for Assessing the Effect of Cardiac and Respiratory Motion for Stereotactic Arrhythmia Radioablation Using a Digital Phantom With a 17-Segment Model: A STOPSTORM.eu Consortium Study. Int J Radiat Oncol Biol Phys 2024; 118:533-542. [PMID: 37652302 DOI: 10.1016/j.ijrobp.2023.08.059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 08/14/2023] [Accepted: 08/22/2023] [Indexed: 09/02/2023]
Abstract
PURPOSE The optimal motion management strategy for patients receiving stereotactic arrhythmia radioablation (STAR) for the treatment of ventricular tachycardia (VT) is not fully known. We developed a framework using a digital phantom to simulate cardiorespiratory motion in combination with different motion management strategies to gain insight into the effect of cardiorespiratory motion on STAR. METHODS AND MATERIALS The 4-dimensional (4D) extended cardiac-torso (XCAT) phantom was expanded with the 17-segment left ventricular (LV) model, which allowed placement of STAR targets in standardized ventricular regions. Cardiac- and respiratory-binned 4D computed tomography (CT) scans were simulated for free-breathing, reduced free-breathing, respiratory-gating, and breath-hold scenarios. Respiratory motion of the heart was set to population-averaged values of patients with VT: 6, 2, and 1 mm in the superior-inferior, posterior-anterior, and left-right direction, respectively. Cardiac contraction was adjusted by reducing LV ejection fraction to 35%. Target displacement was evaluated for all segments using envelopes encompassing the cardiorespiratory motion. Envelopes incorporating only the diastole plus respiratory motion were created to simulate the scenario where cardiac motion is not fully captured on 4D respiratory CT scans used for radiation therapy planning. RESULTS The average volume of the 17 segments was 6 cm3 (1-9 cm3). Cardiac contraction-relaxation resulted in maximum segment (centroid) motion of 4, 6, and 3.5 mm in the superior-inferior, posterior-anterior, and left-right direction, respectively. Cardiac contraction-relaxation resulted in a motion envelope increase of 49% (24%-79%) compared with individual segment volumes, whereas envelopes increased by 126% (79%-167%) if respiratory motion also was considered. Envelopes incorporating only the diastole and respiration motion covered on average 68% to 75% of the motion envelope. CONCLUSIONS The developed LV-segmental XCAT framework showed that free-wall regions display the most cardiorespiratory displacement. Our framework supports the optimization of STAR by evaluating the effect of (cardio)respiratory motion and motion management strategies for patients with VT.
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Affiliation(s)
- Raoul R F Stevens
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, The Netherlands.
| | - Colien Hazelaar
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Marta Bogowicz
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Rachel M A Ter Bekke
- Department of Cardiology, Cardiovascular Research Institute Maastricht, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Paul G A Volders
- Department of Cardiology, Cardiovascular Research Institute Maastricht, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Karolien Verhoeven
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Dirk de Ruysscher
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, The Netherlands
| | - Joost J C Verhoeff
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Martin F Fast
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Stefano Mandija
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Jakub Cvek
- Department of Oncology, University Hospital and Faculty of Medicine, Ostrava, Czech Republic
| | - Lukas Knybel
- Department of Oncology, University Hospital and Faculty of Medicine, Ostrava, Czech Republic
| | - Pavel Dvorak
- Department of Oncology, University Hospital and Faculty of Medicine, Ostrava, Czech Republic
| | - Oliver Blanck
- Department of Radiation Oncology, University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Wouter van Elmpt
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Center+, Maastricht, The Netherlands
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Stevens RRF, Hazelaar C, Fast MF, Mandija S, Grehn M, Cvek J, Knybel L, Dvorak P, Pruvot E, Verhoeff JJC, Blanck O, van Elmpt W. Stereotactic Arrhythmia Radioablation (STAR): Assessment of cardiac and respiratory heart motion in ventricular tachycardia patients - A STOPSTORM.eu consortium review. Radiother Oncol 2023; 188:109844. [PMID: 37543057 DOI: 10.1016/j.radonc.2023.109844] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 07/10/2023] [Accepted: 07/28/2023] [Indexed: 08/07/2023]
Abstract
AIM To identify the optimal STereotactic Arrhythmia Radioablation (STAR) strategy for individual patients, cardiorespiratory motion of the target volume in combination with different treatment methodologies needs to be evaluated. However, an authoritative overview of the amount of cardiorespiratory motion in ventricular tachycardia (VT) patients is missing. METHODS In this STOPSTORM consortium study, we performed a literature review to gain insight into cardiorespiratory motion of target volumes for STAR. Motion data and target volumes were extracted and summarized. RESULTS Out of the 232 studies screened, 56 provided data on cardiorespiratory motion, of which 8 provided motion amplitudes in VT patients (n = 94) and 10 described (cardiac/cardiorespiratory) internal target volumes (ITVs) obtained in VT patients (n = 59). Average cardiac motion of target volumes was < 5 mm in all directions, with maximum values of 8.0, 5.2 and 6.5 mm in Superior-Inferior (SI), Left-Right (LR), Anterior-Posterior (AP) direction, respectively. Cardiorespiratory motion of cardiac (sub)structures showed average motion between 5-8 mm in the SI direction, whereas, LR and AP motions were comparable to the cardiac motion of the target volumes. Cardiorespiratory ITVs were on average 120-284% of the gross target volume. Healthy subjects showed average cardiorespiratory motion of 10-17 mm in SI and 2.4-7 mm in the AP direction. CONCLUSION This review suggests that despite growing numbers of patients being treated, detailed data on cardiorespiratory motion for STAR is still limited. Moreover, data comparison between studies is difficult due to inconsistency in parameters reported. Cardiorespiratory motion is highly patient-specific even under motion-compensation techniques. Therefore, individual motion management strategies during imaging, planning, and treatment for STAR are highly recommended.
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Affiliation(s)
- Raoul R F Stevens
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, the Netherlands.
| | - Colien Hazelaar
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, the Netherlands
| | - Martin F Fast
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Stefano Mandija
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Melanie Grehn
- Department of Radiation Oncology, University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Jakub Cvek
- Department of Oncology, University Hospital and Faculty of Medicine, Ostrava, Czech Republic
| | - Lukas Knybel
- Department of Oncology, University Hospital and Faculty of Medicine, Ostrava, Czech Republic
| | - Pavel Dvorak
- Department of Oncology, University Hospital and Faculty of Medicine, Ostrava, Czech Republic
| | - Etienne Pruvot
- Heart and Vessel Department, Service of Cardiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Joost J C Verhoeff
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Oliver Blanck
- Department of Radiation Oncology, University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Wouter van Elmpt
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, the Netherlands
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Jung K, Mandija S, Cui C, Kim J, Al‐masni MA, Meerbothe TG, Park M, van den Berg CAT, Kim D. Data-driven electrical conductivity brain imaging using 3 T MRI. Hum Brain Mapp 2023; 44:4986-5001. [PMID: 37466309 PMCID: PMC10502651 DOI: 10.1002/hbm.26421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 06/14/2023] [Accepted: 07/03/2023] [Indexed: 07/20/2023] Open
Abstract
Magnetic resonance electrical properties tomography (MR-EPT) is a non-invasive measurement technique that derives the electrical properties (EPs, e.g., conductivity or permittivity) of tissues in the radiofrequency range (64 MHz for 1.5 T and 128 MHz for 3 T MR systems). Clinical studies have shown the potential of tissue conductivity as a biomarker. To date, model-based conductivity reconstructions rely on numerical assumptions and approximations, leading to inaccuracies in the reconstructed maps. To address such limitations, we propose an artificial neural network (ANN)-based non-linear conductivity estimator trained on simulated data for conductivity brain imaging. Network training was performed on 201 synthesized T2-weighted spin-echo (SE) data obtained from the finite-difference time-domain (FDTD) electromagnetic (EM) simulation. The dataset was composed of an approximated T2-w SE magnitude and transceive phase information. The proposed method was tested three in-silico and in-vivo on two volunteers and three patients' data. For comparison purposes, various conventional phase-based EPT reconstruction methods were used that ignoreB 1 + magnitude information, such as Savitzky-Golay kernel combined with Gaussian filter (S-G Kernel), phase-based convection-reaction EPT (cr-EPT), magnitude-weighted polynomial-fitting phase-based EPT (Poly-Fit), and integral-based phase-based EPT (Integral-based). From the in-silico experiments, quantitative analysis showed that the proposed method provides more accurate and improved quality (e.g., high structural preservation) conductivity maps compared to conventional reconstruction methods. Representatively, in the healthy brain in-silico phantom experiment, the proposed method yielded mean conductivity values of 1.97 ± 0.20 S/m for CSF, 0.33 ± 0.04 S/m for WM, and 0.52 ± 0.08 S/m for GM, which were closer to the ground-truth conductivity (2.00, 0.30, 0.50 S/m) than the integral-based method (2.56 ± 2.31, 0.39 ± 0.12, 0.68 ± 0.33 S/m). In-vivo ANN-based conductivity reconstructions were also of improved quality compared to conventional reconstructions and demonstrated network generalizability and robustness to in-vivo data and pathologies. The reported in-vivo brain conductivity values were in agreement with literatures. In addition, the proposed method was observed for various SNR levels (SNR levels = 10, 20, 40, and 58) and repeatability conditions (the eight acquisitions with the number of signal averages = 1). The preliminary investigations on brain tumor patient datasets suggest that the network trained on simulated dataset can generalize to unforeseen in-vivo pathologies, thus demonstrating its potential for clinical applications.
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Affiliation(s)
- Kyu‐Jin Jung
- Department of Electrical and Electronic EngineeringYonsei UniversitySeoulRepublic of Korea
| | - Stefano Mandija
- Computational Imaging Group for MR Therapy and DiagnosticsUniversity Medical Center UtrechtUtrechtThe Netherlands
- Department of RadiotherapyUniversity Medical Center UtrechtUtrechtThe Netherlands
| | - Chuanjiang Cui
- Department of Electrical and Electronic EngineeringYonsei UniversitySeoulRepublic of Korea
| | - Jun‐Hyeong Kim
- Department of Electrical and Electronic EngineeringYonsei UniversitySeoulRepublic of Korea
| | - Mohammed A. Al‐masni
- Department of Artificial IntelligenceCollege of Software & Convergence Technology, Daeyang AI Center, Sejong UniversitySeoulRepublic of Korea
| | - Thierry G. Meerbothe
- Computational Imaging Group for MR Therapy and DiagnosticsUniversity Medical Center UtrechtUtrechtThe Netherlands
- Department of RadiotherapyUniversity Medical Center UtrechtUtrechtThe Netherlands
| | - Mina Park
- Department of Radiology, Gangnam Severance HospitalYonsei University College of MedicineSeoulRepublic of Korea
| | - Cornelis A. T. van den Berg
- Computational Imaging Group for MR Therapy and DiagnosticsUniversity Medical Center UtrechtUtrechtThe Netherlands
- Department of RadiotherapyUniversity Medical Center UtrechtUtrechtThe Netherlands
| | - Dong‐Hyun Kim
- Department of Electrical and Electronic EngineeringYonsei UniversitySeoulRepublic of Korea
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Huttinga NRF, Akdag O, Fast MF, Verhoeff JJC, Mohamed Hoesein FAA, Van den Berg CAT, Sbrizzi A, Mandija S. Real-time myocardial landmark tracking for MRI-guided cardiac radio-ablation using Gaussian Processes. Phys Med Biol 2023. [PMID: 37339638 DOI: 10.1088/1361-6560/ace023] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/22/2023]
Abstract
The high speed of cardiorespiratory motion introduces a unique challenge for cardiac stereotactic radio-ablation (STAR) treatments with the MR-linac. Such treatments require tracking myocardial landmarks with a maximum latency of 100 ms, which includes the acquisition of the required data. The aim of this study is to present a new method that enables tracking myocardial landmarks from few readouts of MRI data, thereby achieving a latency sufficient for STAR treatments. We present a tracking framework that requires few readouts of k-space data as input, which can be acquired at least an order of magnitude faster than MR-images. Combined with the real-time tracking speed of a probabilistic machine learning framework called Gaussian Processes, this allows to track myocardial landmarks with a sufficiently low latency for cardiac STAR guidance. This includes both the acquisition of required data, and the tracking inference. The framework is demonstrated in 2D on a motion phantom, and in vivo on volunteers and a ventricular tachycardia (arrhythmia) patient. Moreover, the feasibility of an extension to 3D was demonstrated by in silico 3D experiments with a digital motion phantom. The framework was compared with template matching - a reference, image-based, method - and linear regression methods. Results indicate an order of magnitude lower total latency (<10 ms) for the proposed framework in comparison with alternative methods. The root-mean-square-distances and mean end-point-distance with the reference tracking method was less than 0.8 mm for all experiments, showing excellent (sub-voxel) agreement. The high accuracy in combination with a total latency of less than 10 ms - including data acquisition and processing - make the proposed method a suitable candidate for tracking during STAR treatments. Additionally, the probabilistic nature of the Gaussian Processes also gives access to real-time prediction uncertainties, which could prove useful for real-time quality assurance during treatments.
