Real-time Magnetic Resonance Imaging Research Articles

Real-time MRI of the moving wrist at 0.55 tesla.

Magnetic resonance imaging (MRI) using 1.5T or 3.0T systems is routinely employed for assessing wrist pathology; however, due to off-resonance artifacts and high power deposition, these high-field systems have drawbacks for real-time (RT) imaging of the moving wrist. Recently, high-performance 0.55T MRI systems have become available. In this proof-of-concept study, we tested the hypothesis that RT-MRI during continuous, active, and uninterrupted wrist motion is feasible with a high-performance 0.55T system at temporal resolutions below 100 ms and that the resulting images provide visualization of tissues commonly interrogated for assessing dynamic wrist instability. Participants were scanned during uninterrupted wrist radial-ulnar deviation and clenched fist maneuvers. Resulting images (nominal temporal resolution of 12.7-164.6 ms per image) were assessed for image quality. Feasibility of static MRI to supplement RT-MRI acquisition was also tested. The RT images with temporal resolutions < 100 ms demonstrated low distortion and image artifacts, and higher reader assessment scores. Static MRI scans showed the ability to assess anatomical structures of interest in the wrist. RT-MRI of the wrist at a high temporal resolution, coupled with static MRI, is feasible with a high-performance 0.55T system, and may enable improved assessment of wrist dynamic dysfunction and instability. Real-time MRI of the moving wrist is feasible with high-performance 0.55T and may improve the evaluation of dynamic dysfunction of the wrist.

First experience with real-time magnetic resonance imaging-based investigation of respiratory influence on cardiac function in pediatric congenital heart disease patients with chronic right ventricular volume overload

BackgroundCongenital heart disease (CHD) is often associated with chronic right ventricular (RV) volume overload. Real-time magnetic resonance imaging (MRI) enables the analysis of cardiac function during free breathing.ObjectiveTo evaluate the influence of respiration in pediatric patients with CHD and chronic RV volume overload.Methods and materialsRV volume overload patients (n=6) and controls (n=6) were recruited for cardiac real-time MRI at 1.5 tesla during free breathing. Breathing curves from regions of interest reflecting the position of the diaphragm served for binning images in four different tidal volume classes, each in inspiration and expiration. Tidal volumes were estimated from these curves by data previously obtained by magnetic resonance-compatible spirometry. Ventricular volumes indexed to body surface area and Frank-Starling relationships referenced to the typical tidal volume indexed to body height (TTVi) were compared.ResultsIndexed RV end-diastolic volume (RV-EDVi) and indexed RV stroke volume (RV-SVi) increased during inspiration (RV-EDVi/TTVi: RV load: + 16 ± 4%; controls: + 22 ± 13%; RV-SVi/TTVi: RV load: + 21 ± 6%; controls: + 35 ± 17%; non-significant for comparison). The increase in RV ejection fraction during inspiration was significantly lower in RV load patients (RV load: + 1.1 ± 2.2%; controls: + 6.1 ± 1.5%; P=0.01). The Frank-Starling relationship of the RV provided a significantly reduced slope estimate in RV load patients (inspiration: RV load: 0.75 ± 0.11; controls: 0.92 ± 0.02; P=0.02).ConclusionIn pediatric patients with CHD and chronic RV volume overload, cardiac real-time MRI during free breathing in combination with respiratory-based binning indicates an impaired Frank-Starling relationship of the RV.Graphical

Real-time imaging of vocal tract configuration in Australian english vowel production

Dynamic phonetic properties of Australian English vowels have been well described in acoustic (Cox, 2006; Elvin et al., 2016) and articulographic studies (Ratko et al., 2023), but the global configuration of the vocal tract during Australian English vowel production has not previously been examined. Midsagittal configuration of the upper airway during production of Australian English vowels was tracked using real-time magnetic resonance imaging (Kennerley et al., 2022) on a 3T scanner (Siemens Magnetom Prisma) using a 64-channel head/neck receiver array coil. Speech audio was simultaneously recorded in-scanner at 16 kHz using a ceramic noise-canceling microphone (Opto-acoustics FOMRI-III). 3D configuration of the vocal tract during sustained productions of the same monophthongs was captured using volumetric imaging of the upper airway at a resolution of 2mm × 2 mm × 2 mm. These data offer new details on configuration of the entire vocal tract and the relationship between articulatory and acoustic targets during production of Australian English vowels.

