MR Applications Research Articles

A Novel J-Shape Antenna Array for Simultaneous MR-PET or MR-SPECT Imaging.

Simultaneous MR-PET/-SPECT is an emerging technology that capitalises on the invaluable advantages of both modalities, allowing access to numerous sensitive tracers and superior soft-tissue contrast alongside versatile functional imaging capabilities. However, to optimise these capabilities, concurrent acquisitions require the MRI antenna located inside the PET/SPECT field-of-view to be operated without compromising any aspects of system performance or image quality compared to the stand-alone instrumentation. Here, we report a novel gamma-radiation-transparent antenna concept. The end-fed J-shape antenna is particularly adept for hybrid ultra-high field MR-PET/-SPECT applications as it enables all highly attenuating materials to be placed outside the imaging field-of-view. Furthermore, this unique configuration also provides advantages in stand-alone MR applications by reducing the amount of coupling between the cables and the antenna elements, and by lowering the potential specific absorption rate burden. The use of this new design was experimentally verified according to the important features for both ultra-high field MRI and the 511 keV transmission scan. The reconstructed attenuation maps evidently showed much lower attenuation ( ∼15 %) for the proposed array when compared to the conventional dipole antenna array since there were no high-density components. In MR, it was observed that the signal-to-noise ratio from the whole volume obtained using the proposed array was comparable to that acquired by the conventional array which was also in agreement with the simulation results. The unique feature, J-shape array, would enable simultaneous MR-PET/-SPECT experiments to be conducted without unduly compromising any aspects of system performance and image quality compared to the stand-alone instrumentation.

Interventional cardiac magnetic resonance imaging: current applications, technology readiness level, and future perspectives.

Background:Cardiac magnetic resonance (CMR) provides excellent temporal and spatial resolution, tissue characterization, and flow measurements. This enables major advantages when guiding cardiac invasive procedures compared with X-ray fluoroscopy or ultrasound guidance. However, clinical implementation is limited due to limited availability of technological advancements in magnetic resonance imaging (MRI) compatible equipment. A systematic review of the available literature on past and present applications of interventional MR and its technology readiness level (TRL) was performed, also suggesting future applications.Methods:A structured literature search was performed using PubMed. Search terms were focused on interventional CMR, cardiac catheterization, and other cardiac invasive procedures. All search results were screened for relevance by language, title, and abstract. TRL was adjusted for use in this article, level 1 being in a hypothetical stage and level 9 being widespread clinical translation. The papers were categorized by the type of procedure and the TRL was estimated.Results:Of 466 papers, 117 papers met the inclusion criteria. TRL was most frequently estimated at level 5 meaning only applicable to in vivo animal studies. Diagnostic right heart catheterization and cavotricuspid isthmus ablation had the highest TRL of 8, meaning proven feasibility and efficacy in a series of humans.Conclusion:This article shows that interventional CMR has a potential widespread application although clinical translation is at a modest level with TRL usually at 5. Future development should be directed toward availability of MR-compatible equipment and further improvement of the CMR techniques. This could lead to increased TRL of interventional CMR providing better treatment.

Bone Mechanoregulation Allows Subject-Specific Load Estimation Based on Time-Lapsed Micro-CT and HR-pQCT in Vivo.

Patients at high risk of fracture due to metabolic diseases frequently undergo long-term antiresorptive therapy. However, in some patients, treatment is unsuccessful in preventing fractures or causes severe adverse health outcomes. Understanding load-driven bone remodelling, i.e., mechanoregulation, is critical to understand which patients are at risk for progressive bone degeneration and may enable better patient selection or adaptive therapeutic intervention strategies. Bone microarchitecture assessment using high-resolution peripheral quantitative computed tomography (HR-pQCT) combined with computed mechanical loads has successfully been used to investigate bone mechanoregulation at the trabecular level. To obtain the required mechanical loads that induce local variances in mechanical strain and cause bone remodelling, estimation of physiological loading is essential. Current models homogenise strain patterns throughout the bone to estimate load distribution in vivo, assuming that the bone structure is in biomechanical homoeostasis. Yet, this assumption may be flawed for investigating alterations in bone mechanoregulation. By further utilising available spatiotemporal information of time-lapsed bone imaging studies, we developed a mechanoregulation-based load estimation (MR) algorithm. MR calculates organ-scale loads by scaling and superimposing a set of predefined independent unit loads to optimise measured bone formation in high-, quiescence in medium-, and resorption in low-strain regions. We benchmarked our algorithm against a previously published load history (LH) algorithm using synthetic data, micro-CT images of murine vertebrae under defined experimental in vivo loadings, and HR-pQCT images from seven patients. Our algorithm consistently outperformed LH in all three datasets. In silico-generated time evolutions of distal radius geometries (n = 5) indicated significantly higher sensitivity, specificity, and accuracy for MR than LH (p < 0.01). This increased performance led to substantially better discrimination between physiological and extra-physiological loading in mice (n = 8). Moreover, a significantly (p < 0.01) higher association between remodelling events and computed local mechanical signals was found using MR [correct classification rate (CCR) = 0.42] than LH (CCR = 0.38) to estimate human distal radius loading. Future applications of MR may enable clinicians to link subtle changes in bone strength to changes in day-to-day loading, identifying weak spots in the bone microstructure for local intervention and personalised treatment approaches.

