Timing the ischaemic stroke by 1H-MRI: improved accuracy using absolute relaxation times over signal intensities

The Value of Applying Machine Learning in Predicting the Time of Symptom Onset in Stroke Patients: Systematic Review and Meta-Analysis

BACKGROUND

Machine learning is a potentially effective method for identifying and predicting the time of the onset of stroke. However, the value of applying machine learning in this field remains controversial and debatable.

OBJECTIVE

We aimed to assess the value of applying machine learning in predicting the time of stroke onset.

METHODS

PubMed, Web of Science, Embase, and Cochrane were comprehensively searched. The C index and sensitivity with 95% CI were used as effect sizes. The risk of bias was evaluated using PROBAST (Prediction Model Risk of Bias Assessment Tool), and meta-analysis was conducted using R (version 4.2.0; R Core Team).

RESULTS

Thirteen eligible studies were included in the meta-analysis involving 55 machine learning models with 41 models in the training set and 14 in the validation set. The overall C index was 0.800 (95% CI 0.773-0.826) in the training set and 0.781 (95% CI 0.709-0.852) in the validation set. The sensitivity and specificity were 0.76 (95% CI 0.73-0.80) and 0.79 (95% CI 0.74-0.82) in the training set and 0.81 (95% CI 0.68-0.90) and 0.83 (95% CI 0.73-0.89) in the validation set, respectively. Subgroup analysis revealed that the accuracy of machine learning in predicting the time of stroke onset within 4.5 hours was optimal (training: 0.80, 95% CI 0.77-0.83; validation: 0.79, 95% CI 0.71-0.86).

CONCLUSIONS

Machine learning has ideal performance in identifying the time of stroke onset. More reasonable image segmentation and texture extraction methods in radiomics should be used to promote the value of applying machine learning in diverse ethnic backgrounds.

TRIAL REGISTRATION

PROSPERO CRD42022358898; https://www.crd.york.ac.uk/Prospero/display_record.php?RecordID=358898.

MRI investigation of vascular remodeling for heterogeneous edema lesions in subacute ischemic stroke rat models: Correspondence between cerebral vessel structure and function

The spatial heterogeneity in the temporal occurrence of pseudo-normalization of MR apparent diffusion coefficient values for ischemic lesions may be related to morphological and functional vascular remodeling. As the area of accelerated pseudo-normalization tends to expand faster and more extensively into the chronic stage, detailed vascular characterization of such areas is necessary. During the subacute stage of transient middle cerebral artery occlusion rat models, the morphological size of the macrovasculature, microvascular vessel size index (VSI), and microvessel density (MVD) were quantified along with functional perfusion measurements of the relative cerebral blood flow (rCBF) and mean transit time (rMTT) of the corresponding areas (33 cases for each parameter). When compared with typical pseudo-normalization lesions, early pseudo-normalization lesions exhibited larger VSI and rCBF (p < 0.001) at reperfusion days 4 and 7, along with reduced MVD and elongated rMTT (p < 0.001) at reperfusion days 1, 4, and 7. The group median VSI and rCBF exhibited a strong positive correlation (r = 0.92), and the corresponding MVD and rMTT showed a negative correlation (r = -0.48). Light sheet fluorescence microscopy images were used to quantitatively validate the corresponding MRI-derived microvascular size, density, and cerebral blood volume.

A Comparison of T2 Relaxation-Based MRI Stroke Timing Methods in Hyperacute Ischemic Stroke Patients: A Pilot Study

Background:

T₂ relaxation-based magnetic resonance imaging (MRI) signals may provide onset time for acute ischemic strokes with an unknown onset. The ability of visual and quantitative MRI-based methods in a cohort of hyperacute ischemic stroke patients was studied.

Methods:

A total of 35 patients underwent 3T (3 Tesla) MRI (<9-hour symptom onset). Diffusion-weighted (DWI), apparent diffusion coefficient (ADC), T₁-weighted (T₁w), T₂-weighted (T₂w), and T₂ relaxation time (T₂) images were acquired. T₂-weighted fluid attenuation inversion recovery (FLAIR) images were acquired for 17 of these patients. Image intensity ratios of the average intensities in ischemic and non-ischemic reference regions were calculated for ADC, DWI, T₂w, T₂ relaxation, and FLAIR images, and optimal image intensity ratio cut-offs were determined. DWI and FLAIR images were assessed visually for DWI/FLAIR mismatch.