Manganese Amplifies Photoinduced ROS in Toluidine Blue Carbon Dots to Boost MRI Guided Chemo/Photodynamic Therapy.

The contrast agents and tumor treatments currently used in clinical practice are far from satisfactory, due to the specificity of the tumor microenvironment (TME). Identification of diagnostic and therapeutic reagents with strong contrast and therapeutic effect remains a great challenge. Herein, a novel carbon dot nanozyme (Mn-CD) is synthesized for the first time using toluidine blue (TB) and manganese as raw materials. As expected, the enhanced magnetic resonance (MR) imaging capability of Mn-CDs is realized in response to the TME (acidity and glutathione), and r1 and r2 relaxation rates are enhanced by 224% and 249%, respectively. In addition, the photostability of Mn-CDs is also improved, and show an efficient singlet oxygen (1 O2 ) yield of 1.68. Moreover, Mn-CDs can also perform high-efficiency peroxidase (POD)-like activity and catalyze hydrogen peroxide to hydroxyl radicals, which is greatly improved under the light condition. The results both in vitro and in vivo demonstrate that the Mn-CDs are able to achieve real-time MR imaging of TME responsiveness through aggregation of the enhanced permeability and retention effect at tumor sites and facilitate light-enhanced chemodynamic and photodynamic combination therapies. This work opens a new perspective in terms of the role of carbon nanomaterials in integrated diagnosis and treatment of diseases.

Real-Time Strain-Encoding Cardiovascular MRI for Assessment of Regional Heart Function in Tetralogy of Fallot Patients.

Tetralogy of Fallot (ToF) is the most common form of cyanotic congenital heart disease, where right ventricular (RV) function is an important determinant of subsequent intervention. In this study, we evaluate the feasibility of fast strain-encoding (fastSENC; a one-heartbeat sequence) magnetic resonance imaging (MRI) for assessing regional cardiac function in ToF. FastSENC was implemented to characterize regional circumferential (Ecc) and longitudinal (Ell) strains in the left ventricle (LV) and RV in post-repair ToF. Data analysis was conducted to compare strain measurements in the RV to those in the LV, as well as to those generated by the MRI Tissue-Tracking (MRI-TT) technique, and to assess the relationship between strain and ejection fraction (EF). Despite normal LVEF (55±8.5%), RVEF was borderline (46±6.4%), but significantly lower than LVEF. RV strains (RV-Ell=-20.2±2.9%, RV-Ecc=-15.7±6.4%) were less than LV strains (LV-Ell=-21.7±3.7%, LV-Ecc=-18.3±4.7%), and Ell was the dominant strain component. Strain differences between fastSENC and MRI-TT were less significant in RV than in LV. There existed moderate and weak correlations for RV-Ecc and RV-Ell, respectively, against RVEF. Compared to LV strain, RV strain showed regional heterogeneity with a trend for reduced strain from the inferior to anterior regions. Inter-ventricular strain delay was larger for Ell (64±47ms) compared to Ecc (36±40ms), reflecting a trend for contraction dyssynchrony. FastSENC allows for characterizing subclinical regional RV dysfunction in ToF. Due to its sensitivity for evaluating regional myocardial contractility patterns and real-time imaging capability without the need for breath-holding, fastSENC makes it more suitable for evaluating RV function in ToF.

Probability of Cavitation in a Custom Iron-Based Coupling Medium for Transcranial Magnetic Resonance-Guided Focused Ultrasound Procedures.