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Affiliation(s)
- Niek Ricardo Ferdinand Huttinga
- Department of Radiotherapy, Universitair Medisch Centrum Utrecht, Heidelberglaan 100, Utrecht, Utrecht, 3508 GA, NETHERLANDS
| | - Osman Akdag
- Department of Radiotherapy, Universitair Medisch Centrum Utrecht, Heidelberglaan 100, Utrecht, Utrecht, 3508 GA, NETHERLANDS
| | - Martin F Fast
- Department of Radiotherapy, Universitair Medisch Centrum Utrecht, Heidelberglaan 100, Utrecht, Utrecht, 3508 GA, NETHERLANDS
| | - Joost J C Verhoeff
- Department of Radiotherapy, Universitair Medisch Centrum Utrecht, Heidelberglaan 100, Utrecht, Utrecht, 3508 GA, NETHERLANDS
| | - Firdaus A A Mohamed Hoesein
- Department of Radiology, Universitair Medisch Centrum Utrecht, Heidelberglaan 100, Utrecht, Utrecht, 3508 GA, NETHERLANDS
| | - Cornelis A T Van den Berg
- Department of Radiotherapy, Universitair Medisch Centrum Utrecht, Heidelberglaan 100, Utrecht, Utrecht, 3508 GA, NETHERLANDS
| | - Alessandro Sbrizzi
- Department of Radiotherapy, Universitair Medisch Centrum Utrecht, Heidelberglaan 100, Utrecht, Utrecht, 3508 GA, NETHERLANDS
| | - Stefano Mandija
- Department of Radiotherapy, Universitair Medisch Centrum Utrecht, Heidelberglaan 100, Utrecht, Utrecht, 3508 GA, NETHERLANDS
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Grehn M, Mandija S, Miszczyk M, Krug D, Tomasik B, Stickney KE, Alcantara P, Alongi F, Anselmino M, Aranda RS, Balgobind BV, Boda-Heggemann J, Boldt LH, Bottoni N, Cvek J, Elicin O, De Ferrari GM, Hassink RJ, Hazelaar C, Hindricks G, Hurkmans C, Iotti C, Jadczyk T, Jiravsky O, Jumeau R, Buus Kristiansen S, Levis M, López MA, Martí-Almor J, Mehrhof F, Møller DS, Molon G, Ouss A, Peichl P, Plasek J, Postema PG, Quesada A, Reichlin T, Rordorf R, Rudic B, Saguner AM, Ter Bekke RMA, Torrecilla JL, Troost EGC, Vitolo V, Andratschke N, Zeppenfeld K, Blamek S, Fast M, de Panfilis L, Blanck O, Pruvot E, Verhoeff JJC. STereotactic Arrhythmia Radioablation (STAR): the Standardized Treatment and Outcome Platform for Stereotactic Therapy Of Re-entrant tachycardia by a Multidisciplinary consortium (STOPSTORM.eu) and review of current patterns of STAR practice in Europe. Europace 2023; 25:1284-1295. [PMID: 36879464 PMCID: PMC10105846 DOI: 10.1093/europace/euac238] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 11/18/2022] [Indexed: 03/08/2023] Open
Abstract
The EU Horizon 2020 Framework-funded Standardized Treatment and Outcome Platform for Stereotactic Therapy Of Re-entrant tachycardia by a Multidisciplinary (STOPSTORM) consortium has been established as a large research network for investigating STereotactic Arrhythmia Radioablation (STAR) for ventricular tachycardia (VT). The aim is to provide a pooled treatment database to evaluate patterns of practice and outcomes of STAR and finally to harmonize STAR within Europe. The consortium comprises 31 clinical and research institutions. The project is divided into nine work packages (WPs): (i) observational cohort; (ii) standardization and harmonization of target delineation; (iii) harmonized prospective cohort; (iv) quality assurance (QA); (v) analysis and evaluation; (vi, ix) ethics and regulations; and (vii, viii) project coordination and dissemination. To provide a review of current clinical STAR practice in Europe, a comprehensive questionnaire was performed at project start. The STOPSTORM Institutions' experience in VT catheter ablation (83% ≥ 20 ann.) and stereotactic body radiotherapy (59% > 200 ann.) was adequate, and 84 STAR treatments were performed until project launch, while 8/22 centres already recruited VT patients in national clinical trials. The majority currently base their target definition on mapping during VT (96%) and/or pace mapping (75%), reduced voltage areas (63%), or late ventricular potentials (75%) during sinus rhythm. The majority currently apply a single-fraction dose of 25 Gy while planning techniques and dose prescription methods vary greatly. The current clinical STAR practice in the STOPSTORM consortium highlights potential areas of optimization and harmonization for substrate mapping, target delineation, motion management, dosimetry, and QA, which will be addressed in the various WPs.
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Affiliation(s)
- Melanie Grehn
- Department of Radiation Oncology, University Medical Center Schleswig-Holstein, Arnold-Heller-Strasse 3, Kiel 24105, Germany
| | - Stefano Mandija
- Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, Utrecht 3584 CX, The Netherlands
| | - Marcin Miszczyk
- IIIrd Radiotherapy and Chemotherapy Department, Maria Skłodowska-Curie National Research Institute of Oncology, Ul. Wybrzeze Armii Krajowej, Gliwice 44102, Poland
| | - David Krug
- Department of Radiation Oncology, University Medical Center Schleswig-Holstein, Arnold-Heller-Strasse 3, Kiel 24105, Germany
| | - Bartłomiej Tomasik
- Department of Radiotherapy, Maria Skłodowska-Curie National Research Institute of Oncology, Ul. Wybrzeze Armii Krajowej, Gliwice 44102, Poland.,Department of Oncology and Radiotherapy, Faculty of Medicine, Medical University of Gdansk, M. Sklodowskiel-Curie 3a, Gdansk 80210, Poland
| | - Kristine E Stickney
- Research Support Office, University Medical Center Utrecht, Heidelberglaan 100, Utrecht 3584 CX, The Netherlands
| | - Pino Alcantara
- Department of Radiation Oncology, Hospital Clínico San Carlos, Faculty of Medicine, University Complutense of Madrid, Profesor Martin Lagos, Madrid 28040, Spain
| | - Filippo Alongi
- Department of Advanced Radiation Oncology, IRCCS Sacro Cuore Don Calabria Hospital, University of Brescia, Via San Zeno in Monte 23, Verona 37129, Italy
| | - Matteo Anselmino
- Division of Cardiology, Cardiovascular and Thoracic Department, 'Città della Salute e della Scienza' Hospital, Via Giuseppe Verdi 8, Torino 10124, Italy.,Department of Medical Sciences, University of Turin, Via Verdi 8, Torino 10124, Italy
| | - Ricardo Salgado Aranda
- Electrophysiology Unit, Department of Cardiology, Hospital Clínico San Carlos Madrid, Professor Martin Lagos, Madrid 28040, Spain
| | - Brian V Balgobind
- Department of Radiation Oncology, Amsterdam UMC, University of Amsterdam, Meibergdreef 15, Amsterdam 1105AZ, The Netherlands
| | - Judit Boda-Heggemann
- Department of Radiation Oncology, University Medical Center Mannheim, Medical Faculty Mannheim, University of Heidelberg, Theodor-Kutzer-Ufer 1-3, Mannheim 68167, Germany
| | - Leif-Hendrik Boldt
- Department of Rhythmology, Charité-University Medicine Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Nicola Bottoni
- Cardiology Arrhythmology Center, AUSL-IRCCS di Reggio Emilia, Via Amendola 2, Reggio Emilia 42100, Italy
| | - Jakub Cvek
- Department of Oncology, University Hospital and Faculty of Medicine, Listopadu 1790, Ostrava Poruba 70852, Czech Republic
| | - Olgun Elicin
- Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, Bern 3010, Switzerland
| | - Gaetano Maria De Ferrari
- Division of Cardiology, Cardiovascular and Thoracic Department, 'Città della Salute e della Scienza' Hospital, Via Giuseppe Verdi 8, Torino 10124, Italy
| | - Rutger J Hassink
- Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, Utrecht 3584 CX, The Netherlands
| | - Colien Hazelaar
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, P. Debyelaan 25, Maastricht 6229 HX, The Netherlands
| | - Gerhard Hindricks
- Department of Electrophysiology, Heart Center Leipzig, University of Leipzig, Struempellstrasse 39, Leipzig 04289, Germany
| | - Coen Hurkmans
- Department of Radiation Oncology, Catharina Hospital, Michelangelolaan 2, Eindhoven 5623 EJ, The Netherlands
| | - Cinzia Iotti
- Radiation Oncology Unit, Clinical Cancer Centre, AUSL-IRCCS di Reggio Emilia, Via Amendola 2, Reggio Emilia 42100, Italy
| | - Tomasz Jadczyk
- Division of Cardiology and Structural Heart Diseases, Medical University of Silesia, Ul. Poniatowskiego 15, Katowice 40055, Poland.,Interventional Cardiac Electrophysiology Group, International Clinical Research Center, St. Anne's University Hospital Brno, Brno, Czech Republic
| | - Otakar Jiravsky
- Cardiocenter, Hospital Agel Trinec Podlesi and Masaryk University, Konska 453, Trinec 73961, Czech Republic
| | - Raphaël Jumeau
- Department of Radio-Oncology, Lausanne University Hospital, Rue du Bugnon 21, Lausanne 1011, Switzerland
| | - Steen Buus Kristiansen
- Department of Cardiology, Aarhus University Hospital, Palle Juul-Jensens Boulevard 99, Aarhus 8200, Denmark
| | - Mario Levis
- Department of Oncology, University of Torino, Via Giuseppe Verdi 8, Torino 10124, Italy
| | - Manuel Algara López
- Department of Radiation Oncology, Hospital del Mar, Universitat Pompeu Fabra, Institut Hospital del Mar d'Investigacions Mèdiques, Paseo Maritim 25-29, Barcelona 08003, Spain
| | - Julio Martí-Almor
- Department of Cardiology, Hospital del Mar, Universitat Pompeu Fabra, Institut Hospital del Mar d'Investigacions Mèdiques, Paseo Maritim 25-29, Barcelona 08003, Spain
| | - Felix Mehrhof
- Department for Radiation Oncology, Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Ditte Sloth Møller
- Department of Oncology, Aarhus University Hospital, Palle Juul-Jensens Boulevard 99, Aarhus 8200, Denmark
| | - Giulio Molon
- Department of Cardiology, IRCCS Sacro Cuore Don Calabria Hospital, Via San Zeno in Monte 23, Verona 37129, Italy
| | - Alexandre Ouss
- Department of Cardiology, Catharina Hospital, Michelangelolaan 2, Eindhoven 5623 EJ, The Netherlands
| | - Petr Peichl
- Department of Cardiology, Institute for Clinical and Experimental Medicine, Videnska 9, Prague 14000, Czech Republic
| | - Jiri Plasek
- Department of Cardiovascular Medicine, University Hospital Ostrava, Listopadu 1790. Ostrava Poruba 70852, Czech Republic
| | - Pieter G Postema
- Department of Cardiology, Amsterdam UMC, University of Amsterdam, Meibergdreef 15, Amsterdam 1105AZ, The Netherlands
| | - Aurelio Quesada
- Arrhythmia Unit, Department of Cardiology, Consorcio Hospital General Universitario de Valencia, Av Tres Cruces 2, Valencia 46014, Spain
| | - Tobias Reichlin
- Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, Bern 3010, Switzerland
| | - Roberto Rordorf
- Cardiac Intensive Care Unit, Arrhythmia and Electrophysiology and Experimental Cardiology, Fondazione IRCCS Policlinico San Matteo, Camillo Golgi Avenue 5, Pavia 27100, Italy
| | - Boris Rudic
- Department of Medicine I, University Medical Center Mannheim, Medical Faculty Mannheim, University of Heidelberg, Theodor-Kutzer-Ufer 1-3, Mannheim 68167, Germany
| | - Ardan M Saguner
- Arrhythmia Unit, Department of Cardiology, University Hospital Zurich, Ramistrasse 71, Zurich 8006, Switzerland
| | - Rachel M A Ter Bekke
- Department of Cardiology, Maastricht University Medical Center, P. Debyelaan 25, Maastricht 6229 HX, The Netherlands
| | - José López Torrecilla
- Department of Radiation Oncology, Hospital General Valencia, Av Tres Cruces 2, Valencia 46014, Spain
| | - Esther G C Troost
- Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Fetscherstraße 74, Dresden 01307, Germany.,OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus. Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Fetscherstrasse 74, Dresden 01307, Germany.,Institute of Radiooncology - OncoRay, Helmholtz-Zentrum Dresden-Rossendorf, Bautzner Landstr. 400, Dresden 01328, Germany
| | - Viviana Vitolo
- National Center of Oncological Hadrontherapy (Fondazione CNAO), Strada Campeggi 53, Pavia PV27100, Italy
| | - Nicolaus Andratschke
- Department of Radiation Oncology, University Hospital of Zurich, Ramistrasse 71, Zurich 8006, Switzerland
| | - Katja Zeppenfeld
- Unit of Clinical Electrophysiology, Leiden University Medical Center, Albinusdreef 2, Leiden 2333 ZA, The Netherlands
| | - Slawomir Blamek