Flexible numerical simulation framework for dynamic PET-MR data

This paper presents a simulation framework for dynamic PET-MR. The main focus of this framework is to provide motion-resolved MR and PET data and ground truth motion information. This can be used in the optimisation and quantitative evaluation of image registration and in assessing the error propagation due to inaccuracies in motion estimation in complex motion-compensated reconstruction algorithms. Contrast and tracer kinetics can also be simulated and are available as ground truth information. To closely emulate medical examination, input and output of the simulation are files in standardised open-source raw data formats. This enables the use of existing raw data as a template input and ensures seamless integration of the output into existing reconstruction pipelines.The proposed framework was validated in PET-MR and image registration applications. It was used to simulate a FDG-PET-MR scan with cardiac and respiratory motion. Ground truth motion information could be utilised to optimise parameters for PET and synergistic PET-MR image registration. In addition, a free-breathing dynamic contrast enhancement (DCE) abdominal scan of a patient with hepatic lesions was simulated. In order to correct for breathing motion, a motion-corrected image reconstruction scheme was used and a Toft’s model was fit to the DCE data to obtain quantitative DCE-MRI parameters. Utilising the ground truth motion information, the dependency of quantitative DCE-MR images on the accuracy of the motion estimation was evaluated. We demonstrated that respiratory motion had to be available with an average accuracy of at least the spatial resolution of the DCE-MR images in order to ensure an improvement in lesions visualisation and quantification compared to no motion correction.The proposed framework provides a valuable tool with a wide range of scientific PET and MR applications and will be available as part of the open-source project Synergistic Image Reconstruction Framework (SIRF).

The Financial Benefits of Faster Development Times: Integrated Formulation Development, Real-Time Manufacturing, and Clinical Testing.

Faster drug development times get new therapies to patients sooner and financially benefit drug developers by shortening the time between investment and returns and increasing the time on the market with intellectual property protection. The result is enhanced incentives to innovate. We provide a real-world example of the financial gains from quicker development using recent estimates of drug development costs, returns, and estimates of time reductions from an alternative early-stage drug development paradigm. We utilized data obtained from a drug development and manufacturing services organization to estimate the reduction in development time for drug sponsors from using an integrated platform of formulation development, real-time manufacturing, and clinical testing for 19 completed drug product development projects covering three key drug development activities (transitioning from first-in-human to proof-of-concept [FIH-PoC], modified release formulation development [MR], and enhanced solubility formulation development [ES]). A traditional drug development paradigm was taken as the base case and financial impacts of the alternative development program were determined relative to the base case. The total after-tax financial benefits of shorter development times from integrating formulation development, real-time manufacturing, and clinical testing when applied across a broad portfolio of investigational drugs ranged from $230.5 million to $290.1 million, $196.4 million to $247.5 million, and $102.6 million to $275.5 million, per approved new drug for FIH-PoC, MR, and ES applications, respectively (2018 dollars). For the data we examined, this integrated development model yielded substantial financial benefits over traditional drug development.

Selected Magnetic Resonance applications for non-destructive material testing

Abstract Magnetic resonance’s potential is already proven in the clinical sector and for non-destructive testing also in the laboratory environment. With recent developments in technology today’s small and midsized often portable MR systems are also able to work in an industrial setup and in the field. Therefore, the number of MR applications is rapidly increasing as is the acceptance of MR as technique for non-destructive testing. Selected examples for magnetic resonance in polymers, in porous media and in food samples are shown here.

Performance of compressed sensing for fluorine-19 magnetic resonance imaging at low signal-to-noise ratio conditions.

