Stereo reconstruction from microscopic images for computer-assisted ophthalmic surgery.

PURPOSE: This work presents a novel platform for stereo reconstruction in anterior segment ophthalmic surgery to enable enhanced scene understanding, especially depth perception, for advanced computer-assisted eye surgery by effectively addressing the lack of texture and corneal distortions artifacts in the surgical scene. METHODS: The proposed platform for stereo reconstruction uses a two-step approach: generating a sparse 3D point cloud from microscopic images, deriving a dense 3D representation by fitting surfaces onto the point cloud, and considering geometrical priors of the eye anatomy. We incorporate a pre-processing step to rectify distortion artifacts induced by the cornea's high refractive power, achieved by aligning a 3D phenotypical cornea geometry model to the images and computing a distortion map using ray tracing. RESULTS: The accuracy of 3D reconstruction is evaluated on stereo microscopic images of ex vivo porcine eyes, rigid phantom eyes, and synthetic photo-realistic images. The results demonstrate the potential of the proposed platform to enhance scene understanding via an accurate 3D representation of the eye and enable the estimation of instrument to layer distances in porcine eyes with a mean average error of 190  µ m , comparable to the scale of surgeons' hand tremor. CONCLUSION: This work marks a significant advancement in stereo reconstruction for ophthalmic surgery by addressing corneal distortions, a previously often overlooked aspect in such surgical scenarios. This could improve surgical outcomes by allowing for intra-operative computer assistance, e.g., in the form of virtual distance sensors.

Repeatability of Open-MOLLI: An open-source inversion recovery myocardial T1 mapping sequence for fast prototyping.

PURPOSE: To develop an open-source prototype of myocardial T1 mapping (Open-MOLLI) to improve accessibility to cardiac T1 mapping and evaluate its repeatability. With Open-MOLLI, we aim to enable faster implementation and testing of sequence modifications and to facilitate inter-scanner and cross-vendor reproducibility studies. METHODS: Open-MOLLI is an inversion-recovery sequence using a balanced SSFP (bSSFP) readout, with inversion and triggering schemes based on the 5(3)3 MOLLI sequence, developed in Pulseq. Open-MOLLI and MOLLI sequences were acquired in the ISMRM/NIST phantom and 21 healthy volunteers. In 18 of those subjects, Open-MOLLI and MOLLI were repeated in the same session (test-retest). RESULTS: Phantom T1 values were comparable between methods, specifically for the vial with reference T1 value most similar to healthy myocardium T1 (T1vial3 = 1027 ms): T1MOLLI = 1011 ± 24 ms versus T1Open-MOLLI = 1009 ± 20 ms. In vivo T1 estimates were similar between Open-MOLLI and MOLLI (T1MOLLI = 1004 ± 33 ms vs. T1Open-MOLLI = 998 ± 52 ms), with a mean difference of -17 ms (p = 0.20), despite noisier Open-MOLLI weighted images and maps. Repeatability measures were slightly higher for Open-MOLLI (RCMOLLI = 3.0% vs. RCOpen-MOLLI = 4.4%). CONCLUSION: The open-source sequence Open-MOLLI can be used for T1 mapping in vivo with similar mean T1 values to the MOLLI method. Open-MOLLI increases the accessibility to cardiac T1 mapping, providing also a base sequence to which further improvements can easily be added and tested.

Impact of truncating diffusion MRI scans on diffusional kurtosis imaging.

OBJECTIVE: Diffusional kurtosis imaging (DKI) extends diffusion tensor imaging (DTI), characterizing non-Gaussian diffusion effects but requires longer acquisition times. To ensure the robustness of DKI parameters, data acquisition ordering should be optimized allowing for scan interruptions or shortening. Three methodologies were used to examine how reduced diffusion MRI scans impact DKI histogram-metrics: 1) the electrostatic repulsion model (OptEEM); 2) spherical codes (OptSC); 3) random (RandomTRUNC). MATERIALS AND METHODS: Pre-acquired diffusion multi-shell data from 14 female healthy volunteers (29±5 years) were used to generate reordered data. For each strategy, subsets containing different amounts of the full dataset were generated. The subsampling effects were assessed on histogram-based DKI metrics from tract-based spatial statistics (TBSS) skeletonized maps. To evaluate each subsampling method on simulated data at different SNRs and the influence of subsampling on in vivo data, we used a 3-way and 2-way repeated measures ANOVA, respectively. RESULTS: Simulations showed that subsampling had different effects depending on DKI parameter, with fractional anisotropy the most stable (up to 5% error) and radial kurtosis the least stable (up to 26% error). RandomTRUNC performed the worst while the others showed comparable results. Furthermore, the impact of subsampling varied across distinct histogram characteristics, the peak value the least affected (OptEEM: up to 5% error; OptSC: up to 7% error) and peak height (OptEEM: up to 8% error; OptSC: up to 11% error) the most affected. CONCLUSION: The impact of truncation depends on specific histogram-based DKI metrics. The use of a strategy for optimizing the acquisition order is advisable to improve DKI robustness to exam interruptions.