- Department of Radiotherapy, Maria Skłodowska-Curie National Research Institute of Oncology, Ul. Wybrzeze Armii Krajowej, Gliwice 44102, Poland
| | - Martin Fast
- Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, Utrecht 3584 CX, The Netherlands
| | - Ludovica de Panfilis
- Bioethics Unit, Azienda Unità Sanitaria Locale-IRCCS, Via Amendola 2, Reggio Emilia 42100, Italy
| | - Oliver Blanck
- Department of Radiation Oncology, University Medical Center Schleswig-Holstein, Arnold-Heller-Strasse 3, Kiel 24105, Germany
| | - Etienne Pruvot
- Heart and Vessel Department, Service of Cardiology, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 21, Lausanne 1011, Switzerland
| | - Joost J C Verhoeff
- Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, Utrecht 3584 CX, The Netherlands
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Mandija S, Ma C, Bai R, Feng L, Giganti F, Ianus A, Lee HH, Li F, Welton T, Calamante F. Walking with the ISMRM in the footprints of our MR history. Magn Reson Med 2023; 89:883-885. [PMID: 36353850 DOI: 10.1002/mrm.29488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 09/21/2022] [Indexed: 11/11/2022]
Affiliation(s)
- Stefano Mandija
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Chao Ma
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Ruiliang Bai
- Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, China
| | - Li Feng
- BioMedical Engineering and Imaging Institute (BMEII) and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Francesco Giganti
- Division of Surgery and Interventional Science, Faculty of Medical Sciences, University College London, London, UK
| | - Andrada Ianus
- Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal
| | - Hong-Hsi Lee
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Fei Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Thomas Welton
- National Neuroscience Institute, Singapore & Duke-NUS Graduate Medical School, Singapore, Singapore
| | - Fernando Calamante
- School of Biomedical Engineering and Sydney Imaging, The University of Sydney, Sydney, Australia
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Grehn M, Balgobind BV, Trojani V, Visser J, Botti A, Dolla L, van Elmpt W, Hurkmans C, Schweikard A, Fast M, Mandija S, Both M, Zeppenfeld K, Postema PG, Andratschke N, Miszczyk M, Pruvot E, Verhoeff J, Iori M, Blanck O. PATTERN-OF-PRACTISE, MULTI-CENTRE BENCHMARKS AND CREDENTIALING WORKFLOW FOR CONTOURING, TREATMENT PLANNING AND DELIVERY OF STEREOTACTIC ARRHYTHMIA RADIOABLATION FROM THE STOPSTORM.EU CONSORTIUM. Phys Med 2022. [DOI: 10.1016/s1120-1797(22)02137-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
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11
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Mandija S, Ma C, Bai R, Feng L, Giganti F, Ianus A, Lee H, Li F, Welton T, Calamante F. Walking With the
ISMRM
in the Footprints of Our
MR
History. J Magn Reson Imaging 2022; 57:1934-1936. [PMID: 36353846 DOI: 10.1002/jmri.28459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 09/19/2022] [Indexed: 11/11/2022] Open
Affiliation(s)
- Stefano Mandija
- Department of Radiotherapy University Medical Center Utrecht Utrecht The Netherlands
| | - Chao Ma
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital Harvard Medical School Boston Massachusetts USA
| | - Ruiliang Bai
- Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University Hangzhou China
| | - Li Feng
- BioMedical Engineering and Imaging Institute (BMEII) and Department of Radiology Icahn School of Medicine at Mount Sinai New York USA
| | - Francesco Giganti
- Division of Surgery and Interventional Science Faculty of Medical Sciences, University College London London UK
| | - Andrada Ianus
- Champalimaud Research Champalimaud Foundation Lisbon Portugal
| | - Hong‐Hsi Lee
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital Harvard Medical School Boston Massachusetts USA
| | - Fei Li
- Huaxi MR Research Center (HMRRC), Department of Radiology West China Hospital of Sichuan University Chengdu Sichuan China
| | - Thomas Welton
- National Neuroscience Institute Singapore & Duke‐NUS Graduate Medical School Singapore Singapore
| | - Fernando Calamante
- School of Biomedical Engineering and Sydney Imaging The University of Sydney Sydney Australia
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Liu H, van der Heide O, Mandija S, van den Berg CAT, Sbrizzi A. Acceleration Strategies for MR-STAT: Achieving High-Resolution Reconstructions on a Desktop PC Within 3 Minutes. IEEE Trans Med Imaging 2022; 41:2681-2692. [PMID: 35436186 DOI: 10.1109/tmi.2022.3168436] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
MR-STAT is an emerging quantitative magnetic resonance imaging technique which aims at obtaining multi-parametric tissue parameter maps from single short scans. It describes the relationship between the spatial-domain tissue parameters and the time-domain measured signal by using a comprehensive, volumetric forward model. The MR-STAT reconstruction solves a large-scale nonlinear problem, thus is very computationally challenging. In previous work, MR-STAT reconstruction using Cartesian readout data was accelerated by approximating the Hessian matrix with sparse, banded blocks, and can be done on high performance CPU clusters with tens of minutes. In the current work, we propose an accelerated Cartesian MR-STAT algorithm incorporating two different strategies: firstly, a neural network is trained as a fast surrogate to learn the magnetization signal not only in the full time-domain but also in the compressed low-rank domain; secondly, based on the surrogate model, the Cartesian MR-STAT problem is re-formulated and split into smaller sub-problems by the alternating direction method of multipliers. The proposed method substantially reduces the computational requirements for runtime and memory. Simulated and in-vivo balanced MR-STAT experiments show similar reconstruction results using the proposed algorithm compared to the previous sparse Hessian method, and the reconstruction times are at least 40 times shorter. Incorporating sensitivity encoding and regularization terms is straightforward, and allows for better image quality with a negligible increase in reconstruction time. The proposed algorithm could reconstruct both balanced and gradient-spoiled in-vivo data within 3 minutes on a desktop PC, and could thereby facilitate the translation of MR-STAT in clinical settings.
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Kleinloog JPD, Mandija S, D'Agata F, Liu H, van der Heide O, Koktas B, Jacobs SM, van den Berg CAT, Hendrikse J, van der Kolk AG, Sbrizzi A. Synthetic MRI with Magnetic Resonance Spin TomogrAphy in Time-Domain (MR-STAT): Results from a Prospective Cross-Sectional Clinical Trial. J Magn Reson Imaging 2022; 57:1451-1461. [PMID: 36098348 DOI: 10.1002/jmri.28425] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 08/23/2022] [Accepted: 08/23/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Magnetic Resonance Spin TomogrAphy in Time-domain (MR-STAT) can reconstruct whole-brain multi-parametric quantitative maps (eg, T1 , T2 ) from a 5-minute MR acquisition. These quantitative maps can be leveraged for synthetization of clinical image contrasts. PURPOSE The objective was to assess image quality and overall diagnostic accuracy of synthetic MR-STAT contrasts compared to conventional contrast-weighted images. STUDY TYPE Prospective cross-sectional clinical trial. POPULATION Fifty participants with a median age of 45 years (range: 21-79 years) consisting of 10 healthy participants and 40 patients with neurological diseases (brain tumor, epilepsy, multiple sclerosis or stroke). FIELD STRENGTH/SEQUENCE 3T/Conventional contrast-weighted imaging (T1 /T2 weighted, proton density [PD] weighted, and fluid-attenuated inversion recovery [FLAIR]) and a MR-STAT acquisition (2D Cartesian spoiled gradient echo with varying flip angle preceded by a non-selective inversion pulse). ASSESSMENT Quantitative T1 , T2 , and PD maps were computed from the MR-STAT acquisition, from which synthetic contrasts were generated. Three neuroradiologists blinded for image type and disease randomly and independently evaluated synthetic and conventional datasets for image quality and diagnostic accuracy, which was assessed by comparison with the clinically confirmed diagnosis. STATISTICAL TESTS Image quality and consequent acceptability for diagnostic use was assessed with a McNemar's test (one-sided α = 0.025). Wilcoxon signed rank test with a one-sided α = 0.025 and a margin of Δ = 0.5 on the 5-level Likert scale was used to assess non-inferiority. RESULTS All data sets were similar in acceptability for diagnostic use (≥3 Likert-scale) between techniques (T1 w:P = 0.105, PDw:P = 1.000, FLAIR:P = 0.564). However, only the synthetic MR-STAT T2 weighted images were significantly non-inferior to their conventional counterpart; all other synthetic datasets were inferior (T1 w:P = 0.260, PDw:P = 1.000, FLAIR:P = 1.000). Moreover, true positive/negative rates were similar between techniques (conventional: 88%, MR-STAT: 84%). DATA CONCLUSION MR-STAT is a quantitative technique that may provide radiologists with clinically useful synthetic contrast images within substantially reduced scan time. EVIDENCE LEVEL 1 Technical Efficacy: Stage 2.
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Affiliation(s)
- Jordi P D Kleinloog
- Computational Imaging Group for MR Therapy and Diagnostics, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands.,Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Stefano Mandija
- Computational Imaging Group for MR Therapy and Diagnostics, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands.,Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
| | | | - Hongyan Liu
- Computational Imaging Group for MR Therapy and Diagnostics, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands.,Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Oscar van der Heide
- Computational Imaging Group for MR Therapy and Diagnostics, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands.,Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Beyza Koktas
- Computational Imaging Group for MR Therapy and Diagnostics, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Sarah M Jacobs
- Department of Radiology and Nuclear Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Cornelis A T van den Berg
- Computational Imaging Group for MR Therapy and Diagnostics, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands.,Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Jeroen Hendrikse
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Anja G van der Kolk
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands.,Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Alessandro Sbrizzi
- Computational Imaging Group for MR Therapy and Diagnostics, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands.,Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
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Groot Koerkamp ML, van den Bongard HD, Philippens ME, van der Leij F, Mandija S, Houweling AC. Intrafraction motion during radiotherapy of breast tumor, breast tumor bed, and individual axillary lymph nodes on cine magnetic resonance imaging. Phys Imaging Radiat Oncol 2022; 23:74-79. [PMID: 35833200 PMCID: PMC9271760 DOI: 10.1016/j.phro.2022.06.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 06/29/2022] [Accepted: 06/30/2022] [Indexed: 11/17/2022] Open
Abstract
Intrafraction motion of the breast and individual axillary lymph nodes was studied. Displacements were investigated using cine magnetic resonance imaging. Motion was separated into breathing and drift components. Medians of the maximum displacements were small, <3 mm for breast and lymph nodes. Intrafraction motion of the tumor (bed) was less in prone than in supine position.