To examine the performance of compressed sensing (CS) in reconstructing low signal-to-noise ratio (SNR) 19 F MR signals that are close to the detection threshold and originate from small signal sources with no a priori known location. Regularization strength was adjusted automatically based on noise level. As performance metrics, root-mean-square deviations, true positive rates (TPRs), and false discovery rates were computed. CS and conventional reconstructions were compared at equal measurement time and evaluated in relation to high-SNR reference data. 19 F MR data were generated from a purpose-built phantom and benchmarked against simulations, as well as from the experimental autoimmune encephalomyelitis mouse model. We quantified the signal intensity bias and introduced an intensity calibration for in vivo data using high-SNR ex vivo data. Low-SNR 19 F MR data could be reliably reconstructed. Detection sensitivity was consistently improved and data fidelity was preserved for undersampling and averaging factors of α = 2 or = 3. Higher α led to signal blurring in the mouse model. The improved TPRs at α = 3 were comparable to a 2.5-fold increase in measurement time. Whereas CS resulted in a downward bias of the 19 F MR signal, Fourier reconstructions resulted in an unexpected upward bias of similar magnitude. The calibration corrected signal-intensity deviations for all reconstructions. CS is advantageous whenever image features are close to the detection threshold. It is a powerful tool, even for low-SNR data with sparsely distributed 19 F signals, to improve spatial and temporal resolution in 19 F MR applications.

Enhancing surface coil sensitive volume with hybridized electric dipoles at 17.2 T.

Preclinical MR applications at 17.2 T can require field of views on the order of a few square centimeters. This is a challenging task as the proton Larmor frequency reaches 730 MHz. Most of the protocols at such frequencies are performed with surface transceiver coils for which the sensitive volume and the signal to noise ratio (SNR) is given by their size. Here we propose an approach based on metamaterials in order to enhance the sensitive volume of a commercial surface coil for small animal imaging at 17.2 T. We designed a passive resonator composed of four hybridized electric dipoles placed onto the floor of the MRI bed. Combining numerical and experimental results on a phantom and in vivo, we demonstrate a 20% increase of the sensitive volume in depth and 25% along the rostro-caudal axis while maintaining more than 85% of the local SNR right beneath the surface coil plane. Moreover, our solution gives the ability to double the average SNR in the region between 1.2 and 2 cm away from the loop using a single layer of 1 mm thick metallic wires easy to design and manufacture.

Cells, drugs and NMR.

Nuclear magnetic resonance (NMR) spectroscopy is a versatile tool for investigating cellular structures and their compositions. While in vivo and whole-cell NMR have a long tradition in cell-based approaches, high-resolution in-cell NMR spectroscopy is a new addition to these methods. In recent years, technological advancements in multiple areas provided converging benefits for cellular MR applications, especially in terms of robustness, reproducibility and physiological relevance. Here, we review the use of cellular NMR methods for drug discovery purposes in academia and industry. Specifically, we discuss how developments in NMR technologies such as miniaturized bioreactors and flow-probe perfusion systems have helped to consolidate NMR's role in cell-based drug discovery efforts.

Reconstruction of the 12‐lead ECG using a novel MR‐compatible ECG sensor network

Current electrocardiography (ECG) devices in MRI use non-conventional electrode placement, have a narrow bandwidth, and suffer from signal distortions including magnetohydrodynamic (MHD) effects and gradient-induced artifacts. In this work a system is proposed to obtain a high-quality 12-lead ECG. A network of N electrically independent MR-compatible ECG sensors was developed (N = 4 in this study). Each sensor uses a safe technology - short cables, preamplification/digitization close to the patient, and optical transmission - and provides three bipolar voltage leads. A matrix combination is applied to reconstruct a 12-lead ECG from the raw network signals. A subject-specific calibration is performed to identify the matrix coefficients, maximizing the similarity with a true 12-lead ECG, acquired with a conventional 12-lead device outside the scan room. The sensor network was subjected to radiofrequency heating phantom tests at 3T. It was then tested in four subjects, both at 1.5T and 3T. Radiofrequency heating at 3T was within the MR-compatibility standards. The reconstructed 12-lead ECG showed minimal MHD artifacts and its morphology compared well with that of the true 12-lead ECG, as measured by correlation coefficients above 93% (respectively, 84%) for the QRS complex shape during steady-state free precession (SSFP) imaging at 1.5T (respectively, 3T). High-quality 12-lead ECG can be reconstructed by the proposed sensor network at 1.5T and 3T with reduced MHD artifacts compared to previous systems. The system might help improve patient monitoring and triggering and might also be of interest for interventional MRI and advanced cardiac MR applications.