A coupling bath of circulating, chilled, degassed water is essential to safe and precise acoustic transmittance during transcranial magnetic resonance-guided focused ultrasound (tMRgFUS) procedures, but the circulating water impairs the critical real-time magnetic resonance imaging (MRI). An iron-based coupling medium (IBCM) using iron oxide nanoparticles previously developed by our group increased the relaxivity of the coupling bath such that it appears to be invisible on MRI compared with degassed water. However, the nanoparticles also reduced the pressure threshold for cavitation. To address this concern for prefocal cavitation, our group recently developed an IBCM of electrosterically stabilized and aggregation-resistant poly(methacrylic acid)-coated iron oxide nanoparticles (PMAA-FeOX) with a similar capability to reduce the MR signal of degassed water. This study examines the effect of the PMAA-FeOX IBCM on the cavitation threshold. Increasing concentrations of PMAA-FeOX nanoparticles in degassed, deionized water were placed at the focus of two different transducers to assess low and high duty-cycle pulsing parameters which are representative of two modes of focused ultrasound being investigated for tMRgFUS. Passive cavitation detection and high-speed optical imaging were used to measure cavitation threshold pressures. The mean cavitation threshold was determined in both cases to be indistinguishable from the degassed water control, between 6-8 MPa for high duty-cycle pulsing (CW) and between 25.5-26.5 MPa for very low duty-cycle pulsing. The findings of this study indicate that an IBCM of PMAA-FeOX nanoparticles is a possible solution to reducing MRI interference from the coupling bath without increasing the risk of prefocal cavitation.

Brain tumor detection model based on CNN and threshold segmentation

Brain tumours pose a substantial global health issue, emphasising the criticality of timely and precise identification to ensure optimal treatment outcomes. This research introduces an innovative methodology for the identification of brain tumours by employing a fusion of Convolutional Neural Networks (CNNs) with threshold segmentation techniques. The objective of the suggested model is to improve the precision and effectiveness of brain tumour identification in medical imaging, namely Magnetic Resonance Imaging (MRI) scans. Fast and precise diagnosis is necessary in the medical profession for effective treatment, but current technologies lack this capability. For successful therapy, it is therefore necessary to develop an effective diagnosis application. Global threshold segmentation for pre-processing is used in this study. Image capture and de-noising were completed in the first stage, while classification and regression were completed in the second stage using ML approaches. A computer-aided automated identification method is the computational technique used in this study. This investigation uses one hundred twenty (120) brain scans from a real-time MRI brain database, of them 15 normal and 105 abnormal. According to performance metrics, the accuracy of training and testing pictures was 99.46%. Comparing this method to recently published methods, it is determined that LR-ML with the threshold segmentation has a rapid, precise brain diagnostic system.

Effective Connectivity of the Bilateral Amygdala, Dorsomedial Prefrontal, and Subgenual Anterior Cingulate Cortices: Feasibility of Positive Social Emotion Regulation Models for Real-Time Functional Magnetic Resonance Imaging.

Effective connectivity based on functional magnetic resonance imaging (fMRI) allows assessing directions of interaction between brain regions. For real-time fMRI, we compared models of positive social emotion regulation based on a network involving the bilateral amygdala, dorsomedial prefrontal, and subgenual anterior cingulate cortex. The top-down regulation model implied modulation of the dorsomedial prefrontal cortex exerted onto other regions, while the bottom-up model implied the inverse modulation. The validity of model calculations was tested using the data from three healthy volunteers who imagined positive interactions with people in presented photos (stimuli). We confirmed the dominance of the top-down model and evaluated the number and duration of iterations required for model estimations. The study shows the applicability of the four-node effective connectivity models for regulation of positive social emotions using real-time fMRI, e.g., for neurofeedback applications.

An open-source toolbox for measuring vocal tract shape from real-time magnetic resonance images

Real-time magnetic resonance imaging (rtMRI) is a technique that provides high-contrast videographic data of human anatomy in motion. Applied to the vocal tract, it is a powerful method for capturing the dynamics of speech and other vocal behaviours by imaging structures internal to the mouth and throat. These images provide a means of studying the physiological basis for speech, singing, expressions of emotion, and swallowing that are otherwise not accessible for external observation. However, taking quantitative measurements from these images is notoriously difficult. We introduce a signal processing pipeline that produces outlines of the vocal tract from the lips to the larynx as a quantification of the dynamic morphology of the vocal tract. Our approach performs simple tissue classification, but constrained to a researcher-specified region of interest. This combination facilitates feature extraction while retaining the domain-specific expertise of a human analyst. We demonstrate that this pipeline generalises well across datasets covering behaviours such as speech, vocal size exaggeration, laughter, and whistling, as well as producing reliable outcomes across analysts, particularly among users with domain-specific expertise. With this article, we make this pipeline available for immediate use by the research community, and further suggest that it may contribute to the continued development of fully automated methods based on deep learning algorithms.