Background and purpose In (ultra-)hypofractionation, the contribution of intrafraction motion to treatment accuracy becomes increasingly important. Our purpose was to evaluate intrafraction motion and resulting geometric uncertainties for breast tumor (bed) and individual axillary lymph nodes, and to compare prone and supine position for the breast tumor (bed). Materials and methods During 1–3 min of free breathing, we acquired transverse/sagittal interleaved 1.5 T cine magnetic resonance imaging (MRI) of the breast tumor (bed) in prone and supine position and coronal/sagittal cine MRI of individual axillary lymph nodes in supine position. A total of 31 prone and 23 supine breast cine MRI (in 23 women) and 52 lymph node cine MRI (in 24 women) were included. Maximum displacement, breathing amplitude, and drift were analyzed using deformable image registration. Geometric uncertainties were calculated for all displacements and for breathing motion only. Results Median maximum displacements (range over the three orthogonal orientations) were 1.1–1.5 mm for the breast tumor (bed) in prone and 1.8–3.0 mm in supine position, and 2.2–2.4 mm for lymph nodes. Maximum displacements were significantly smaller in prone than in supine position, mainly due to smaller breathing amplitude: 0.6–0.9 mm in prone vs. 0.9–1.4 mm in supine. Systematic and random uncertainties were 0.1–0.4 mm in prone position and 0.2–0.8 mm in supine position for the tumor (bed), and 0.4–0.6 mm for the lymph nodes. Conclusion Intrafraction motion of breast tumor (bed) and individual lymph nodes was small. Motion of the tumor (bed) was smaller in prone than in supine position.
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Affiliation(s)
- Maureen L Groot Koerkamp
- Department of Radiotherapy, UMC Utrecht, Heidelberglaan 100, 3584CX Utrecht, The Netherlands
- Corresponding author.
| | | | | | - Femke van der Leij
- Department of Radiotherapy, UMC Utrecht, Heidelberglaan 100, 3584CX Utrecht, The Netherlands
| | - Stefano Mandija
- Department of Radiotherapy, UMC Utrecht, Heidelberglaan 100, 3584CX Utrecht, The Netherlands
- Computational Imaging Group for MR Diagnostics & Therapy, Center for Image Sciences, UMC Utrecht, Heidelberglaan 100, 3584CX Utrecht, The Netherlands
| | - Antonetta C Houweling
- Department of Radiotherapy, UMC Utrecht, Heidelberglaan 100, 3584CX Utrecht, The Netherlands
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Grehn M, Mandija S, Andratschke N, Zeppenfeld K, Blamek S, Fast M, Botrugno C, Blanck O, Verhoeff J, Pruvot E. Survey results of the STOPSTORM consortium about stereotactic arrhythmia radioablation in Europe. Europace 2022. [DOI: 10.1093/europace/euac053.376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Funding Acknowledgements
Type of funding sources: Public grant(s) – EU funding. Main funding source(s): Horizon 2020 research and innovation programme
Background/Introduction
In patients with structural heart disease (SHD), ventricular tachycardia (VT) plays a decisive role in sudden cardiac death. VT patients are often treated with antiarrhythmic medication and catheter ablation. For refractory VTs, STereotactic Arrhythmia Radioablation (STAR) delivered to the underlying VT substrate has recently been introduced and showed promising results for otherwise untreatable patients. [1]
Purpose
The purpose of the STOPSTORM consortium is to harmonize and optimize STAR across Europe. It consists of 31 members including 24 electrophysiology and 22 radiation oncology departments performing or participating in STAR throughout eight European countries. To obtain initial overview of organization, equipment, procedures, experiences, and quality levels for STAR, a detailed survey was circulated among STOPSTORM members.
Methods
The survey included questions for electrophysiology (18 questions), radiation oncology (24 questions) and medical physics (23 questions). The survey was the first step for accreditation of the centres and therefore mandatory for all consortium members.
Results
All centres participating in STOPSTORM completed the survey. 16 centres performed a total of 84 STAR treatments until May 2021 and 11 centres already participate in clinical trials for STAR.
Annual number of VT ablations in SHD: less than 20 (17%), 20-50 (50%), 50-100 (21%), more than 100 (12%) and epicardial: less than 20 (71%), 20-50 (17%), n/s (12%). An overview of the availability of a clinical program for catheter ablation of ventricular arrhythmia with certification of the respective national cardiology society and the practice of general quality audits for ablation is given in figure 1. Participation in multicentre clinical trials in cardiology/EP were indicated by 19 departments (79%).
Target volume definition is based on invasive electroanatomical mapping during VT (96%), pace mapping (75%), reduced voltage areas (63%) and/or late ventricular potentials (75%). Half of the centres includes the clinical VT substrate, while the other half includes the whole arrhythmogenic substrate. Non-invasive surface ECG mapping has so far found little application: used clinically (13%), research purposes (8%) and evaluation (4%).
Stereotactic Body Radiotherapy experience (> 10 years: 82%, > 200 p.a.: 59%) is high. In all but one clinic, a dose of 25 Gy in a single fraction is applied. The prescription method, planning technique and inhomogeneity in the target volume, however, varies greatly. All departments perform patient-specific plan verifications for STAR, but with various evaluation criteria.
Conclusion
Experience in STAR within the STOPSTORM consortium is adequate, while the survey shows areas of harmonization and optimization need for substrate mapping, target delineation, dosimetry and quality assurance which will be addressed in the STOPSTORM project work-packages.
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Affiliation(s)
- M Grehn
- University Medical Center of Schleswig-Holstein, Radiotherapy, Kiel, Germany
| | - S Mandija
- University Medical Center Utrecht, Radiotherapy, Utrecht, Netherlands (The)
| | - N Andratschke
- University Hospital Zurich, Radiation Oncology, Zurich, Switzerland
| | - K Zeppenfeld
- Leiden University Medical Center, Clinical Electrophysiology, Leiden, Netherlands (The)
| | - S Blamek
- Maria Sklodowska-Curie National Research Institute of Oncology, Radiotherapy, Gliwice, Poland
| | - M Fast
- University Medical Center Utrecht, Radiotherapy, Utrecht, Netherlands (The)
| | - C Botrugno
- University of Florence, Research Unit on Everyday Bioethics and Ethics of Science, Florence, Italy
| | - O Blanck
- University Medical Center of Schleswig-Holstein, Radiotherapy, Kiel, Germany
| | - J Verhoeff
- University Medical Center Utrecht, Radiotherapy, Utrecht, Netherlands (The)
| | - E Pruvot
- Lausanne university hospital, Heart and Vessel, Lausanne, Switzerland
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16
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Leijsen R, van den Berg C, Webb A, Remis R, Mandija S. Combining deep learning and 3D contrast source inversion in MR-based electrical properties tomography. NMR Biomed 2022; 35:e4211. [PMID: 31840897 PMCID: PMC9285035 DOI: 10.1002/nbm.4211] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Revised: 10/09/2019] [Accepted: 10/09/2019] [Indexed: 05/28/2023]
Abstract
Magnetic resonance electrical properties tomography (MR-EPT) is a technique used to estimate the conductivity and permittivity of tissues from MR measurements of the transmit magnetic field. Different reconstruction methods are available; however, all these methods present several limitations, which hamper the clinical applicability. Standard Helmholtz-based MR-EPT methods are severely affected by noise. Iterative reconstruction methods such as contrast source inversion electrical properties tomography (CSI-EPT) are typically time-consuming and are dependent on their initialization. Deep learning (DL) based methods require a large amount of training data before sufficient generalization can be achieved. Here, we investigate the benefits achievable using a hybrid approach, that is, using MR-EPT or DL-EPT as initialization guesses for standard 3D CSI-EPT. Using realistic electromagnetic simulations at 3 and 7 T, the accuracy and precision of hybrid CSI reconstructions are compared with those of standard 3D CSI-EPT reconstructions. Our results indicate that a hybrid method consisting of an initial DL-EPT reconstruction followed by a 3D CSI-EPT reconstruction would be beneficial. DL-EPT combined with standard 3D CSI-EPT exploits the power of data-driven DL-based EPT reconstructions, while the subsequent CSI-EPT facilitates a better generalization by providing data consistency.
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Affiliation(s)
- Reijer Leijsen
- Department of Radiology, C.J. Gorter Center for High Field MRILeiden University Medical CenterLeidenThe Netherlands
| | - Cornelis van den Berg
- Department of Radiotherapy, Division of Imaging & OncologyUniversity Medical Center UtrechtUtrechtThe Netherlands
- Computational Imaging Group for MR Diagnostics & Therapy, Center for Image SciencesUtrecht UniversityUtrechtThe Netherlands
| | - Andrew Webb
- Department of Radiology, C.J. Gorter Center for High Field MRILeiden University Medical CenterLeidenThe Netherlands
| | - Rob Remis
- Circuits and Systems Group, Faculty of Electrical Engineering, Mathematics and Computer ScienceDelft University of TechnologyDelftThe Netherlands
| | - Stefano Mandija
- Department of Radiotherapy, Division of Imaging & OncologyUniversity Medical Center UtrechtUtrechtThe Netherlands
- Computational Imaging Group for MR Diagnostics & Therapy, Center for Image SciencesUtrecht UniversityUtrechtThe Netherlands
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17
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Akdag O, Borman PTS, Woodhead P, Uijtewaal P, Mandija S, Van Asselen B, Verhoeff JJC, Raaymakers BW, Fast MF. First experimental exploration of real-time cardiorespiratory motion management for future stereotactic arrhythmia radioablation treatments on the MR-linac. Phys Med Biol 2022; 67. [PMID: 35189610 DOI: 10.1088/1361-6560/ac5717] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 02/21/2022] [Indexed: 12/25/2022]
Abstract
Objective.Stereotactic arrhythmia radioablation (STAR) is a novel, non-invasive treatment for refractory ventricular tachycardia (VT). The VT isthmus is subject to both respiratory and cardiac motion. Rapid cardiac motion presents a unique challenge. In this study, we provide first experimental evidence for real-time cardiorespiratory motion-mitigated MRI-guided STAR on the 1.5 T Unity MR-linac (Elekta AB, Stockholm, Sweden) aimed at simultaneously compensating cardiac and respiratory motions.Approach.A real-time cardiorespiratory motion-mitigated radiotherapy workflow was developed on the Unity MR-linac in research mode. A 15-beam intensity-modulated radiation therapy treatment plan (1 × 25 Gy) was created in Monaco v.5.40.01 (Elekta AB) for the Quasar MRI4Dphantom (ModusQA, London, ON). A film dosimetry insert was moved by combining either artificial (cos4, 70 bpm, 10 mm peak-to-peak) or subject-derived (59 average bpm, 15.3 mm peak-to-peak) cardiac motion with respiratory (sin, 12 bpm, 20 mm peak-to-peak) motion. A balanced 2D cine MRI sequence (13 Hz, field-of-view = 400 × 207 mm2, resolution = 3 × 3 × 15 mm3) was developed to estimate cardiorespiratory motion. Cardiorespiratory motion was estimated by rigid registration and then deconvoluted into cardiac and respiratory components. For beam gating, the cardiac component was used, whereas the respiratory component was used for MLC-tracking. In-silico dose accumulation experiments were performed on three patient data sets to simulate the dosimetric effect of cardiac motion on VT targets.Main results.Experimentally, a duty cycle of 57% was achieved when simultaneously applying respiratory MLC-tracking and cardiac gating. Using film, excellent agreement was observed compared to a static reference delivery, resulting in a 1%/1 mm gamma pass rate of 99%. The end-to-end gating latency was 126 ms on the Unity MR-linac. Simulations showed that cardiac motion decreased the target's D98% dose between 0.1 and 1.3 Gy, with gating providing effective mitigation.Significance.Real-time MRI-guided cardiorespiratory motion management greatly reduces motion-induced dosimetric uncertainty and warrants further research and development for potential future use in STAR.