Analytical composite performance models for Big Data applications

Recent years witnessed a steep rise in data generation and, consequently, the widespread adoption of software solutions able to support data-intensive applications. Many companies currently engage in data-intensive processes, however, fully embracing a data-driven paradigm is still cumbersome, and establishing a production-ready and fine-tuned deployment is time-consuming. This situation calls for innovative models and techniques to streamline the process of deployment configuration for Big Data applications. Moreover, many companies are using Cloud deployed clusters, which represent a cost-effective alternative to installation on premises. Accurate and fast prediction of the execution time of a Big Data application helps improving design time decisions, reduces Cloud over allocation charges, and assists budget management. In this paper, analytical models based on Stochastic Activity Networks (SANs) are proposed to accurately model the execution of Hadoop, Tez and Spark applications, i.e., the most referred frameworks to support Big Data analyses. The proposed SANs model these applications together with the underlying cluster in order to accurately estimate the execution time. We evaluate the accuracy of the proposed models over the TPC-DS industry benchmark across different configurations. Results obtained by numerically solving the SAN models show an average error of 4.5%, 5.8%, and 2.7% in estimating the execution time of MR, Tez, and Spark applications, respectively, against the data gathered from the experiments demonstrating higher accuracy compared with the state-of-the-art. Moreover, the time required to solve the proposed models is lower than the simulation time of the previously presented approaches in this area.

Sparsity and locally low rank regularization for MR fingerprinting.

PurposeDevelop a sparse and locally low rank (LLR) regularized reconstruction to accelerate MR fingerprinting (MRF).MethodsRecent works have introduced low rank reconstructions to MRF, based on temporal compression operators learned from the MRF dictionary. In other MR applications, LLR regularization has been introduced to exploit temporal redundancy in local regions of the image. Here, we propose to include spatial sparsity and LLR regularization terms in the MRF reconstruction. This approach, so called SLLR‐MRF, further reduces aliasing in the time‐point images and enables higher acceleration factors. The proposed approach was evaluated in simulations, T1/T2 phantom acquisition, and in vivo brain acquisitions in 5 healthy subjects with different undersampling factors. Acceleration was also used in vivo to enable acquisitions with higher in‐plane spatial resolution in comparable scan time.ResultsSimulations, phantom, and in vivo results show that low rank MRF reconstructions with high acceleration factors (<875 time‐point images, 1 radial spoke per time‐point) have residual aliasing artifacts that propagate into the parametric maps. The artifacts are reduced with the proposed SLLR‐MRF resulting in considerable improvements in precision, without changes in accuracy. In vivo results show improved parametric maps for the proposed SLLR‐MRF, potentially enabling MRF acquisitions with 1 radial spoke per time‐point in approximately 2.6 s (~600 time‐point images) for 2 × 2 mm and 9.6 s (1750 time‐point images) for 1 × 1 mm in‐plane resolution.ConclusionThe proposed SLLR‐MRF reconstruction further improves parametric map quality compared with low rank MRF, enabling shorter scan times and/or increased spatial resolution.

A Multi-Source Adaptive MR Image Fusion Technique for MR-Guided Radiation Therapy

Current radiation oncology practice to capture clinical indications like tumor from MR images is to review multiple types of MRI separately, which can be inefficient and tumor contrast is limited by existing MRI. The purpose of this study is to develop an integrated MR image fusion technique to adaptively fuse multi-source standard MRI for improved MR-guided radiation therapy applications. The hypothesis is that by integrating multi-source MR images, tumor contrast can be enhanced and the efficiency of using multiple MR images can be improved. The multi-source adaptive MRI fusion technique is designed with five key components: input multiple MRI, image pre-processing, fusion algorithm, adaptation methods, and output fused MRI. T1-weighted (T1-w), T2-weighted (T2-w), cine and diffusion-weighted MRI (DWI) were used as source images. Deformable image registration is applied to align all images to T2-w MRI. A linear-weighting algorithm Yi = ∑wik⋅Xk is used for fusion, where Xk is the kth source image, wik is the weighting factor of the kth source image in the ith fused image Yi, w∈[-1,1] with an interval of 0.2. Features of fused images are extracted, including signal-to-noise-ratio (SNR) and contrast-to-noise-ratio (CNR) of tumor and major organs. The weighting factors and image features are pre-calculated and saved in a database for fast adaptation operation. Weighting-factor driven and image-feature driven approaches are developed for MRI fusion adaptation. Digital human phantom (XCAT) and seven liver cancer patients were evaluated. Multiple new organ contrasts were explored and tumor CNR were quantified and compared before and after adaptive fusion using Student’s t-test. Hundreds of fused MR images with versatile image contrasts were generated in both XCAT phantom and liver cancer patients. In XCAT phantom, versatile new organ contrasts were achieved, including tumor, vertebral body, and blood vessel enhancement, respectively. In the patient study, tumor contrasts were significantly improved (mean: 14.83 ± 7.72 vs. 1.5 ± 0.65, 2.54 ± 1.31, 2.24 ± 2.23, 3.72 ± 2.45 and 0.74 ± 0.66 in T1-w, T2-w, cine, DWI, and CT, respectively) (p-values: 0.003∼0.010). Multiple new contrast MR images were efficiently generated, including liver, vertebral body, and cerebrospinal fluid enhancement, respectively. A novel multi-source adaptive image fusion technique capable of generating versatile contrast MRI from a limited number of standard MR images was developed. This method has the potential to improve tumor contrast, as well as the effectiveness and efficiency of multiple MR applications in radiation therapy.

