Hybrid MRI-linac (MRL) systems enable daily multiparametric quantitative MRI to assess tumor response to radiotherapy. Magnetic Resonance Fingerprinting (MRF) may provide time efficient means of rapid multiparametric quantitative MRI. The accuracy of MRF, however, relies on adequate control over system imperfections, such as eddy currents and B1+, which are different and not as well established on MRL systems compared to diagnostic systems. In this study we investigate the technical feasibility of gradient spoiled 2D MRF on a 1.5T MRL. We show with phantom experiments that the MRL generates reliable MRF signals that are temporally stable during the day and have good agreement with spin-echo reference measurements. Subsequent in-vivo MRF scans in healthy volunteers and a patient with a colorectal liver metastasis showed good image quality, where the quantitative values of selected organs corresponded with the values reported in literature. Therefore we conclude that gradient spoiled 2D MRF is feasible on a 1.5T MRL with similar performance as on a diagnostic system. The precision and accuracy of the parametric maps are sufficient for further investigation of the clinical utility of MRF for online quantitatively MRI-guided radiotherapy.
MRI-guided cardiac radiotherapy using a 1.5 T MR-linac: surveying emerging patterns of care
University Medical Center Utrecht（乌得勒支大学医学中心）；University of Texas MD Anderson Cancer Center（德克萨斯大学安德森癌症中心）；Allegheny Health Network（阿勒格尼健康网络）；GenesisCare（创生医疗）
Magnetic resonance imaging (MRI) is increasingly utilized for image-guided radiotherapy due to its outstanding soft-tissue contrast and lack of ionizing radiation. However, geometric distortions caused by gradient nonlinearity (GNL) limit anatomical accuracy, potentially compromising the quality of tumour treatments. In addition, slow MR acquisition and reconstruction limit the potential for real-time image guidance. Here, we demonstrate a deep learning-based method that rapidly reconstructs distortion-corrected images from raw k-space data for real-time MR-guided radiotherapy applications. We…
Magnetic resonance fingerprinting (MRF) can successfully recover quantitative multi-parametric maps of human tissue in a very short acquisition time. Due to their pseudo-random nature, the large spatial undersampling artifacts can be filtered out by an exhaustive search over a pre-computed dictionary of signal fingerprints. This reconstruction approach is robust to large data-model discrepancies and is easy to implement. The curse of dimensionality and the intrinsic rigidity of such a precomputed dictionary approach can however limit its practical applicability. In this work, a method is prese…
Diffusion tractography is routinely used to study white matter architecture and brain connectivity in vivo. A key step for successful tractography of neuronal tracts is the correct identification of tract directions in each voxel. Here we propose a fingerprinting-based methodology to identify these fiber directions in Orientation Distribution Functions, dubbed ODF-Fingerprinting (ODF-FP). In ODF-FP, fiber configurations are selected based on the similarity between measured ODFs and elements in a pre-computed library. In noisy ODFs, the library matching algorithm penalizes the more complex fibe…
Mathematical models are becoming increasingly important in magnetic resonance imaging (MRI), as they provide a mechanistic approach for making a link between tissue microstructure and signals acquired using the medical imaging instrument. The Bloch equations, which describes spin and relaxation in a magnetic field, is a set of integer order differential equations with a solution exhibiting mono-exponential behaviour in time. Parameters of the model may be estimated using a non-linear solver, or by creating a dictionary of model parameters from which MRI signals are simulated and then matched w…