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Affiliation(s)
- O Akdag
- Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - P T S Borman
- Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - P Woodhead
- Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands.,Elekta AB, Kungstensgatan 18, 113 57 Stockholm, Sweden
| | - P Uijtewaal
- Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - S Mandija
- Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands.,Computational Imaging Group for MR Diagnostics and Therapy, Center for Image Sciences, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - B Van Asselen
- Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - J J C Verhoeff
- Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - B W Raaymakers
- Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - M F Fast
- Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
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18
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Vasmel JE, Groot Koerkamp ML, Mandija S, Veldhuis WB, Moman MR, Froeling M, van der Velden BH, Charaghvandi RK, Vreuls CP, van Diest PJ, van Leeuwen AG, van Gorp J, Philippens ME, van Asselen B, Lagendijk JJ, Verkooijen HM, van den Bongard HD, Houweling AC. Dynamic Contrast-enhanced and Diffusion-weighted Magnetic Resonance Imaging for Response Evaluation After Single-Dose Ablative Neoadjuvant Partial Breast Irradiation. Adv Radiat Oncol 2022; 7:100854. [PMID: 35387418 PMCID: PMC8977856 DOI: 10.1016/j.adro.2021.100854] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 11/01/2021] [Indexed: 12/13/2022] Open
Abstract
Purpose We aimed to evaluate changes in dynamic contrast-enhanced (DCE) and diffusion-weighted (DW) magnetic resonance imaging (MRI) scans acquired before and after single-dose ablative neoadjuvant partial breast irradiation (NA-PBI), and explore the relation between semiquantitative MRI parameters and radiologic and pathologic responses. Methods and Materials We analyzed 3.0T DCE and DW-MRI of 36 patients with low-risk breast cancer who were treated with single-dose NA-PBI, followed by breast-conserving surgery 6 or 8 months later. MRI was acquired before NA-PBI and 1 week, 2, 4, and 6 months after NA-PBI. Breast radiologists assessed the radiologic response and breast pathologists scored the pathologic response after surgery. Patients were grouped as either pathologic responders or nonresponders (<10% vs ≥10% residual tumor cells). The semiquantitative MRI parameters evaluated were time to enhancement (TTE), 1-minute relative enhancement (RE1min), percentage of enhancing voxels (%EV), distribution of washout curve types, and apparent diffusion coefficient (ADC). Results In general, the enhancement increased 1 week after NA-PBI (baseline vs 1 week median – TTE: 15s vs 10s; RE1min: 161% vs 197%; %EV: 47% vs 67%) and decreased from 2 months onward (6 months median – TTE: 25s; RE1min: 86%; %EV: 12%). Median ADC increased from 0.83 × 10−3 mm2/s at baseline to 1.28 × 10−3 mm2/s at 6 months. TTE, RE1min, and %EV showed the most potential to differentiate between radiologic responses, and TTE, RE1min, and ADC between pathologic responses. Conclusions Semiquantitative analyses of DCE and DW-MRI showed changes in relative enhancement and ADC 1 week after NA-PBI, indicating acute inflammation, followed by changes indicating tumor regression from 2 to 6 months after radiation therapy. A relation between the MRI parameters and radiologic and pathologic responses could not be proven in this exploratory study.
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19
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van Rosmalen MHJ, Froeling M, Mandija S, Hendrikse J, van der Pol WL, Stephan Goedee H. MRI of the intraspinal nerve roots in patients with chronic inflammatory neuropathies: abnormalities correlate with clinical phenotypes. J Neurol 2022; 269:3159-3166. [PMID: 34988617 DOI: 10.1007/s00415-021-10864-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 10/17/2021] [Accepted: 10/18/2021] [Indexed: 11/29/2022]
Abstract
OBJECTIVE Chronic inflammatory demyelinating polyneuropathy (CIDP) and multifocal motor neuropathy (MMN) are caused by inflammatory changes of peripheral nerves. It is unknown if the intra-spinal roots are also affected. This MRI study systematically visualized intra-spinal nerve roots, i.e., the ventral and dorsal roots, in patients with CIDP, MMN and motor neuron disease (MND). METHODS We performed a cross-sectional study in 40 patients with CIDP, 27 with MMN and 34 with MND. All patients underwent an MRI scan of the cervical intra-spinal roots. We systematically measured intra-spinal nerve root sizes bilaterally in the transversal plane at C5, C6 and C7 level. We calculated mean nerve root sizes and compared them between study groups and between different clinical phenotypes using a univariate general linear model. RESULTS Patients with MMN and CIDP with a motor phenotype had thicker ventral roots compared to patients with CIDP with a sensorimotor phenotype (p = 0.012), while patients with CIDP with a sensory phenotype had thicker dorsal roots compared to patients with a sensorimotor phenotype (p = 0.001) and with MND (p = 0.004). CONCLUSION We here show changes in the morphology of intra-spinal nerve roots in patients with chronic inflammatory neuropathies, compatible with their clinical phenotype.
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Affiliation(s)
- Marieke H J van Rosmalen
- Department of Neurology and Neurosurgery, University Medical Center Utrecht, Heidelberglaan 100, 3508GA, Utrecht, The Netherlands.,Department of Radiology, University Medical Center Utrecht, Heidelberglaan 100, 3508GA, Utrecht, The Netherlands
| | - Martijn Froeling
- Department of Radiology, University Medical Center Utrecht, Heidelberglaan 100, 3508GA, Utrecht, The Netherlands
| | - Stefano Mandija
- Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, 3508GA, Utrecht, The Netherlands.,Computational Imaging Group for MR Diagnostic and Therapy, University Medical Center Utrecht, Heidelberglaan 100, 3508GA, Utrecht, The Netherlands
| | - Jeroen Hendrikse
- Department of Radiology, University Medical Center Utrecht, Heidelberglaan 100, 3508GA, Utrecht, The Netherlands
| | - W Ludo van der Pol
- Department of Neurology and Neurosurgery, University Medical Center Utrecht, Heidelberglaan 100, 3508GA, Utrecht, The Netherlands.
| | - H Stephan Goedee
- Department of Neurology and Neurosurgery, University Medical Center Utrecht, Heidelberglaan 100, 3508GA, Utrecht, The Netherlands
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20
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Akdag O, Mandija S, van Lier AL, Borman PT, Schakel T, Alberts E, van der Heide O, Hassink RJ, Verhoeff JJ, Mohamed Hoesein FA, Raaymakers BW, Fast MF. Feasibility of cardiac-synchronized quantitative T1 and T2 mapping on a hybrid 1.5 Tesla magnetic resonance imaging and linear accelerator system. Phys Imaging Radiat Oncol 2022; 21:153-159. [PMID: 35287380 PMCID: PMC8917300 DOI: 10.1016/j.phro.2022.02.017] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 02/18/2022] [Accepted: 02/20/2022] [Indexed: 11/30/2022] Open
Abstract
Background and Purpose Materials and methods Results Conclusions
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Affiliation(s)
- Osman Akdag
- Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
- Corresponding author.
| | - Stefano Mandija
- Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
- Computational Imaging Group for MR Diagnostics and Therapy, Center for Image Sciences, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Astrid L.H.M.W. van Lier
- Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Pim T.S. Borman
- Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Tim Schakel
- Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Eveline Alberts
- Philips Healthcare, Veenpluis 6 5684 PC Best, The Netherlands
| | - Oscar van der Heide
- Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
- Computational Imaging Group for MR Diagnostics and Therapy, Center for Image Sciences, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Rutger J. Hassink
- Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Joost J.C. Verhoeff
- Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Firdaus A.A. Mohamed Hoesein
- Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Bas W. Raaymakers
- Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Martin F. Fast
- Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
- Corresponding author.
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21
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Groot Koerkamp ML, van der Leij F, van 't Westeinde T, Bol GH, Scholten V, Bouwmans R, Mandija S, Philippens MEP, van den Bongard HJGD, Houweling AC. Prone vs. supine accelerated partial breast irradiation on an MR-Linac: A planning study. Radiother Oncol 2021; 165:193-199. [PMID: 34774649 DOI: 10.1016/j.radonc.2021.11.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 11/01/2021] [Accepted: 11/03/2021] [Indexed: 02/07/2023]
Abstract
BACKGROUND AND PURPOSE Accelerated partial breast irradiation (APBI) may benefit from the MR-Linac for target definition, patient setup, and motion monitoring. In this planning study, we investigated whether prone or supine position is dosimetrically beneficial for APBI on an MR-Linac and we evaluated patient comfort. MATERIALS AND METHODS Twenty-patients (9 postoperative, 11 preoperative) with a DCIS or breast tumor <3 cm underwent 1.5 T MRI in prone and supine position. The tumor or tumor bed was delineated as GTV and a 2 cm CTV-margin and 0.5 cm PTV-margin were added. 1.5 T MR-Linac treatment plans (5 × 5.2 Gy) with 11 beams were created for both positions in each patient. We evaluated the number of plans that achieved the planning constraints and performed a dosimetric comparison between prone and supine position using the Wilcoxon signed-rank test (p-value <0.01 for significance). Patient experience during scanning was evaluated with a questionnaire. RESULTS All 40 plans met the target coverage and OAR constraints, regardless of position. Heart Dmean was not significantly different (1.07 vs. 0.79 Gy, p-value: 0.027). V5Gy to the ipsilateral lung (4.4% vs. 9.8% median, p-value 0.009) and estimated delivery time (362 vs. 392 s, p-value: 0.003) were significantly lower for prone position. PTV coverage and dose to other OAR were comparable between positions. The majority of patients (13/20) preferred supine position. CONCLUSION APBI on the MR-Linac is dosimetrically feasible in prone and supine position. Mean heart dose was similar in both positions. Ipsilateral lung V5Gy was lower in prone position.
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Affiliation(s)
| | | | | | - Gijsbert H Bol
- Department of Radiotherapy, UMC Utrecht, The Netherlands
| | | | - Roel Bouwmans
- Department of Radiotherapy, UMC Utrecht, The Netherlands
| | - Stefano Mandija
- Department of Radiotherapy, UMC Utrecht, The Netherlands; Computational Imaging Group for MR Diagnostics & Therapy, Center for Image Sciences, UMC Utrecht, The Netherlands
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22
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Groot Koerkamp M, van der Leij F, van ‘t Westeinde T, Bol G, Scholten V, Bouwmans R, Mandija S, van den Bongard D, Houweling A. PO-1897 Prone vs. supine neoadjuvant accelerated partial breast irradiation on an MR-linac: a planning study. Radiother Oncol 2021. [DOI: 10.1016/s0167-8140(21)08348-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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23
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Jung KJ, Mandija S, Kim JH, Ryu K, Jung S, Cui C, Kim SY, Park M, van den Berg CAT, Kim DH. Improving phase-based conductivity reconstruction by means of deep learning-based denoising of B 1 + phase data for 3T MRI. Magn Reson Med 2021; 86:2084-2094. [PMID: 33949721 DOI: 10.1002/mrm.28826] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 03/28/2021] [Accepted: 04/13/2021] [Indexed: 12/24/2022]
Abstract
PURPOSE To denoise B 1 + phase using a deep learning method for phase-based in vivo electrical conductivity reconstruction in a 3T MR system. METHODS For B 1 + phase deep-learning denoising, a convolutional neural network (U-net) was chosen. Training was performed on data sets from 10 healthy volunteers. Input data were the real and imaginary components of single averaged spin-echo data (SNR = 45), which was used to approximate the B 1 + phase. For label data, multiple signal-averaged spin-echo data (SNR = 128) were used. Testing was performed on in silico and in vivo data. Reconstructed conductivity maps were derived using phase-based conductivity reconstructions. Additionally, we investigated the usability of the network to various SNR levels, imaging contrasts, and anatomical sites (ie, T1 , T2 , and proton density-weighted brain images and proton density-weighted breast images. In addition, conductivity reconstructions from deep learning-based denoised data were compared with conventional image filters, which were used for data denoising in electrical properties tomography (ie, the Gaussian filtering and the Savitzky-Golay filtering). RESULTS The proposed deep learning-based denoising approach showed improvement for B 1 + phase for both in silico and in vivo experiments with reduced quantitative error measures compared with other methods. Subsequently, this resulted in an improvement of reconstructed conductivity maps from the denoised B 1 + phase with deep learning. CONCLUSION The results suggest that the proposed approach can be used as an alternative preprocessing method to denoise B 1 + maps for phase-based conductivity reconstruction without relying on image filters or signal averaging.