Deep Belief Networks (DBN) with IoT-Based Alzheimer’s Disease Detection and Classification

Dementias that develop in older people test the limits of modern medicine. As far as dementia in older people goes, Alzheimer’s disease (AD) is by far the most prevalent form. For over fifty years, medical and exclusion criteria were used to diagnose AD, with an accuracy of only 85 per cent. This did not allow for a correct diagnosis, which could be validated only through postmortem examination. Diagnosis of AD can be sped up, and the course of the disease can be predicted by applying machine learning (ML) techniques to Magnetic Resonance Imaging (MRI) techniques. Dementia in specific seniors could be predicted using data from AD screenings and ML classifiers. Classifier performance for AD subjects can be enhanced by including demographic information from the MRI and the patient’s preexisting conditions. In this article, we have used the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. In addition, we proposed a framework for the AD/non-AD classification of dementia patients using longitudinal brain MRI features and Deep Belief Network (DBN) trained with the Mayfly Optimization Algorithm (MOA). An IoT-enabled portable MR imaging device is used to capture real-time patient MR images and identify anomalies in MRI scans to detect and classify AD. Our experiments validate that the predictive power of all models is greatly enhanced by including early information about comorbidities and medication characteristics. The random forest model outclasses other models in terms of precision. This research is the first to examine how AD forecasting can benefit from using multimodal time-series data. The ability to distinguish between healthy and diseased patients is demonstrated by the DBN-MOA accuracy of 97.456%, f-Score of 93.187 %, recall of 95.789 % and precision of 94.621% achieved by the proposed technique. The experimental results of this research demonstrate the efficacy, superiority, and applicability of the DBN-MOA algorithm developed for the purpose of AD diagnosis.

A tumor microenvironment-responsive core-shell tecto dendrimer nanoplatform for magnetic resonance imaging-guided and cuproptosis-promoted chemo-chemodynamic therapy.

Theranostic nanoplatforms for combination tumor therapy have gained lots of attention recently due to the optimized therapeutic efficiency and simultaneous diagnosis performance. Herein, a novel tumor microenvironment (TME)-responsive core-shell tecto dendrimer (CSTD) was assembled by phenylboronic acid- and mannose-modified poly(amidoamine) dendrimers via the phenylboronic ester bonds that are responsive to low pH and reactive oxygen species (ROS), and efficiently loaded with copper ions and chemotherapeutic drug disulfiram (DSF) for tumor-targeted magnetic resonance (MR) imaging and cuproptosis-promoted chemo-chemodynamic therapy. The formed CSTD-Cu(II)@DSF could be specifically taken up by MCF-7 breast cancer cells, accumulated to the tumor model after circulation, and released drugs in response to the weakly acidic TME with overexpressed ROS. The enriched intracellular Cu(II) ions could induce the oligomerization of lipoylated proteins and proteotoxic stress for cuproptosis, and lipid peroxidation for chemodynamic therapy as well. Moreover, the CSTD-Cu(II)@DSF could cause the dysfunction of mitochondria and arrest the cell cycle at the G2/M phase, leading to enhanced DSF-mediated cell apoptosis. As a result, CSTD-Cu(II)@DSF could effectively inhibit the growth of MCF-7 tumors by a combination therapy strategy integrating chemotherapy with cuproptosis and chemodynamic therapy. Lastly, the CSTD-Cu(II)@DSF also displays Cu(II)-associated r1 relaxivity, allowing for T1-weighted real-time MR imaging of tumors in vivo. The developed tumor-targeted and TME-responsive CSTD-based nanomedicine formulation may be developed for accurate diagnosis and synergistic treatment of other cancer types. STATEMENT OF SIGNIFICANCE: Constructing an effective nanoplatform for the combination of therapeutic effects and real-time tumor imaging remains a challenge. In this study, we reported for the first time an all-in-one tumor-targeted and tumor microenvironment (TME) responsive nanoplatform based on core-shell tecto dendrimer (CSTD) for the cuproptosis-promoted chemo-chemodynamic therapy and enhanced MR imaging. The efficient loading, selective tumor-targeting, and TME-responsive release of Cu(II) and disulfiram could enhance the intracellular accumulation of drugs, induce cuproptosis of cancer cells, and amplify the synergistic chemo-chemodynamic therapeutic effect, resulting in enhanced MR imaging and accelerated tumor eradication. This study sheds new light on the development of theranostic nanoplatforms for early accurate diagnosis and effective treatment of cancers.