Open-source magnetic resonance imaging: Improving access, science, and education through global collaboration.

Open-source practices and resources in magnetic resonance imaging (MRI) have increased substantially in recent years. This trend started with software and data being published open-source and, more recently, open-source hardware designs have become increasingly available. These developments towards a culture of sharing and establishing nonexclusive global collaborations have already improved the reproducibility and reusability of code and designs, while providing a more inclusive approach, especially for low-income settings. Community-driven standardization and documentation efforts are further strengthening and expanding these milestones. The future of open-source MRI is bright and we have just started to discover its full collaborative potential. In this review we will give an overview of open-source software and open-source hardware projects in human MRI research.

Impact of susceptibility-induced distortion correction on perfusion imaging by pCASL with a segmented 3D GRASE readout.

PURPOSE: The consensus for the clinical implementation of arterial spin labeling (ASL) perfusion imaging recommends a segmented 3D Gradient and Spin-Echo (GRASE) readout for optimal signal-to-noise-ratio (SNR). The correction of the associated susceptibility-induced geometric distortions has been shown to improve diagnostic precision, but its impact on ASL data has not been systematically assessed and it is not consistently part of pre-processing pipelines. Here, we investigate the effects of susceptibility-induced distortion correction on perfusion imaging by pseudo-continuous ASL (pCASL) with a segmented 3D GRASE readout. METHODS: Data acquired from 28 women using pCASL with 3D GRASE at 3T was analyzed using three pre-processing options: without distortion correction, with distortion correction, and with spatial smoothing (without distortion correction) matched to control for blurring effects induced by distortion correction. Maps of temporal SNR (tSNR) and relative perfusion were analyzed in eight regions-of-interest (ROIs) across the brain. RESULTS: Distortion correction significantly affected tSNR and relative perfusion across the brain. Increases in tSNR were like those produced by matched spatial smoothing in most ROIs, indicating that they were likely due to blurring effects. However, that was not the case in the frontal and temporal lobes, where we also found increased relative perfusion with distortion correction even compared with matched spatial smoothing. These effects were found in both controls and patients, with no interactions with the participant group. CONCLUSION: Correction of susceptibility-induced distortions significantly impacts ASL perfusion imaging using a segmented 3D GRASE readout, and this step should therefore be considered in ASL pre-processing pipelines. This is of special importance in clinical studies, reporting perfusion across ROIs defined on relatively undistorted images and when conducting group analyses requiring the alignment of images across different subjects.

Open-source myocardial T1 mapping with simultaneous multi-slice acceleration: Combining an auto-calibrated blipped-bSSFP readout with VERSE-MB pulses.

PURPOSE: Enabling fast and accessible myocardial T1 mapping is crucial for extending its clinical application. We introduce Open-MOLLI-SMS combining simultaneous multi-slice (SMS) with auto-calibration and variable-rate selective excitation (VERSE)-multiband pulses to obtain all slices in a fast single-shot T1 mapping sequence. METHODS: Open-MOLLI-SMS was developed by integrating SMS with the open-source method Open-MOLLI previously implemented in Pulseq. Three methods were integrated for Open-MOLLI-SMS: (1) auto-calibration blip patterns to ensure consistency between the data and coil information; (2) a blipped-balanced SSFP (bSSFP) readout to induce controlled aliasing in parallel imaging shifts without disturbing the bSSFP frequency response; and (3) a VERSE-multiband pulse for minimizing the achievable TR and the specific absortion rate (SAR) impact of SMS. Two (SMS2) or three (SMS3) slices were excited simultaneously and encoded with an in-plane acceleration factor of 2. Experiments were performed in the International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology phantom and five healthy volunteers. RESULTS: Phantom results show accurate T1 estimates for reference values between 400 to 2200 ms. Artifacts were visible for Open-MOLLI-SMS3 but not replicated in vivo. In vivo Open-MOLLI-SMS (T1 SMS2 = 993 ± 10 ms; T1 SMS3 = 1031 ± 17 ms) provided similar values to mean T1 single-band Open-MOLLI estimates (T1 Open-MOLLI = 1005 ± 47 ms). Open-MOLLI-SMS2 provided the closest estimates to the reference. CONCLUSION: This proof-of-principle implementation study demonstrates the feasibility of speeding up T1 -mapping acquisitions and increasing coverage by combining auto-calibration strategies with a blipped-bSFFP readout and VERSE multiband RF excitation pulses. The proposed methodology was built on the Open-MOLLI mapping sequence, which provides a fast means for prototyping and enables open-source sharing of the method.