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Affiliation(s)
- Kyu-Jin Jung
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| | - Stefano Mandija
- Computational Imaging Group for MR Diagnostic & Therapy, Center for Image Sciences, University Medical Center Utrecht, Utrecht, the Netherlands.,Department of Radiotherapy, Division of Imaging & Oncology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Jun-Hyeong Kim
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| | - Kanghyun Ryu
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea.,Department of Radiology, Stanford University, Stanford, California, USA
| | - Soozy Jung
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| | - Chuanjiang Cui
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
| | - Soo-Yeon Kim
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Mina Park
- Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Cornelis A T van den Berg
- Computational Imaging Group for MR Diagnostic & Therapy, Center for Image Sciences, University Medical Center Utrecht, Utrecht, the Netherlands.,Department of Radiotherapy, Division of Imaging & Oncology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Dong-Hyun Kim
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea
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24
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Groot Koerkamp ML, de Hond YJM, Maspero M, Kontaxis C, Mandija S, Vasmel JE, Charaghvandi RK, Philippens MEP, van Asselen B, van den Bongard HJGD, Hackett SS, Houweling AC. Synthetic CT for single-fraction neoadjuvant partial breast irradiation on an MRI-linac. Phys Med Biol 2021; 66. [PMID: 33761491 DOI: 10.1088/1361-6560/abf1ba] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 03/24/2021] [Indexed: 01/08/2023]
Abstract
A synthetic computed tomography (sCT) is required for daily plan optimization on an MRI-linac. Yet, only limited information is available on the accuracy of dose calculations on sCT for breast radiotherapy. This work aimed to (1) evaluate dosimetric accuracy of treatment plans for single-fraction neoadjuvant partial breast irradiation (PBI) on a 1.5 T MRI-linac calculated on a) bulk-density sCT mimicking the current MRI-linac workflow and b) deep learning-generated sCT, and (2) investigate the number of bulk-density levels required. For ten breast cancer patients we created three bulk-density sCTs of increasing complexity from the planning-CT, using bulk-density for: (1) body, lungs, and GTV (sCTBD1); (2) volumes for sCTBD1plus chest wall and ipsilateral breast (sCTBD2); (3) volumes for sCTBD2plus ribs (sCTBD3); and a deep learning-generated sCT (sCTDL) from a 1.5 T MRI in supine position. Single-fraction neoadjuvant PBI treatment plans for a 1.5 T MRI-linac were optimized on each sCT and recalculated on the planning-CT. Image evaluation was performed by assessing mean absolute error (MAE) and mean error (ME) in Hounsfield Units (HU) between the sCTs and the planning-CT. Dosimetric evaluation was performed by assessing dose differences, gamma pass rates, and dose-volume histogram (DVH) differences. The following results were obtained (median across patients for sCTBD1/sCTBD2/sCTBD3/sCTDLrespectively): MAE inside the body contour was 106/104/104/75 HU and ME was 8/9/6/28 HU, mean dose difference in the PTVGTVwas 0.15/0.00/0.00/-0.07 Gy, median gamma pass rate (2%/2 mm, 10% dose threshold) was 98.9/98.9/98.7/99.4%, and differences in DVH parameters were well below 2% for all structures except for the skin in the sCTDL. Accurate dose calculations for single-fraction neoadjuvant PBI on an MRI-linac could be performed on both bulk-density and deep learning sCT, facilitating further implementation of MRI-guided radiotherapy for breast cancer. Balancing simplicity and accuracy, sCTBD2showed the optimal number of bulk-density levels for a bulk-density approach.
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Affiliation(s)
- M L Groot Koerkamp
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Y J M de Hond
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands.,Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - M Maspero
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands.,Computational Imaging Group for MR diagnostics & therapy, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
| | - C Kontaxis
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - S Mandija
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands.,Computational Imaging Group for MR diagnostics & therapy, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
| | - J E Vasmel
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - R K Charaghvandi
- Department of Radiation Oncology, Radboudumc, Nijmegen, The Netherlands
| | - M E P Philippens
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - B van Asselen
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | | | - S S Hackett
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - A C Houweling
- Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
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Mandija S, Petrov PI, Vink JJT, Neggers SFW, van den Berg CAT. Brain Tissue Conductivity Measurements with MR-Electrical Properties Tomography: An In Vivo Study. Brain Topogr 2021; 34:56-63. [PMID: 33289858 PMCID: PMC7803705 DOI: 10.1007/s10548-020-00813-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Accepted: 11/28/2020] [Indexed: 01/19/2023]
Abstract
First in vivo brain conductivity reconstructions using Helmholtz MR-Electrical Properties Tomography (MR-EPT) have been published. However, a large variation in the reconstructed conductivity values is reported and these values differ from ex vivo conductivity measurements. Given this lack of agreement, we performed an in vivo study on eight healthy subjects to provide reference in vivo brain conductivity values. MR-EPT reconstructions were performed at 3 T for eight healthy subjects. Mean conductivity and standard deviation values in the white matter, gray matter and cerebrospinal fluid (σWM, σGM, and σCSF) were computed for each subject before and after erosion of regions at tissue boundaries, which are affected by typical MR-EPT reconstruction errors. The obtained values were compared to the reported ex vivo literature values. To benchmark the accuracy of in vivo conductivity reconstructions, the same pipeline was applied to simulated data, which allow knowledge of ground truth conductivity. Provided sufficient boundary erosion, the in vivo σWM and σGM values obtained in this study agree for the first time with literature values measured ex vivo. This could not be verified for the CSF due to its limited spatial extension. Conductivity reconstructions from simulated data verified conductivity reconstructions from in vivo data and demonstrated the importance of discarding voxels at tissue boundaries. The presented σWM and σGM values can therefore be used for comparison in future studies employing different MR-EPT techniques.
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Affiliation(s)
- Stefano Mandija
- Computational Imaging Group for MR Diagnostic & Therapy, Center for Image Sciences, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3584 CX, The Netherlands.
- Division of Imaging & Oncology, Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3584 CX, The Netherlands.
| | - Petar I Petrov
- Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3584 CX, The Netherlands
| | - Jord J T Vink
- Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3584 CX, The Netherlands
| | - Sebastian F W Neggers
- Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3584 CX, The Netherlands
| | - Cornelis A T van den Berg
- Computational Imaging Group for MR Diagnostic & Therapy, Center for Image Sciences, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3584 CX, The Netherlands
- Division of Imaging & Oncology, Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3584 CX, The Netherlands
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Vasmel J, Groot Koerkamp M, Charaghvandi R, Vreuls C, Van Diest P, Witkamp A, Koelemij R, Doeksen A, Van Dalen T, Van der Wall E, Wijnen J, Van der Velden B, Moman M, Veldhuis W, Philippens M, Mandija S, Lagendijk J, Verkooijen H, Houweling A, Van den Bongard D. OC-0586: Can MRI predict pathologic response after single dose neoadjuvant partial breast irradiation? Radiother Oncol 2020. [DOI: 10.1016/s0167-8140(21)00608-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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27
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van Rosmalen MHJ, Goedee HS, van der Gijp A, Witkamp TD, van Eijk RPA, Asselman FL, van den Berg LH, Mandija S, Froeling M, Hendrikse J, van der Pol WL. Quantitative assessment of brachial plexus MRI for the diagnosis of chronic inflammatory neuropathies. J Neurol 2020; 268:978-988. [PMID: 32965512 PMCID: PMC7914242 DOI: 10.1007/s00415-020-10232-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 09/14/2020] [Accepted: 09/15/2020] [Indexed: 12/22/2022]
Abstract
Objective This study aimed at developing a quantitative approach to assess abnormalities on MRI of the brachial plexus and the cervical roots in patients with chronic inflammatory demyelinating polyneuropathy (CIDP) and multifocal motor neuropathy (MMN) and to evaluate interrater reliability and its diagnostic value. Methods We performed a cross-sectional study in 50 patients with CIDP, 31 with MMN and 42 disease controls. We systematically measured cervical nerve root sizes on MRI bilaterally (C5, C6, C7) in the coronal [diameter (mm)] and sagittal planes [area (mm2)], next to the ganglion (G0) and 1 cm distal from the ganglion (G1). We determined their diagnostic value using a multivariate binary logistic model and ROC analysis. In addition, we evaluated intra- and interrater reliability. Results Nerve root size was larger in patients with CIDP and MMN compared to controls at all predetermined anatomical sites. We found that nerve root diameters in the coronal plane had optimal reliability (intrarater ICC 0.55–0.87; interrater ICC 0.65–0.90). AUC was 0.78 (95% CI 0.69–0.87) for measurements at G0 and 0.81 (95% CI 0.72–0.91) for measurements at G1. Importantly, our quantitative assessment of brachial plexus MRI identified an additional 10% of patients that showed response to treatment, but were missed by nerve conduction (NCS) and nerve ultrasound studies. Conclusion Our study showed that a quantitative assessment of brachial plexus MRI is reliable. MRI can serve as an important additional diagnostic tool to identify treatment-responsive patients, complementary to NCS and nerve ultrasound. Electronic supplementary material The online version of this article (10.1007/s00415-020-10232-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Marieke H J van Rosmalen
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht, The Netherlands
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - H Stephan Goedee
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht, The Netherlands.
| | - Anouk van der Gijp
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Theo D Witkamp
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Ruben P A van Eijk
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht, The Netherlands
- Biostatistics and Research Support, Julius Centre for Healthy Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Fay-Lynn Asselman
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht, The Netherlands
| | - Leonard H van den Berg
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht, The Netherlands
| | - Stefano Mandija
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
- Computational Imaging Group for MR Diagnostic and Therapy, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Martijn Froeling
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Jeroen Hendrikse
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - W Ludo van der Pol
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht, The Netherlands
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Hampe N, Katscher U, van den Berg CAT, Tha KK, Mandija S. Investigating the challenges and generalizability of deep learning brain conductivity mapping. ACTA ACUST UNITED AC 2020; 65:135001. [DOI: 10.1088/1361-6560/ab9356] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Navest RJM, Mandija S, Zijlema SE, Stemkens B, Andreychenko A, Lagendijk JJW, van den Berg CAT. The noise navigator for MRI-guided radiotherapy: an independent method to detect physiological motion. Phys Med Biol 2020; 65:12NT01. [PMID: 32330921 DOI: 10.1088/1361-6560/ab8cd8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Motion is problematic during radiotherapy as it could lead to potential underdosage of the tumor, and/or overdosage in organs-at-risk. A solution is adaptive radiotherapy guided by magnetic resonance imaging (MRI). MRI allows for imaging of target volumes and organs-at-risk before and during treatment delivery with superb soft tissue contrast in any desired orientation, enabling motion management by means of (real-time) adaptive radiotherapy. The noise navigator, which is independent of the MR signal, could serve as a secondary motion detection method in synergy with MR imaging. The feasibility of respiratory motion detection by means of the noise navigator was demonstrated previously. Furthermore, from electromagnetic simulations we know that the noise navigator is sensitive to tissue displacement and thus could in principle be used for the detection of various types of motion. In this study we demonstrate the detection of various types of motion for three anatomical use cases of MRI-guided radiotherapy, i.e. torso (bulk movement and variable breathing), head-and-neck (swallowing) and cardiac. Furthermore, it is shown that the noise navigator can detect bulk movement, variable breathing and swallowing on a hybrid 1.5 T MRI-linac system. Cardiac activity detection through the noise navigator seems feasible in an MRI-guided radiotherapy setting, but needs further optimization. The noise navigator is a versatile and fast (millisecond temporal resolution) motion detection method independent of MR signal that could serve as an independent verification method to detect the occurrence of motion in synergy with real-time MRI-guided radiotherapy.
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Affiliation(s)
- R J M Navest
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, Netherlands. Computational Imaging Group for MRI Diagnostics & Therapy, Centre for Image Sciences, Universiy Medical Center Utrecht, Utrecht, Netherlands
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Navest RJM, Mandija S, Bruijnen T, Stemkens B, Tijssen RHN, Andreychenko A, Lagendijk JJW, van den Berg CAT. The noise navigator: a surrogate for respiratory-correlated 4D-MRI for motion characterization in radiotherapy. Phys Med Biol 2020; 65:01NT02. [PMID: 31775130 DOI: 10.1088/1361-6560/ab5c62] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Respiratory-correlated 4D-MRI can characterize respiratory-induced motion of tumors and organs-at-risk for radiotherapy treatment planning and is a necessity for image guidance of moving tumors treated on an MRI-linac. Essential for 4D-MRI generation is a robust respiratory surrogate signal. We investigated the feasibility of the noise navigator as respiratory surrogate signal for 4D-MRI generation. The noise navigator is based on the respiratory-induced modulation of the thermal noise variance measured by the receive coils during MR acquisition and thus is inherently present and synchronized with MRI data acquisition. Additionally, the noise navigator can be combined with any rectilinear readout strategy (e.g. radial and cartesian) and is independent of MR image contrast and imaging orientation. For radiotherapy applications, the noise navigator provides a robust respiratory signal for patients scanned with an elevated coil setup. This is particularly attractive for widely used cartesian sequences where currently a non-interfering self-navigation means is lacking for MRI-based simulation and MRI-guided radiotherapy. The feasibility of 4D-MRI generation with the noise navigator as respiratory surrogate signal was demonstrated for both cartesian and radial readout strategies in radiotherapy setup on four healthy volunteers and two radiotherapy patients on a dedicated 1.5 T MRI scanner and two radiotherapy patients on a 1.5 T MRI-linac system. Moreover, the respiratory-correlated 4D-MR images showed liver motion comparable to a reference 2D cine MRI series for the volunteers. For 2D cartesian cine MRI acquisitions, both the noise navigator and respiratory bellows were benchmarked against an image navigator. Respiratory phase detection based on the noise navigator agreed 1.4 times better with the image navigator than the respiratory bellows did. For a 3D Stack-of-Stars acquisitions, the noise navigator was compared to radial self-navigation and a 1.7 times higher respiratory phase detection agreement was observed than for the respiratory bellows compared to radial self-navigation.