Nuclear magnetic resonance signal dynamics as described by the Bloch equations are highly complex and often are without closed form solutions. This is especially the case for quantitative magnetic resonance fingerprinting (MRF) scans in which acquisition parameters are varied to efficiently probe the parameter space. As previously demonstrated, optimization of the pattern in which the MRI acquisition parameters are varied relative to the variance in the target quantitative parameters can improve experiment design. This process, however, relies on large scale non-linear optimizations with hundr…
TrackRAD2025 challenge dataset: Real-time tumor tracking for MRI-guided radiotherapy
Sichuan Cancer Hospital & Institute（四川省肿瘤医院研究所）；University of Electronic Science and Technology of China（电子科技大学）；LMU University Hospital（LMU大学医院）；LMU Munich（慕尼黑大学）；Catharina Hospital（凯瑟琳医院）；Mater Olbia Hospital（奥尔比亚 Mater 医院）；University Medical Center Utrecht（乌得勒支大学医学中心）；Ludwig-Maximilians-Universität München（慕尼黑大学）；Czech Technical University in Prague（布拉格捷克理工大学）；University of Sydney（悉尼大学）；Fondazione Policlinico Universitario Agostino Gemelli, IRCCS（阿戈斯蒂诺·杰梅利大学综合医院基金会 IRCCS）；Abano Terme Hospital（阿巴诺泰尔梅医院）；GenesisCare；St Vincent’s Hospital（圣文森特医院）；Amsterdam UMC location Vrije Universiteit Amsterdam（阿姆斯特丹自由大学医学中心）；Technical University Eindhoven（埃因霍温理工大学）
MR fingerprinting (MRF) is a rapid growing approach for fast quantitave MRI. A typical drawback of dictionary-based MRF is its explosion in size as a function of the number of reconstructed parameters, according to the curse of dimensionality. Deep Neural Networks (NNs) have been proposed as a feasible alternative, but these approaches are still in their infancy. We tested different NN pipelines on simulated data: we studied optimal training procedures by including different strategies of noise addition and parameter space sampling, to achieve better accuracy and robustness to noise. Four MRF …
Purpose: Magnetic Resonance Fingerprinting (MRF) is a relatively new approach that provides quantitative MRI measures using randomized acquisition. Extraction of physical quantitative tissue parameters is performed off-line, without the need of patient presence, based on acquisition with varying parameters and a dictionary generated according to the Bloch equation simulations. MRF uses hundreds of radio frequency (RF) excitation pulses for acquisition, and therefore a high undersampling ratio in the sampling domain (k-space) is required for reasonable scanning time. This undersampling causes s…
Quantitative Magnetic Resonance Imaging (MRI) is based on a two-steps approach: estimation of the magnetic moments distribution inside the body, followed by a voxel-by-voxel quantification of the human tissue properties. This splitting simplifies the computations but poses several constraints on the measurement process, limiting its efficiency. Here, we perform quantitative MRI as a one step process; signal localization and parameter quantification are simultaneously obtained by the solution of a large scale nonlinear inversion problem based on first-principles. As a consequence, the constrain…
Implicit neural representation for free-breathing MR fingerprinting (INR-MRF): co-registered 3D whole-liver water T1, water T2, proton density fat fraction, and R2* mapping
Quantitative MRI methods that estimate multiple physical parameters simultaneously often require the fitting of a computational complex signal model defined through the Bloch equations. Repeated Bloch simulations can be avoided by matching the measured signal with a precomputed signal dictionary on a discrete parameter grid, as used in MR Fingerprinting. However, accurate estimation requires discretizing each parameter with a high resolution and consequently high computational and memory costs for dictionary generation, storage, and matching. Here, we reduce the required parameter resolution b…
This study presents a comparison of quantitative MRI methods based on an efficiency metric that quantifies their intrinsic ability to extract information about tissue parameters. Under a regime of unbiased parameter estimates, an intrinsic efficiency metric $η$ was derived for fully-sampled experiments which can be used to both optimize and compare sequences. Here we optimize and compare several steady-state and transient gradient-echo based qMRI methods, such as magnetic resonance fingerprinting (MRF), for joint T1 and T2 mapping. The impact of undersampling was also evaluated, assuming incoh…