Advanced integration of stereotaxis and real-time MRI for precise and safe medical navigation: a future paradigm for minimally invasive interventions

Minimally invasive techniques have transformed medicine by improving patient outcomes and reducing invasiveness. Existing navigation methods, which use fluoroscopy or pre-operative imaging, lack real-time visualization and precision during complex surgeries. Fluoroscopy may also expose patients and medical staff to ionizing radiation. We propose enhanced stereotaxis and real-time magnetic resonance imaging (MRI) integration to overcome these problems and improve minimally invasive intervention precision and safety. Stereotactic guiding and high-resolution real-time MRI imaging are combined in this research to improve medical navigation. The conceptual framework includes modeling the stereotactic system's magnetic field, real-time tracking of magnetic-sensored medical devices, and dynamic MRI imaging for continuous visibility throughout treatments. Stereotactic and MRI data can be fused for simultaneous vision and navigation, and adaptive path planning algorithms allow real-time targeting and avoidance of key structures. A simulated cardiac electrophysiology catheter ablation treatment shows the combined approach's potential benefits. Real-time adaptive navigation reduces radiation exposure and problems while targeting precisely. This research establishes a new medical navigation paradigm that improves precision, patient safety, and radiation exposure. This integrated method could revolutionize minimally invasive procedures across medical disciplines, despite limitations in patient-specific data integration and real-time algorithm development. This new navigation approach needs further research, validation, and clinical trials to confirm its feasibility and efficacy and improve medical patient care

Surgical outcomes of open and laser interstitial thermal therapy approaches for corpus callosotomy in pediatric epilepsy.

Corpus callosotomy (CC) is a palliative surgical intervention for patients with medically refractory epilepsy that has evolved in recent years to include a less-invasive alternative with the use of laser interstitial thermal therapy (LITT). LITT works by heating a stereotactically placed laser fiber to ablative temperatures under real-time magnetic resonance imaging (MRI) thermometry. This study aims to (1) describe the surgical outcomes of CC in a large cohort of children with medically refractory epilepsy, (2) compare anterior and complete CC, and (3) review LITT as a surgical alternative to open craniotomy for CC. This retrospective cohort study included 103 patients <21 years of age with at least 1 year follow-up at a single institution between 2003 and 2021. Surgical outcomes and the comparative effectiveness of anterior vs complete and open versus LITT surgical approaches were assessed. CC was the most common surgical disconnection (65%, n = 67) followed by anterior two-thirds (35%, n = 36), with a portion proceeding to posterior completion (28%, n = 10). The overall surgical complication rate was 6% (n = 6/103). Open craniotomy was the most common approach (87%, n = 90), with LITT used increasingly in recent years (13%, n = 13). Compared to open, LITT had shorter hospital stay (3 days [interquartile range (IQR) 2-5] vs 5 days [IQR 3-7]; p < .05). Modified Engel class I, II, III, and IV outcomes at last follow-up were 19.8% (n = 17/86), 19.8% (n = 17/86), 40.2% (n = 35/86), and 19.8% (n = 17/86). Of the 70 patients with preoperative drop seizures, 75% resolved postoperatively (n = 52/69). No significant differences in seizure outcome between patients who underwent only anterior CC and complete CC were observed. LITT is a less-invasive surgical alternative to open craniotomy for CC, associated with similar seizure outcomes, lower blood loss, shorter hospital stays, and lower complication rates, but with longer operative times, when compared with the open craniotomy approach.