Tailored magnetic resonance fingerprinting.

Neuroimaging of certain pathologies requires both multi-parametric qualitative and quantitative imaging. The role of the quantitative MRI (qMRI) is well accepted but suffers from long acquisition times leading to patient discomfort, especially in geriatric and pediatric patients. Previous studies show that synthetic MRI can be used in order to reduce the scan time and provide qMRI as well as multi-contrast data. However, this approach suffers from artifacts such as partial volume and flow. In order to increase the scan efficiency (the number of contrasts and quantitative maps acquired per unit time), we designed, simulated, and demonstrated rapid, simultaneous, multi-contrast qualitative (T1 weighted, T1 fluid attenuated inversion recovery (FLAIR), T2 weighted, water, and fat), and quantitative imaging (T1 and T2 maps) through the approach of tailored MR fingerprinting (TMRF) to cover whole-brain in approximately four minutes. We performed TMRF on in vivo four healthy human brains and in vitro ISMRM/NIST phantom and compared with vendor supplied gold standard (GS) and MRF sequences. All scans were performed on a 3 T GE Premier system and images were reconstructed offline using MATLAB. The reconstructed qualitative images were then subjected to custom DL denoising and gradient anisotropic diffusion denoising. The quantitative tissue parametric maps were reconstructed using a dense neural network to gain computational speed compared to dictionary matching. The grey matter and white matter tissues in qualitative and quantitative data for the in vivo datasets were segmented semi-automatically. The SNR and mean contrasts were plotted and compared across all three methods. The GS images show better SNR in all four subjects compared to MRF and TMRF (GS > TMRF>MRF). The T1 and T2 values of MRF are relatively overestimated as compared to GS and TMRF. The scan efficiency for TMRF is 1.72 min-1 which is higher compared to GS (0.32 min-1) and MRF (0.90 min-1).

Myocardial T1 mapping with magnetic resonance imaging - a useful tool to understand the diseased heart.

Cardiac magnetic resonance imaging (MRI) is increasingly used in clinical practice due to its versatility. T1 mapping of the myocardium, a recently introduced MRI technique that is becoming available enables quantitative tissue characterization, overcoming some of the limitations of late enhancement. This promising technique has the ability to identify diffuse myocardial fibrosis and is beginning to be used in the diagnostic and prognostic assessment of several heart diseases. In this review, we start by explaining the physical principles of myocardial T1 mapping and possible confounding factors in its measurement. We then analyze the evidence supporting its potential usefulness as a complement to the existing cardiac MRI methods. Finally, we discuss the current limitations of T1 mapping and possible areas of future research.

Open-source magnetic resonance imaging acquisition: Data and documentation for two validated pulse sequences.

Raw data, simulated and acquired phantom images, and quantitative longitudinal and transverse relaxation times (T1/T2) maps from two open-source Magnetic Resonance Imaging (MRI) pulse sequences are presented in this dataset along with corresponding ".seq" files, sequence implementation scripts, and reconstruction/analysis scripts [1]. Real MRI data were collected from a 3T Siemens Prisma Fit and a 1.5T Siemens Aera via the Pulseq open-source MR sequence platform, and corresponding in silico data were generated using the simulation module of Virtual Scanner [2]. This dataset and its associated code can be used to validate the pipeline for using the same pulse sequences at other research sites using Pulseq, to provide guidelines for documenting and sharing open-source pulse sequences in general, and to demonstrate practical, customizable acquisition scripts using the PyPulseq library.

Impact of white-matter mask selection on DTI histogram-based metrics as potential biomarkers in cerebral small vessel disease.