The Emerging Role of 7 Tesla MRI in Musculoskeletal Imaging

To describe the emergent role of ultra-high field (UHF) MR with respect to musculoskeletal MRI applications. With the recent US Federal Drug Administration (FDA) and European Union (EU) approval of ultra-high field (UHF) MRI below 8T for clinical use, and the availability of clinical 7T MRI systems, there is a rising interest in the potential clinical and research applications in musculoskeletal MRI. With increases in field strength and SNR gains resulting in sharper and higher spatial resolution MRI images, there is increasing interest in UHF MRI. Although there are challenges and limitations in UHF, there are many new and unique musculoskeletal MR applications that UHF excels at such as morphological imaging, bone micro-architecture evaluation, biochemical imaging techniques such gagCEST, UTE/ZTE, T2 mapping, T2* mapping, T1ρ mapping, and multi-nuclear imaging and spectroscopy/imaging techniques with 23Na and 31P. The goal of this review is to highlight some of these recent findings in musculoskeletal MRI applications at UHF.

High peak and high average radiofrequency power transmit/receive switch for thermal magnetic resonance.

To study the role of temperature in biological systems, diagnostic contrasts and thermal therapies, RF pulses for MR spin excitation can be deliberately used to apply a thermal stimulus. This application requires dedicated transmit/receive (Tx/Rx) switches that support high peak powers for MRI and high average powers for RF heating. To meet this goal, we propose a high-performance Tx/Rx switch based on positive-intrinsic-negative diodes and quarter-wavelength (λ/4) stubs. The λ/4 stubs in the proposed Tx/Rx switch design route the transmitted RF signal directly to the RF coil/antenna without passing through any electronic components (e.g., positive-intrinsic-negative diodes). Bench measurements, MRI, MR thermometry, and RF heating experiments were performed at f = 297 MHz (B0 = 7 T) to examine the characteristics and applicability of the switch. The proposed design provided an isolation of -35.7dB/-41.5dB during transmission/reception. The insertion loss was -0.41dB/-0.27dB during transmission/reception. The switch supports high peak (3.9 kW) and high average (120 W) RF powers for MRI and RF heating at f = 297 MHz. High-resolution MRI of the wrist yielded image quality competitive with that obtained with a conventional Tx/Rx switch. Radiofrequency heating in phantom monitored by MR thermometry demonstrated the switch applicability for thermal modulation. Upon these findings, thermally activated release of a model drug attached to thermoresponsive polymers was demonstrated. The high-power Tx/Rx switch enables thermal MR applications at 7 T, contributing to the study of the role of temperature in biological systems and diseases. All design files of the switch will be made available open source at www.opensourceimaging.org.

Sensitivity estimation in circular and square loops

AbstractCircular and square loops are the simplest design employed as radiofrequency coils for magnetic resonance applications. Being usually much smaller than the volume coils, these loop coils have higher signal‐to‐noise ratio (SNR) because they receive noises only from nearby regions. However, they have a relatively poor field homogeneity and, thus, are mainly used as receive coils. In this work, author describes a coil sensitivity estimation method based on a simulator which permits the characterization of loop coils performance, in terms of magnetic field patterns and coil losses, where this last term including conductor, sample, capacitor, radiative and soldering losses. The evaluation of coil sensitivity for circular and square loops was performed at different tuning frequencies usually used in clinical scanner (21, 64, and 128 MHz) and at different distances respect to the coil plane. Simulation data were acquired for circular loop radius between 0.5 and 4 cm and for square loop side sizes between 1 and 8 cm, with the scope of evaluating the loop sizes which maximize the sensitivity in dependence of the penetration depth in the sample region and therefore to design optimized coils for maximizing SNR in MR applications.