Diagnosis and treatment of the giant malignant peripheral nerve sheath tumor in the anterior mediastinum: A case report

INTRODUCTION: Malignant peripheral nerve sheath tumor (MPNST) occurs in young and middle-aged people, more frequently in those with the genetic disease known as neurofibromatosis type 1 (NF1). Approximately, 50% of people with MPNST have NF1 and 13% of people with NF1 will get MPNST during their lifetime.&#x0D; CASE REPORT: A 30-year-old patient after surgery for MPNST of the right hip in 2013 was observed. In 2017, a relapse was detected, and combined treatment was performed, including surgical excision of the relapse and postoperative distance radiotherapy with a total focal dose of 66 Gy. In 2018, MPNST of the left femoral nerve was diagnosed, and the tumor was excised. In 2020, a chest X-ray diagnosed a single focus of localized opacity in the anterior mediastinum, adjacent to the right lung. Computed tomography (CT) was performed to further verify the neoplasm, which revealed a single hypodense focus in the anterior mediastinum, adjacent to the right lung. According to histological examination of a tumor biopsy obtained by transthoracic ultrasound-guided biopsy, MPNST was verified. Magnetic resonance imaging (MRI) with intravenous contrast enhancement, including real-time MRI, was performed to assess the invasion of the mass into soft tissues and vessels and to plan surgical treatment. The following surgeries were done: right thoracotomy, removal of a neoplasm of the anterior mediastinum with resection of the upper lobe of the right lung, and marginal resections of the lower lobe of the right lung. This case demonstrates a rare localization and non-standard instrumental diagnosis of mediastinal MPNST. Apart from CT scanning, the patient underwent MRI of the thoracic organs. MRI allows assessing the invasion of the neoplasm into the surrounding tissues. This cannot always be achieved by CT scans, including those with contrast enhancement. Invasion is an important component in the planning of further surgical treatment tactics.&#x0D; CONCLUSIONS: MPNST is a tumor with an aggressive course and a poor prognosis due to resistance to therapy. This clinical case demonstrates a rare localized MPNST with a recurrent course and metastases to the lungs. The current radiological methods, such as CT and MRI, may be used effectively both for diagnosing MPNST and determining the appropriate surgical access to the lesion and the extent of surgery.

MFCC Parameters of the Speech Signal: An Alternative to Formant-Based Instantaneous Vocal Tract Length Estimation

On the one hand, the relationship between formant frequencies and vocal tract length (VTL) has been intensively studied over the years. On the other hand, the connection involving mel-frequency cepstral coefficients (MFCCs), which concisely codify the overall shape of a speaker’s spectral envelope with just a few cepstral coefficients, and VTL has only been modestly analyzed, being worth of further investigation. Thus, based on different statistical models, this article explores the advantages and disadvantages of the latter approach, which is relatively novel, in contrast to the former which arises from more traditional studies. Additionally, VTL is assumed to be a static and inherent characteristic of speakers, that is, a single length parameter is frequently estimated per speaker. By contrast, in this paper we consider VTL estimation from a dynamic perspective using modern real-time Magnetic Resonance Imaging (rtMRI) to measure VTL in parallel with audio signals. To support the experiments, data obtained from USC-TIMIT magnetic resonance videos were used, allowing for the 2D real-time analysis of articulators in motion. As a result, we observed that the performance of MFCCs in case of speaker-dependent modeling is higher, however, in case of cross-speaker modeling, which uses different speakers’ data for training and evaluating, its performance is not significantly different of that obtained with formants. In complement, we note that the estimation based on MFCCs is robust, with an acceptable computational time complexity, coherent with the traditional approach.

Association of Internal and External Motion Based on Cine MR Images Acquired During Real-Time Treatment on MRI–Guided Linear Accelerator for Patients With Lung Cancer