OBJECTIVE: Histogram-based metrics extracted from diffusion-tensor imaging (DTI) have been suggested as potential biomarkers for cerebral small vessel disease (SVD), but methods and results have varied across studies. This work aims to assess the impact of mask selection for extracting histogram-based metrics of fractional anisotropy (FA) and mean diffusivity (MD) on their sensitivity as SVD biomarkers. METHODS: DTI data were collected from 17 SVD patients and 12 healthy controls. FA and MD maps were estimated; from these, histograms were computed on two whole-brain white-matter masks: normal-appearing white-matter (NAWM) and mean FA tract skeleton (TBSS). Histogram-based metrics (median, peak height, peak width, peak value) were extracted from the FA and MD maps. These were compared between groups and correlated with the patients' cognitive scores (executive function and processing speed). RESULTS: White-matter mask selection significantly impacted FA and MD histogram metrics. In particular, significant interactions were found between Mask and Group for FA peak height (p = 0.027), MD Median (p = 0.035) and MD peak width (p = 0.047); indicating that the mask used affected their ability to discriminate between groups. In fact, MD peak width showed a significant 8.8% increase in patients when using TBSS (p = 0.037), but not when using NAWM (p = 0.69). Moreover, the mask may have an effect on the correlations with cognitive measures. Nevertheless, MD peak width (TBSS: r = - 0.75, NAWM: r = - 0.71) and MD peak height (TBSS: r = 0.65, NAWM: r = 0.62) remained significantly correlated with executive function, regardless of the mask. CONCLUSION: The impact of the processing methodology, in particular the choice of white-matter mask, highlights the need for standardized MRI data-processing pipelines.

A framework for validating open-source pulse sequences.

Open-source pulse sequence programs offer an accessible and transparent approach to sequence development and deployment. However, a common framework for testing, documenting, and sharing open-source sequences is still needed to ensure sequence usability and repeatability. We propose and demonstrate such a framework by implementing two sequences, Inversion Recovery Spin Echo (IRSE) and Turbo Spin Echo (TSE), with PyPulseq, and testing them on a commercial 3 T scanner. We used the ACR and ISMRM/NIST phantoms for qualitative imaging and T1/T2 mapping, respectively. The qualitative sequences show good agreement with vendor-provided counterparts (mean Structural Similarity Index Measure (SSIM) = 0.810 for IRSE and 0.826 for TSE). Both sequences passed five out of the seven standard ACR tests, performing at similar levels to vendor counterparts. Compared to reference values, the coefficient of determination R2 was 0.9946 for IRSE T1 mapping and 0.9331 for TSE T2 mapping. All sequences passed the scanner safety check for a 70 kg, 175 cm subject. The framework was demonstrated by packaging the sequences and sharing them on GitHub with data and documentation on the file generation, acquisition, reconstruction, and post-processing steps. The same sequences were tested at a second site using a 1.5 T scanner with the information shared. PDF templates for both sequence developers and users were created and filled.

Improving B1+ parametric estimation in the brain from multispin-echo sequences using a fusion bootstrap moves solver.

PURPOSE: To simultaneously estimate the B1+ field (along with the T2 ) in the brain with multispin-echo (MSE) sequences and dictionary matching. METHODS: T2 mapping provides clinically relevant information such as in the assessment of brain degenerative diseases. It is commonly obtained with MSE sequences, and accuracy can be further improved by matching the MSE signal to a precomputed dictionary of echo-modulation curves. For additional T1 quantification, transmit B1+ field knowledge is also required. Preliminary work has shown that although simultaneous brain B1+ estimation along with T2 is possible, it presents a bimodal distribution with the main peak coinciding with the true value. By taking advantage of this, the B1+ maps are expected to be spatially smooth by applying an iterative method that takes into account each pixel neighborhood known as the fusion bootstrap moves solver (FBMS). The effect of the FBMS on B1+ accuracy and piecewise smoothness is investigated and different spatial regularization levels are compared. Total variation regularization was used for both B1+ and T2 simultaneous estimation because of its simplicity as an initial proof-of-concept; future work could explore non edge-preserving regularization independently for B1+ . RESULTS: Improvements in B1+ accuracy (up to 45.37% and 16.81% B1+ error decrease) and recovery of spatially homogeneous maps are shown in simulations and in vivo 3.0T brain data, respectively. CONCLUSION: Accurate B1+ estimated values can be obtained from widely available MSE sequences while jointly estimating T2 maps with the use of echo-modulation curve matching and FBMS at no further cost.

Analysis Protocols for MRI Mapping of the Blood Oxygenation-Sensitive Parameters T2* and T2 in the Kidney.

Renal hypoxia is generally accepted as a key pathophysiologic event in acute kidney injury of various origins and has also been suggested to play a role in the development of chronic kidney disease. Here we describe step-by-step data analysis protocols for MRI monitoring of renal oxygenation in rodents via the deoxyhemoglobin concentration sensitive MR parameters T2* and T2-a contrast mechanism known as the blood oxygenation level dependent (BOLD) effect.This chapter describes how to use the analysis tools provided by vendors of animal and clinical MR systems, as well as how to develop an analysis software. Aspects covered are: data quality checks, data exclusion, model fitting, fitting algorithm, starting values, effects of multiecho imaging, and result validation.This chapter is based upon work from the PARENCHIMA COST Action, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers. This experimental protocol chapter is complemented by two separate chapters describing the basic concept and data analysis.