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Affiliation(s)
- R J M Navest
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands. Computational Imaging Group for MRI Diagnostics & Therapy, Centre for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands. Author to whom any correspondence should be addressed
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Shcherbakova Y, Bartels LW, Mandija S, Beld E, Seevinck PR, van der Voort van Zyp JRN, Kerkmeijer LGW, Moonen CTW, Lagendijk JJW, van den Berg CAT. Visualization of gold fiducial markers in the prostate using phase-cycled bSSFP imaging for MRI-only radiotherapy. ACTA ACUST UNITED AC 2019; 64:185001. [DOI: 10.1088/1361-6560/ab35c3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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Navest RJM, Mandija S, Andreychenko A, Raaijmakers AJE, Lagendijk JJW, van den Berg CAT. Understanding the physical relations governing the noise navigator. Magn Reson Med 2019; 82:2236-2247. [PMID: 31317566 PMCID: PMC6771522 DOI: 10.1002/mrm.27906] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2019] [Revised: 05/30/2019] [Accepted: 06/24/2019] [Indexed: 11/28/2022]
Abstract
Purpose The noise navigator is a passive way to detect physiological motion occurring in a patient through thermal noise modulations measured by standard clinical radiofrequency receive coils. The aim is to gain a deeper understanding of the potential and applications of physiologically induced thermal noise modulations. Methods Numerical electromagnetic simulations and MR measurements were performed to investigate the relative contribution of tissue displacement versus modulation of the dielectric lung properties over the respiratory cycle, the impact of coil diameter and position with respect to the body. Furthermore, the spatial motion sensitivity of specific noise covariance matrix elements of a receive array was investigated. Results The influence of dielectric lung property variations on the noise variance is negligible compared to tissue displacement. Coil size affected the thermal noise variance modulation, but the location of the coil with respect to the body had a larger impact. The modulation depth of a 15 cm diameter stationary coil approximately 3 cm away from the chest (i.e. radiotherapy setup) was 39.7% compared to 4.2% for a coil of the same size on the chest, moving along with respiratory motion. A combination of particular noise covariance matrix elements creates a specific spatial sensitivity for motion. Conclusions The insight gained on the physical relations governing the noise navigator will allow for optimized use and development of new applications. An optimized combination of elements from the noise covariance matrix offer new ways of performing, e.g. motion tracking.
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Affiliation(s)
- R J M Navest
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, Netherlands.,Computational Imaging Group for MRI Diagnostics & Therapy, Centre for Image Sciences, University Medical Center Utrecht, Utrecht, Netherlands
| | - S Mandija
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, Netherlands.,Computational Imaging Group for MRI Diagnostics & Therapy, Centre for Image Sciences, University Medical Center Utrecht, Utrecht, Netherlands
| | - A Andreychenko
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, Netherlands.,ITMO University, St. Petersburg, Russian Federation.,Department of Healthcare, Research and Practical Clinical Center of Diagnostics and Telemedicine Technologies of the Moscow, Moscow, Russian Federation
| | - A J E Raaijmakers
- Computational Imaging Group for MRI Diagnostics & Therapy, Centre for Image Sciences, University Medical Center Utrecht, Utrecht, Netherlands.,Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands.,Deptartment of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - J J W Lagendijk
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, Netherlands
| | - C A T van den Berg
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, Netherlands.,Computational Imaging Group for MRI Diagnostics & Therapy, Centre for Image Sciences, University Medical Center Utrecht, Utrecht, Netherlands
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Mandija S, D'Agata F, Navest RJM, Sbrizzi A, Tijssen RHN, Philippens MEP, Raaijmakers CPJ, Seravalli E, Verhoeff JJC, Lagendijk JJW, van den Berg CAT. Brain and Head-and-Neck MRI in Immobilization Mask: A Practical Solution for MR-Only Radiotherapy. Front Oncol 2019; 9:647. [PMID: 31380283 PMCID: PMC6650525 DOI: 10.3389/fonc.2019.00647] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2019] [Accepted: 07/02/2019] [Indexed: 01/08/2023] Open
Abstract
In brain/head-and-neck radiotherapy (RT), thermoplastic immobilization masks guarantee reproducible patient positioning in treatment position between MRI, CT, and irradiation. Since immobilization masks do not fit in the diagnostic MR head/head-and-neck coils, flexible surface coils are used for MRI imaging in clinical practice. These coils are placed around the head/neck, in contact with the immobilization masks. However, the positioning of these flexible coils is technician dependent, thus leading to poor image reproducibility. Additionally, flexible surface coils have an inferior signal-to-noise-ratio (SNR) compared to diagnostic coils. The aim of this work was to create a new immobilization setup which fits into the diagnostic MR coils in order to enhance MR image quality and reproducibility. For this purpose, a practical immobilization setup was constructed. The performances of the standard clinical and the proposed setups were compared with four tests: SNR, image quality, motion restriction, and reproducibility of inter-fraction subject positioning. The new immobilization setup resulted in 3.4 times higher SNR values on average than the standard setup, except directly below the flexible surface coils where similar SNR was observed. Overall, the image quality was superior for brain/head-and-neck images acquired with the proposed RT setup. Comparable motion restriction in feet-head/left-right directions (maximum motion ≈1 mm) and comparable inter-fraction repositioning accuracy (mean inter-fraction movement 1 ± 0.5 mm) were observed for the standard and the new setup.
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Affiliation(s)
- Stefano Mandija
- Computational Imaging Group for MRI Diagnostics and Therapy, Center for Image Sciences, University Medical Center Utrecht, Utrecht, Netherlands.,Department of Radiotherapy, University Medical Center Utrecht, Utrecht, Netherlands
| | - Federico D'Agata
- Computational Imaging Group for MRI Diagnostics and Therapy, Center for Image Sciences, University Medical Center Utrecht, Utrecht, Netherlands.,Department of Radiotherapy, University Medical Center Utrecht, Utrecht, Netherlands.,Department of Neurosciences, University of Turin, Turin, Italy
| | - Robin J M Navest
- Computational Imaging Group for MRI Diagnostics and Therapy, Center for Image Sciences, University Medical Center Utrecht, Utrecht, Netherlands.,Department of Radiotherapy, University Medical Center Utrecht, Utrecht, Netherlands
| | - Alessandro Sbrizzi
- Computational Imaging Group for MRI Diagnostics and Therapy, Center for Image Sciences, University Medical Center Utrecht, Utrecht, Netherlands.,Department of Radiotherapy, University Medical Center Utrecht, Utrecht, Netherlands
| | - Rob H N Tijssen
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, Netherlands
| | | | | | - Enrica Seravalli
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, Netherlands
| | - Joost J C Verhoeff
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, Netherlands
| | - Jan J W Lagendijk
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, Netherlands
| | - Cornelis A T van den Berg
- Computational Imaging Group for MRI Diagnostics and Therapy, Center for Image Sciences, University Medical Center Utrecht, Utrecht, Netherlands.,Department of Radiotherapy, University Medical Center Utrecht, Utrecht, Netherlands
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Mandija S, Meliadò EF, Huttinga NRF, Luijten PR, van den Berg CAT. Opening a new window on MR-based Electrical Properties Tomography with deep learning. Sci Rep 2019; 9:8895. [PMID: 31222055 PMCID: PMC6586684 DOI: 10.1038/s41598-019-45382-x] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Accepted: 06/04/2019] [Indexed: 11/09/2022] Open
Abstract
In the radiofrequency (RF) range, the electrical properties of tissues (EPs: conductivity and permittivity) are modulated by the ionic and water content, which change for pathological conditions. Information on tissues EPs can be used e.g. in oncology as a biomarker. The inability of MR-Electrical Properties Tomography techniques (MR-EPT) to accurately reconstruct tissue EPs by relating MR measurements of the transmit RF field to the EPs limits their clinical applicability. Instead of employing electromagnetic models posing strict requirements on the measured MRI quantities, we propose a data driven approach where the electrical properties reconstruction problem can be casted as a supervised deep learning task (DL-EPT). DL-EPT reconstructions for simulations and MR measurements at 3 Tesla on phantoms and human brains using a conditional generative adversarial network demonstrate high quality EPs reconstructions and greatly improved precision compared to conventional MR-EPT. The supervised learning approach leverages the strength of electromagnetic simulations, allowing circumvention of inaccessible MR electromagnetic quantities. Since DL-EPT is more noise-robust than MR-EPT, the requirements for MR acquisitions can be relaxed. This could be a major step forward to turn electrical properties tomography into a reliable biomarker where pathological conditions can be revealed and characterized by abnormalities in tissue electrical properties.
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Affiliation(s)
- Stefano Mandija
- Computational Imaging Group for MR diagnostic & therapy, Center for Image Sciences, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3584 CX, The Netherlands.
- Department of Radiotherapy, Division of Imaging & Oncology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3584 CX, The Netherlands.
| | - Ettore F Meliadò
- Computational Imaging Group for MR diagnostic & therapy, Center for Image Sciences, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3584 CX, The Netherlands
- Department of Radiology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3584 CX, The Netherlands
| | - Niek R F Huttinga
- Computational Imaging Group for MR diagnostic & therapy, Center for Image Sciences, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3584 CX, The Netherlands
- Department of Radiotherapy, Division of Imaging & Oncology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3584 CX, The Netherlands
| | - Peter R Luijten
- Department of Radiology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3584 CX, The Netherlands
| | - Cornelis A T van den Berg
- Computational Imaging Group for MR diagnostic & therapy, Center for Image Sciences, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3584 CX, The Netherlands
- Department of Radiotherapy, Division of Imaging & Oncology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3584 CX, The Netherlands
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Mandija S, D'Agata F, Navest R, Sbrizzi A, Raaymakers C, Tijssen R, Philippens M, Seravalli E, Verhoeff J, Lagendijk J, Van den Berg C. OC-0189 Brain and Head-and-Neck MRI in immobilization masks: a novel and practical setup for radiotherapy. Radiother Oncol 2019. [DOI: 10.1016/s0167-8140(19)30609-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Shcherbakova Y, Mandija S, Bartels L, Kerkmeijer L, Van der Voort van Zyp J, Van den Berg C. EP-2036 Visualization of prostate fiducial markers using phase-cycled bSSFP MRI. Radiother Oncol 2019. [DOI: 10.1016/s0167-8140(19)32456-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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de Boer P, Mandija S, Werensteijn-Honingh AM, van den Berg CAT, de Leeuw AAC, Jürgenliemk-Schulz IM. Cervical cancer apparent diffusion coefficient values during external beam radiotherapy. Phys Imaging Radiat Oncol 2019; 9:77-82. [PMID: 33458429 PMCID: PMC7807732 DOI: 10.1016/j.phro.2019.03.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Revised: 02/28/2019] [Accepted: 03/01/2019] [Indexed: 01/22/2023]
Abstract
Background and purpose Apparent diffusion coefficient (ADC) reflects micro-enviromental changes and therefore might be useful in predicting recurrence prior to brachytherapy. The purpose of this study is to evaluate change in ADC of the primary tumour and pathologic lymph nodes during treatment and to correlate this with clinical outcome. Material and methods Twenty patients were included who received chemoradiation for locally advanced cervical cancer between July 2016 and November 2017. All patients underwent magnetic resonance imaging (MRI) prior to treatment, and three MRIs in weeks 1/2, 3 and 4 of treatment, including T2 and diffusion weighted imaging (b-values 0, 200, 800 s/mm2) for determining an ADC-map. Primary tumour was delineated on T2 and ADC-map and pathologic lymph nodes were delineated only on ADC-map. Results At time of analysis median follow-up was 15 (range 7-22) months. From MRI one to four, primary tumour on ADC-map showed a significant signal increase of 0.94 (range 0.74-1.46) × 10-3 mm2/s to 1.13 (0.98-1.49) × 10-3 mm2/s (p < 0.001). When tumour was delineated on T2, ADC-value signal increase (in tumour according to T2) was similar. All 46 delineated pathologic lymph nodes showed an ADC-value increase on average from 0.79 (range 0.33-1.12) × 10-3 mm2/s to 1.14 (0.59-1.75) × 10-3 mm2/s (p < 0.001). The mean tumour/suspected lymph node volumes decreased respectively 51/40%. Four patients developed relapse (one local and three nodal), without clear relation with ΔADC. However, the median volume decrease of the primary tumour was substantially lower in the failing patients compared to the group without relapse (19 vs. 57%). Conclusions ADC values can be acquired using T2-based tumour delineations unless there are substantial shifts between ADC-mapping and T2 acquisition. It remains plausible that ΔADC is a predictor for response to EBRT. However, the correlation in this study was not statistically significant.