With the recent clinical implementation of magnetic resonance imaging (MRI)-guided linear accelerators, a large number of real-time planar MR images has been acquired during lung cancer treatment as a standard of care. In this study, associations among lung tumor, diaphragm, and external skin movement were studied based on MR cine imaging during the entire duration of each treatment fraction. This retrospective study used 181,798 planar MRI frames acquired over 55 treatment/imaging sessions of 13 patients with lung cancer treated on 2 MRI-guided linear accelerators. From each planar MR image frame, in-house software automatically extracted 9 features: the superior-interior (SI) and posterior-anterior (PA) positions of a lung tumor; the area of the lung (Lung_Area); the posterior (Dia_Post), dome/apex (Dia_Dome), and anterior (Dia_Ant) points of a diaphragmatic curve; the diaphragm curve point (Dia_Max); and the chest (Chest) and belly (Belly) skin points experienced the maximum range of motions. Correlation analyses were performed among the 9 features for every session. Lung tumor motion range and standard deviations were calculated based on positions obtained in cine images and compared with motion ranges obtained from 4-dimensional computed tomography images. In the study, 177,009 frames of images were successfully analyzed. For all patients, correlation coefficients were as follows: 0.91 ± 0.10 between any 2 features among Lung_Area, Dia_Post, Dia_Dome, and Dia_Max; 0.82 ± 0.21 between SI and any feature among Lung_Area, Dia_Post, Dia_Dome, and Dia_Max; 0.75 ± 0.24 between SI and Belly. Six of 13 patients were considered large amplitude motion (patients with lung tumor SI motion standard deviation >5 mm). Furthermore, 92,956 frames of images were analyzed for the 6 large-amplitude motion patients. For this set, correlation coefficients were 0.93 ± 0.07 between any 2 features among Lung_Area, Dia_Post, Dia_Dome, and Dia_Max; 0.94 ± 0.06 between SI and any feature among Lung_Area, Dia_Post, Dia_Dome, and Dia_Max; and 0.90 ± 0.09 between SI and Belly. Both belly and diaphragmatic motions as assessed by cine MRI are highly correlated with large amplitude lung tumor motion in the longitudinal axis.

Self-Propelled Janus Nanocatalytic Robots Guided by Magnetic Resonance Imaging for Enhanced Tumor Penetration and Therapy

Biomedical micro/nanorobots as active delivery systems with the features of self-propulsion and controllable navigation have made tremendous progress in disease therapy and diagnosis, detection, and biodetoxification. However, existing micro/nanorobots are still suffering from complex drug loading, physiological drug stability, and uncontrollable drug release. To solve these problems, micro/nanorobots and nanocatalytic medicine as two independent research fields were integrated in this study to achieve self-propulsion-induced deeper tumor penetration and catalytic reaction-initiated tumor therapy in vivo. We presented self-propelled Janus nanocatalytic robots (JNCRs) guided by magnetic resonance imaging (MRI) for in vivo enhanced tumor therapy. These JNCRs exhibited active movement in H2O2 solution, and their migration in the tumor tissue could be tracked by non-invasive MRI in real time. Both increased temperature and reactive oxygen species production were induced by near-infrared light irradiation and iron-mediated Fenton reaction, showing great potential for tumor photothermal and chemodynamic therapy. In comparison with passive nanoparticles, these self-propelled JNCRs enabled deeper tumor penetration and enhanced tumor therapy after intratumoral injection. Importantly, these robots with biocompatible components and byproducts exhibited biosecurity in the mouse model. It is expected that our work could promote the combination of micro/nanorobots and nanocatalytic medicine, resulting in improved tumor therapy and potential clinical transformations.

Accelerated partial separable model using dimension-reduced optimization technique for ultra-fast cardiac MRI

Objective. Imaging dynamic objects with high temporal resolution is challenging in magnetic resonance imaging (MRI). The partial separable (PS) model was proposed to improve imaging quality by reducing the degrees of freedom of the inverse problem. However, the PS model still suffers from a long acquisition time and an even longer reconstruction time. The main objective of this study is to accelerate the PS model, shorten the time required for acquisition and reconstruction, and maintain good image quality simultaneously. Approach. We proposed to fully exploit the dimension-reduction property of the PS model, which means implementing the optimization algorithm in subspace. We optimized the data consistency term and used a Tikhonov regularization term based on the Frobenius norm of temporal difference. The proposed dimension-reduced optimization technique was validated in free-running cardiac MRI. We have performed both retrospective experiments on a public dataset and prospective experiments on in vivo data. The proposed method was compared with four competing algorithms based on the PS model and two non-PS model methods. Main results. The proposed method has robust performance against a shortened acquisition time or suboptimal hyper-parameter settings, and achieves superior image quality over all other competing algorithms. The proposed method is 20-fold faster than the widely accepted PS+sparse method, enabling image reconstruction to be finished in just a few seconds. Significance. The accelerated PS model has the potential to save a great deal of time in clinical dynamic MRI examinations and is promising for real-time MRI applications.