Low-Field MRI of Stroke: Challenges and Opportunities.

Stroke is a leading cause of death and disability worldwide. The reasons for increased stroke burden in developing countries are inadequately controlled risk factors resulting from poor public awareness and inadequate infrastructure. Computed tomography and MRI are common neuroimaging modalities used to assess stroke with diffusion-weighted MRI, in particular, being the recommended choice for acute stroke imaging. However, access to these imaging modalities is primarily restricted to major cities and high-income groups. In the case of stroke, the time-window of treatment to limit the damage is of a few hours and needs a point-of-care diagnosis. A low-cost MR system typically achieved at the ultra-low- and very-low-field would meet the need for a geographically accessible and portable solution. We review studies focused on accessible stroke imaging and recent developments in MR methodologies, including hardware, to image at low fields. We hypothesize that in the absence of a formal, rapid stroke triaging system, the value of timely on-site delivery of the scanner to the stroke patient can be significant. To this end, we discuss multiple recent hardware and methods developments in the low-field regime. Our review suggests a compelling need to explore further the trade-offs between high signal, contrast, and accessibility at low fields in low-income communities. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 6.

Regional White Matter Atrophy Correlates with Spike Activity in Encephalopathy Related to Status Epilepticus During Slow Sleep (ESES) After Early Thalamic Lesions.

Encephalopathy related to Status Epilepticus during slow Sleep (ESES) is an age-related, epileptic syndrome, which associates cognitive/behavioral disturbances with a peculiar pattern of spike activity. One promising line of research is the study of ESES in cases of early thalamic lesions. We studied 7 ESES patients with unilateral thalamic lesions using magnetic resonance imaging to assess regional white matter (WM) and thalamic nuclei volume differences, and long-term electroencephalogram recordings to localize the epileptogenic cortex. N170 event-related potentials were used to demonstrate the dysfunctional character of the WM abnormalities. Diffusion-weighted images in a subset of 4 patients were used to parcellate the thalamus and evaluate volume asymmetries, based on cortical connectivity. Large WM regional atrophy in the hemisphere with the thalamic lesion was associated with both cortical dysfunction and epileptic activity. A correlation was demonstrated between lesions in the pulvinar and the mediodorsal thalamic nuclei and WM atrophy of the corresponding cortical projection areas. We propose that these abnormalities are due to the widespread structural disconnection produced by the thalamic lesions associated to a yet unknown age-dependent factor. Further exploration of WM regional atrophy association with the spike activity in other etiologies could lend support to the cortical disconnection role in ESES genesis.

Impact of Navigated Task-specific fMRI on Direct Cortical Stimulation.

BACKGROUND AND STUDY AIMS: Cortical mapping (CM) with direct cortical stimulation (DCS) in awake craniotomy is used to preserve cognitive functions such as language. Nevertheless, patient collaboration during this procedure is influenced by previous neurological symptoms and growing discomfort with DCS duration. Our study aimed to evaluate the impact of navigated task-specific functional magnetic resonance imaging (nfMRI) on the practical aspects of DCS. MATERIAL AND METHODS: We recruited glioma patients scheduled for awake craniotomy for prior fMRI-based CM, acquired during motor and language tasks (i.e., verb generation, semantic and syntactic decision tasks). Language data was combined to generate a probabilistic map indicating brain regions activated with more than one paradigm. Presurgical neurophysiological language tests (i.e., verb generation, picture naming, and semantic tasks) were also performed. We considered for subsequent study only the patients with a minimum rate of correct responses of 50% in all tests. These patients were then randomized to perform intraoperative language CM either using the multimodal approach (mCM), using nfMRI and DCS combined, or electrical CM (eCM), with DCS alone. DCS was done while the patient performed picture naming and nonverbal semantic decision tasks. Methodological features such as DCS duration, number of stimuli, total delivered stimulus duration per task, and frequency of seizures were analyzed and compared between groups. The correspondence between positive responses obtained with DCS and nfMRI was also evaluated. RESULTS: Twenty-one surgeries were included, thirteen of which using mCM (i.e., test group). Patients with lower presurgical neuropsychological performance (correct response rate between 50 and 80% in language tests) showed a decreased DCS duration in comparison with the control group. None of the compared methodological features showed differences between groups. Correspondence between DCS and nfMRI was 100/84% in the identification of the precentral gyrus for motor function/opercular frontal inferior gyrus for language function, respectively. CONCLUSION: Navigated fMRI data did not influence DCS in practice. Presurgical language disturbances limited the applicability of DCS mapping in awake surgery.