Multimodal MR imaging in hepatic encephalopathy: state of the art.

Hepatic encephalopathy (HE) is a neurological or neuropsychological complication due to liver failure or portosystemic shunting. The clinical manifestation is highly variable, which can exhibit mild cognitive or motor impairment initially, or gradually progress to a coma, even death, without treatment. Neuroimaging plays a critical role in uncovering the neural mechanism of HE. In particular, multimodality MR imaging is able to assess both structural and functional derangements of the brain with HE in focal or neural network perspectives. In recent years, there has been rapid development in novel MR technologies and applications to investigate the pathophysiological mechanism of HE. Therefore, it is necessary to update the latest MR findings regarding HE by use of multimodality MRI to refine and deepen our understanding of the neural traits in HE. Herein, this review highlights the latest MR imaging findings in HE to refresh our understanding of MRI application in HE.

Advances in MRI Methodology.

Magnetic resonance imaging (MRI) is a non-invasive technique which can provide comprehensive, multi-parametric information on brain anatomy, function and metabolism. This chapter will give a brief introduction to the basic principles of MR and cover the evolution of image acquisition. Given the versatility of MR, a vast range of MR applications will be discussed, including structural, diffusion tensor, functional, perfusion and neuromelanin-sensitive imaging, as well as quantitative susceptibility mapping. This chapter will also cover methodological developments in MRI, including image analysis approaches that can be tailored according to experimental design or aims. The ultimate goal of this chapter is to equip readers with a fundamental understanding of the proficiencies and limitations of MR techniques and their corresponding analysis approaches.

A Novel Framework to optimize I/O Cost in MapReduce: An Index based Solution

Hadoop-MapReduce framework was the first framework offering distributed data processing with features like scalability, fault-tolerance, flexible programming model etc. It gained its popularity by the influx of heavy business intelligence and data analytics applications. The high performance of Map-Reduce in such applications requires selective data access and lower response time. Though there has been a variety of frameworks proposed in the state-of-art to improve MapReduce performance by integrating DBMS like features such as indexes, modelling map and reduce phases, using different data layouts etc; they require changes in the existing underlying storage system (HDFS). This paper proposes a novel framework to create indexes based on HDFS splits. The framework allows MapReduce applications to access only relevant data a query is looking for without making any changes in existing data layouts or file organization in HDFS. As the main factors that affect MapReduce application performance are IO usage and CPU usage, the key objective of this work is to optimize IO cost of MR applications because most Hadoop jobs are IO bound.

A multisource adaptive magnetic resonance image fusion technique for versatile contrast magnetic resonance imaging

Aim: Magnetic resonance imaging (MRI) has been widely used in radiation therapy (RT) treatment planning. The current practice to capture clinical indications like tumor from MRI is to review multiple types of MRI separately, which can be inefficient and the tumor contrast is limited by existing images. This study presented a novel approach to effectively integrate clinical meaningful information of multiple MRI to produce a set of fused MRI with versatile image contrasts. A multisource adaptive fusion technique was developed in this approach using limited number of standard MR images as input. Methods: The multisource adaptive MRI fusion technique is designed with five key components: input multiple MRI, image preprocessing, fusion algorithm, adaptation methods, and output-fused MRI. A linear-weighting fusion algorithm is used to demonstrate the proof of concept. Fusion options (weighting parameters and image features) are precalculated and saved in a database for fast fusion operation. Input- and output-driven approaches are developed for MRI contrast adaptation. The technique is tested in human digital phantom 4D extended cardiac-torso (XCAT) for versatile contrast MRI generation. Results: A graphic user interface was developed in Matlab environment. Input- and output-driven adaptation methods were implemented for interactive user operation to achieve different clinical goals. Using four input MR images (T1W, T2W, T2/T1W, and diffusion weighted), the fusion technique generated hundreds of fused MR images with versatile image contrasts. Conclusion: A novel multisource adaptive image fusion technique capable of generating versatile contrast MRI from a limited number of standard MR images was demonstrated. This method has the potential to enhance the effectiveness and efficiency of MR applications in RT.