Accelerating GRAPPA reconstruction using SoC design for real-time cardiac MRI

Real-time cardiac MRI is a rapidly developing area of research that has the potential to improve the diagnosis and treatment of cardiovascular diseases. However, the acquisition of high-quality real-time cardiac MR (CMR) images is challenging as it requires a high frame rate and temporal resolution. To overcome this challenge, there have been recent efforts on several approaches including hardware-based improvements and image reconstruction techniques such as compressed sensing and parallel MRI. The use of parallel MRI techniques such as GRAPPA (Generalized Autocalibrating Partial Parallel Acquisition) is a promising approach for improving the temporal resolution of MRI and expanding its applications in clinical practice. However, the GRAPPA algorithm involves a significant amount of computation, particularly for high acceleration factors and large datasets. This can result in long reconstruction times, which can limit the ability to achieve real-time imaging or high frame rates. One solution to this challenge is to use specialized hardware i.e. field-programmable gate arrays (FPGAs). In this work, a novel 32-bit floating-point FPGA-based GRAPPA accelerator is proposed with an aim to reconstruct high-quality cardiac MR images at higher frame rates, making it well suited for real-time clinical applications. The proposed FPGA-based accelerator consists of custom-designed data processing units named as dedicated computational engines (DCEs) that allow for a continuous flow of data between the calibration and synthesis stages of GRAPPA reconstruction process. This greatly increases the throughput and reduces the latency of the overall proposed system. Moreover, a high-speed memory module (DDR4-SDRAM) is integrated with the proposed architecture to store the multi-coil MR data. An on-chip quad-core ARM Cortex-A53 processor is used to manage access control information required for data transfer between the DCEs and DDR4-SDRAM. The proposed accelerator is implemented on Xilinx Zynq UltraScale + MPSoC using high-level synthesis (HLS) and hardware descriptive language (HDL) with an aim to explore the trade-offs between the reconstruction time, resource utilization and design effort. Several experiments have been performed using in-vivo cardiac datasets i.e. 18-receiver coil and 30-receiver coil to evaluate the performance of the proposed accelerator. A comparison is performed with the contemporary CPU and GPU-based GRAPPA reconstruction methods in terms of reconstruction time, frames-per-second and reconstruction accuracy (RMSE and SNR). The results show that the proposed accelerator achieves speed-up factors up to 121× and 9× as compared to the contemporary CPU-based and GPU-based GRAPPA reconstruction methods, respectively. Moreover, it has been demonstrated that the proposed accelerator can achieve reconstruction rates of up to ∼27 frames-per-second while maintaining the visual quality of the reconstructed images.

Maximum velocity projections within 30 seconds: a combination of real-time three-directional flow MRI and cross-sectional volume coverage.

This work is a proof-of-concept realization of a novel technique for rapid volumetric acquisition, reconstruction, and visualization of three-directional (3dir) flow velocities. The technique combines real-time 3dir phase-contrast (PC) flow magnetic resonance imaging (MRI) with real-time cross-sectional volume coverage. It offers a rapid examination without dependence on electrocardiography (ECG) or respiratory gating during a continuous image acquisition at up to 16 fps. Real-time flow MRI utilizes pronounced radial undersampling and a model-based nonlinear inverse reconstruction. Volume coverage is achieved by automatically advancing the slice position of each PC acquisition by a small percentage of the slice thickness. Post-processing involves the calculation of maximum intensity projections along the slice dimension resulting in six direction-selective velocity maps and a maximum speed map. Preliminary applications to healthy subjects at 3 T comprise mapping of the carotid arteries and cranial vessels at 1.0 mm in-plane resolution within 30 s as well as of the aortic arch at 1.6 mm resolution within 20 s. In conclusion, the proposed method for rapid mapping of 3dir flow velocities offers a quick assessment of the vasculature either to provide a first clinical survey or to plan for more detailed studies.