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Affiliation(s)
- Peter de Boer
- Department of Radiation Oncology, University Medical Centre Utrecht, The Netherlands.,Department of Radiation Oncology, Amsterdam University Medical Centres (Amsterdam UMC) - University of Amsterdam (UvA), The Netherlands
| | - Stefano Mandija
- Centre for Image Sciences, University Medical Centre Utrecht, Heidelberglaan 100, Utrecht 3584 CX, The Netherlands
| | | | - Cornelis A T van den Berg
- Centre for Image Sciences, University Medical Centre Utrecht, Heidelberglaan 100, Utrecht 3584 CX, The Netherlands
| | - Astrid A C de Leeuw
- Department of Radiation Oncology, University Medical Centre Utrecht, The Netherlands
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Vink JJT, Mandija S, Petrov PI, van den Berg CAT, Sommer IEC, Neggers SFW. Cover Image. Hum Brain Mapp 2018. [DOI: 10.1002/hbm.23795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
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Vink JJT, Mandija S, Petrov PI, van den Berg CAT, Sommer IEC, Neggers SFW. A novel concurrent TMS-fMRI method to reveal propagation patterns of prefrontal magnetic brain stimulation. Hum Brain Mapp 2018; 39:4580-4592. [PMID: 30156743 PMCID: PMC6221049 DOI: 10.1002/hbm.24307] [Citation(s) in RCA: 59] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2018] [Revised: 06/26/2018] [Accepted: 06/26/2018] [Indexed: 12/11/2022] Open
Abstract
Major depressive disorder (MDD) is a severe mental disorder associated with high morbidity and mortality rates, which remains difficult to treat, as both resistance and recurrence rates are high. Repetitive transcranial magnetic stimulation (TMS) of the left dorsolateral prefrontal cortex (DLPFC) provides a safe and effective treatment for selected patients with treatment‐resistant MDD. Little is known about the mechanisms of action of TMS provided to the left DLPFC in MDD and we can currently not predict who will respond to this type of treatment, precluding effective patient selection. In order to shed some light on the mechanism of action, we applied single pulse TMS to the left DLPFC in 10 healthy participants using a unique TMS‐fMRI set‐up, in which we could record the direct effects of TMS. Stimulation of the DLPFC triggered activity in a number of connected brain regions, including the subgenual anterior cingulate cortex (sgACC) in four out of nine participants. The sgACC is of particular interest, because normalization of activity in this region has been associated with relief of depressive symptoms in MDD patients. This is the first direct evidence that TMS pulses delivered to the DLPFC can propagate to the sgACC. The propagation of TMS‐induced activity from the DLPFC to sgACC may be an accurate biomarker for rTMS efficacy. Further research is required to determine whether this method can contribute to the selection of patients with treatment resistant MDD who will respond to rTMS treatment.
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Affiliation(s)
- Jord J T Vink
- Department of Imaging, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Stefano Mandija
- Department of Imaging, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Petar I Petrov
- Department of Psychiatry, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Cornells A T van den Berg
- Department of Imaging, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Iris E C Sommer
- Department of Neuroscience and Psychiatry, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.,Department of Medical and Biological Psychology, University of Bergen, Bergen, Norway
| | - Sebastiaan F W Neggers
- Department of Psychiatry, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
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Mandija S, Sbrizzi A, Katscher U, Luijten PR, van den Berg CAT. Error analysis of helmholtz-based MR-electrical properties tomography. Magn Reson Med 2017; 80:90-100. [PMID: 29144031 DOI: 10.1002/mrm.27004] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2017] [Revised: 10/19/2017] [Accepted: 10/21/2017] [Indexed: 11/10/2022]
Abstract
PURPOSE MR electrical properties tomography (MR-EPT) aims to measure tissue electrical properties by computing spatial derivatives of measured B1+ data. This computation is very sensitive to spatial fluctuations caused, for example, by noise and Gibbs ringing. In this work, the error arising from the computation of spatial derivatives using finite difference kernels (FD error) has been investigated. In relation to this FD error, it has also been investigated whether mitigation strategies such as Gibbs ringing correction and Gaussian apodization can be beneficial for conductivity reconstructions. METHODS Conductivity reconstructions were performed on a phantom (by means of simulations and MR measurements at 3T) and on a human brain model. The accuracy was evaluated as a function of image resolution, FD kernel size, k-space windowing, and signal-to-noise ratio. The impact of mitigation strategies was also investigated. RESULTS The adopted small FD kernel is highly sensitive to spatial fluctuations, whereas the large FD kernel is more noise-robust. However, large FD kernels lead to extended numerical boundary error propagation, which severely hampers the MR-EPT reconstruction accuracy for highly spatially convoluted tissue structures such as the human brain. Mitigation strategies slightly improve the accuracy of conductivity reconstructions. For the adopted derivative kernels and the investigated scenario, MR-EPT conductivity reconstructions show low accuracy: less than 37% of the voxels have a relative error lower than 30%. CONCLUSION The numerical error introduced by the computation of spatial derivatives using FD kernels is one of the major causes of limited accuracy in Helmholtz-based MR-EPT reconstructions. Magn Reson Med 80:90-100, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Stefano Mandija
- Center For Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Alessandro Sbrizzi
- Center For Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
| | | | - Peter R Luijten
- Center For Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands.,Department of Radiology, Imaging Division, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Cornelis A T van den Berg
- Center For Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands.,Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
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Mandija S, Petrov PI, Neggers SFW, Luijten PR, van den Berg CAT. MR-based measurements and simulations of the magnetic field created by a realistic transcranial magnetic stimulation (TMS) coil and stimulator. NMR Biomed 2016; 29:1590-1600. [PMID: 27669678 DOI: 10.1002/nbm.3618] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2016] [Revised: 07/04/2016] [Accepted: 08/12/2016] [Indexed: 06/06/2023]
Abstract
Transcranial magnetic stimulation (TMS) is an emerging technique that allows non-invasive neurostimulation. However, the correct validation of electromagnetic models of typical TMS coils and the correct assessment of the incident TMS field (BTMS ) produced by standard TMS stimulators are still lacking. Such a validation can be performed by mapping BTMS produced by a realistic TMS setup. In this study, we show that MRI can provide precise quantification of the magnetic field produced by a realistic TMS coil and a clinically used TMS stimulator in the region in which neurostimulation occurs. Measurements of the phase accumulation created by TMS pulses applied during a tailored MR sequence were performed in a phantom. Dedicated hardware was developed to synchronize a typical, clinically used, TMS setup with a 3-T MR scanner. For comparison purposes, electromagnetic simulations of BTMS were performed. MR-based measurements allow the mapping and quantification of BTMS starting 2.5 cm from the TMS coil. For closer regions, the intra-voxel dephasing induced by BTMS prohibits TMS field measurements. For 1% TMS output, the maximum measured value was ~0.1 mT. Simulations reflect quantitatively the experimental data. These measurements can be used to validate electromagnetic models of TMS coils, to guide TMS coil positioning, and for dosimetry and quality assessment of concurrent TMS-MRI studies without the need for crude methods, such as motor threshold, for stimulation dose determination.
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Affiliation(s)
- Stefano Mandija
- Center for Image Sciences, University Medical Center Utrecht, Utrecht, the Netherlands.
| | - Petar I Petrov
- Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Sebastian F W Neggers
- Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Peter R Luijten
- Center for Image Sciences, University Medical Center Utrecht, Utrecht, the Netherlands
- Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Cornelis A T van den Berg
- Center for Image Sciences, University Medical Center Utrecht, Utrecht, the Netherlands
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, the Netherlands
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Mandija S, Petrov PI, Neggers SFW, Luijten PR, van den Berg CAT. Noninvasive Electric Current Induction for Low-Frequency Tissue Conductivity Reconstruction: Is It Feasible With a TMS-MRI Setup? ACTA ACUST UNITED AC 2016; 2:203-214. [PMID: 30042964 PMCID: PMC6024402 DOI: 10.18383/j.tom.2016.00232] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Noninvasive quantification of subject-specific low-frequency brain tissue conductivity ( σLF ) will be valuable in different fields, for example, neuroscience. Magnetic resonance (MR)-electrical impedance tomography allows measurements of σLF . However, the required high level of direct current injection leads to an undesirable pain sensation. Following the same principles, but avoiding pain sensation, we evaluate the feasibility of inductively inducing currents using a transcranial magnetic stimulation (TMS) device and recording the magnetic field variations arising from the induced tissue eddy currents using a standard 3 T MR scanner. Using simulations, we characterize the strength of the incident TMS magnetic field arising from the current running in the TMS coil, the strength of the induced magnetic field arising from the induced currents in tissues by TMS pulses, and the MR phase accuracy required to measure this latter magnetic field containing information about σLF . Then, using TMS-MRI measurements, we evaluate the achievable phase accuracy for a typical TMS-MRI setup. From measurements and simulations, it is crucial to discriminate the incident from the induced magnetic field. The incident TMS magnetic field range is ±10-4 T, measurable with standard MR scanners. In contrast, the induced TMS magnetic field is much weaker (±10-8 T), leading to an MR phase contribution of ∼10-4 rad. This phase range is too small to be measured, as the phase accuracy for TMS-MRI experiments is ∼10-2 rads. Thus, although highly attractive, noninvasive measurements of the induced TMS magnetic field, and therefore estimations of σLF , are experimentally not feasible.
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Affiliation(s)
- Stefano Mandija
- Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Petar I Petrov
- Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Sebastian F W Neggers
- Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Peter R Luijten
- Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands.,Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands; and
| | - Cornelis A T van den Berg
- Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands.,Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
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Mandija S, van Lier AL, Katscher U, Petrov PI, Neggers SF, Luijten PR, van den Berg CA. A geometrical shift results in erroneous appearance of low frequency tissue eddy current induced phase maps. Magn Reson Med 2015; 76:905-12. [DOI: 10.1002/mrm.25981] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2015] [Revised: 08/20/2015] [Accepted: 08/20/2015] [Indexed: 01/25/2023]
Affiliation(s)
- Stefano Mandija
- Center for Image Sciences; University Medical Center Utrecht; Heidelberglaan 100, 3584 CX Utrecht The Netherlands
| | - Astrid L.H.M.W. van Lier
- Department of Radiotheraphy; University Medical Center Utrecht; Heidelberglaan 100, 3584 CX Utrecht The Netherlands
| | - Ulrich Katscher
- Philips Research Europe-Hamburg; Roentgenstr 24-26, 22335, Hamburg Germany
| | - Petar I. Petrov
- Department of Psychiatry, Rudolf Magnus Institute of Neuroscience; University Medical Center Utrecht; Heidelberglaan 100, 3584 CX Utrecht The Netherlands
| | - Sebastian F.W. Neggers
- Department of Psychiatry, Rudolf Magnus Institute of Neuroscience; University Medical Center Utrecht; Heidelberglaan 100, 3584 CX Utrecht The Netherlands
| | - Peter R. Luijten
- Center for Image Sciences; University Medical Center Utrecht; Heidelberglaan 100, 3584 CX Utrecht The Netherlands
- Department of Radiology, Imaging Division; University Medical Center Utrecht; Heidelberglaan 100, 3584 CX Utrecht The Netherlands
| | - Cornelis A.T. van den Berg
- Center for Image Sciences; University Medical Center Utrecht; Heidelberglaan 100, 3584 CX Utrecht The Netherlands
- Department of Radiotheraphy; University Medical Center Utrecht; Heidelberglaan 100, 3584 CX Utrecht The Netherlands
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Neggers SF, Petrov PI, Mandija S, Sommer IE, van den Berg NA. Understanding the biophysical effects of transcranial magnetic stimulation on brain tissue. Progress in Brain Research 2015; 222:229-59. [DOI: 10.1016/bs.pbr.2015.06.015] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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