The MR-Linac is a combination of an MR-scanner and radiotherapy linear accelerator (Linac) which holds the promise to increase the precision of radiotherapy treatments with MR-guided radiotherapy by monitoring motion during radiotherapy with MRI, and adjusting the radiotherapy plan accordingly. Optimal MR-guidance for respiratory motion during radiotherapy requires MR-based 3D motion estimation with a latency of 200-500 ms. Currently this is still challenging since typical methods rely on MR-images, and are therefore limited by the 3D MR-imaging latency. In this work, we present a method to pe…
NYU School of Medicine（纽约大学医学院）；Univ Gustave Eiffel（古斯塔夫·埃菲尔大学）；Univ Paris Est Creteil（巴黎东克雷泰伊大学）；CNRS（法国国家科学研究中心）；NYU Center for Data Science（纽约大学数据科学中心）；NYU Courant Institute of Mathematical Sciences（纽约大学库朗数学科学研究所）
Neural networks are increasingly used to estimate parameters in quantitative MRI, in particular in magnetic resonance fingerprinting. Their advantages over the gold standard non-linear least square fitting are their superior speed and their immunity to the non-convexity of many fitting problems. We find, however, that in heterogeneous parameter spaces, i.e. in spaces in which the variance of the estimated parameters varies considerably, good performance is hard to achieve and requires arduous tweaking of the loss function, hyper parameters, and the distribution of the training data in paramete…
A No-Reference Medical Image Quality Assessment Method Based on Automated Distortion Recognition Technology: Application to Preprocessing in MRI-guided Radiotherapy
Beijing University of Posts and Telecommunications（北京邮电大学）；National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院国家癌症中心/国家癌症临床医学研究中心/肿瘤医院）
DoseRAD2026 Challenge dataset: AI accelerated photon and proton dose calculation for radiotherapy
Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany（路德维希-马克西米利安慕尼黑大学医学院放射肿瘤科，慕尼黑大学医院，慕尼黑，德国）；Division of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany（放射肿瘤学中的医学物理部门，德国癌症研究中心（DKFZ），海德堡，德国）；National Center for Radiation Research in Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Heidelberg, Germany（肿瘤放射研究国家中心（NCRO），海德堡放射肿瘤学研究所（HIRO），海德堡，德国）；GSI Helmholtzzentrum für Schwerionenforschung, Biophysics Department, Darmstadt, Germany（重离子研究中心GSI，生物物理部门，达姆施塔特，德国）；Department of Physics and Astronomy, Heidelberg University, Heidelberg, Germany（海德堡大学物理与天文学系，海德堡，德国）；Institute of Biomedical Engineering, Karlsruhe Institute of Technology, Baden-Württemberg, Germany（生物医学工程研究所，卡尔斯鲁厄理工学院，巴登-符腾堡，德国）；Department of Radiotherapy, University Medical Center Utrecht, Utrecht, Netherlands（放射治疗科，乌得勒支大学医学中心，乌得勒支，荷兰）；Department of Radiation Oncology, Amsterdam UMC location Vrije Universiteit Amsterdam, Cancer Center Amsterdam, Cancer Treatment and Quality of Life, Amsterdam, Netherlands（放射肿瘤学系，阿姆斯特丹自由大学阿姆斯特丹校区，阿姆斯特丹癌症中心，癌症治疗与生活质量，阿姆斯特丹，荷兰）；Center for Proton Therapy, Paul Scherrer Institute (PSI), Villigen, Switzerland（质子治疗中心，瑞士保罗·谢尔研究所（PSI），维利根，瑞士）；Department of Radiation Oncology, University Hospital Heidelberg, Heidelberg, Germany（放射肿瘤学系，海德堡大学医院，海德堡，德国）；Radiation Physics, Department of Hematology, Oncology, and Radiation Physics, Skåne University Hospital, Lund, Sweden（放射物理，血液学、肿瘤学和放射物理部门，斯德哥尔摩大学医院，吕勒奥，瑞典）；Delft University of Technology, Department of Radiation Science and Technology, Delft, Netherlands（代尔夫特理工大学，辐射科学与技术系，代尔夫特，荷兰）；Radformation Inc., New York, US（Radformation公司，纽约，美国）
Multiparameter Uncertainty Mapping in Quantitative Molecular MRI using a Physics-Structured Variational Autoencoder (PS-VAE)
School of Biomedical Engineering, Tel-Aviv University（生物医学工程学院，特拉维夫大学）；School of Biochemistry, Neurobiology, and Biophysics, Tel-Aviv University（生物化学、神经生物学与生物物理学学院，特拉维夫大学）；Institute of Neuroradiology, University Hospital Erlangen（神经放射学研究所，埃朗根大学医院）；Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU)（生物医学工程人工智能系，弗赖堡-艾尔朗根-纽伦堡大学（FAU））；Sagol School of Neuroscience, Tel-Aviv University（萨戈尔神经科学学院，特拉维夫大学）