Results:

The T₂ relaxation time image intensity ratio was the only parameter with significant correlation with stroke duration (r = 0.49, P = .003), an area under the receiver operating characteristic curve (AUC = 0.77, P < .0001), and an optimal cut-off (T₂ ratio = 1.072) that accurately identified patients within the 4.5-hour thrombolysis treatment window with sensitivity of 0.74 and specificity of 0.74. In the patients with the additional FLAIR, areas under the precision-recall-gain curve (AUPRG) and F₁ scores showed that the T₂ relaxation time ratio (AUPRG = 0.60, F₁ = 0.73) performed considerably better than the FLAIR ratio (AUPRG = 0.39, F₁ = 0.57) and the visual DWI/FLAIR mismatch (F₁ = 0.25).

Conclusions:

Quantitative T₂ relaxation time is the preferred MRI parameter in the assessment of patients with unknown onset for treatment stratification.

Reliability of Synthetic Brain MRI for Assessment of Ischemic Stroke with Phantom Validation of a Relaxation Time Determination Method

The reliability of relaxation time measures in synthetic magnetic resonance images (MRIs) of homemade phantoms were validated, and the diagnostic suitability of synthetic imaging was compared to that of conventional MRIs for detecting ischemic lesions. Phantoms filled with aqueous cupric-sulfate (CuSO₄) were designed to mimic spin-lattice (T₁) and spin-spin (T₂) relaxation properties and were used to compare their accuracies and stabilities between synthetic and conventional scans of various brain tissues. To validate the accuracy of synthetic imaging in ischemic stroke diagnoses, the synthetic and clinical scans of 18 patients with ischemic stroke were compared, and the quantitative contrast-to-noise ratios (CNRs) were measured, using the Friedman test to determine significance in differences. Results using the phantoms showed no significant differences in the interday and intersession synthetic quantitative T₁ and T₂ values. However, between synthetic and referenced T₁ and T₂ values, differences were larger for longer relaxation times, showing that image intensities in synthetic scans are relatively inaccurate in the cerebrospinal fluid (CSF). Similarly, CNRs in CSF regions of stroke patients were significantly different on synthetic T₂-weighted and T₂-fluid-attenuated inversion recovery images. In contrast, differences in stroke lesions were insignificant between the two. Therefore, interday and intersession synthetic T₁ and T₂ values are highly reliable, and discrepancies in synthetic T₁ and T₂ relaxation times and image contrasts in CSF regions do not affect stroke lesion diagnoses. Additionally, quantitative relaxation times from synthetic images allow better estimations of ischemic stroke onset time, consequently increasing confidence in synthetic MRIs as diagnostic tools for ischemic stroke.

Three-Dimensional GRE T1ρ mapping of the brain using tailored variable flip-angle scheduling

PURPOSE

To introduce a new approach called tailored variable flip-angle (VFA) scheduling for SNR-efficient 3D T_1ρ mapping of the brain using a magnetization-prepared gradient-echo sequence.

METHODS

Simulations were used to assess the relative SNR efficiency, quantitative accuracy, and spatial blurring of tailored VFA scheduling for T_1ρ mapping of brain tissue compared with magnetization-prepared angle-modulated partitioned k-space spoiled gradient-echo snapshots (MAPSS), a state-of-the-art technique for accurate 3D gradient-echo T_1ρ mapping. Simulations were also used to calculate optimal imaging parameters for tailored VFA scheduling versus MAPSS, without and with nulling of CSF. Four participants were imaged at 3T MRI to demonstrate the feasibility of tailored VFA scheduling for T_1ρ mapping of the brain. Using MAPSS as a reference standard, in vivo data were used to validate the relative SNR efficiency and quantitative accuracy of the new approach.

RESULTS

Tailored VFA scheduling can provide a 2-fold to 4-fold gain in the SNR of the resulting T_1ρ map as compared with MAPSS when using identical sequence parameters while limiting T_1ρ quantification errors to 2% or less. In vivo whole-brain 3D T_1ρ maps acquired with tailored VFA scheduling had superior SNR efficiency than is achievable with MAPSS, and the SNR efficiency improved with a greater number of views per segment.

CONCLUSIONS

Tailored VFA scheduling is an SNR-efficient GRE technique for 3D T_1ρ mapping of the brain that provides increased flexibility in choice of imaging parameters compared with MAPSS, which may benefit a variety of applications.

Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia

MRI provides a sensitive and specific imaging tool to detect acute ischemic stroke by means of a reduced diffusion coefficient of brain water. In a rat model of ischemic stroke, differences in quantitative T₁ and T₂ MRI relaxation times (qT₁ and qT₂) between the ischemic lesion (delineated by low diffusion) and the contralateral non-ischemic hemisphere increase with time from stroke onset. The time dependency of MRI relaxation time differences is heuristically described by a linear function and thus provides a simple estimate of stroke onset time. Additionally, the volumes of abnormal qT₁ and qT₂ within the ischemic lesion increase linearly with time providing a complementary method for stroke timing. A (semi)automated computer routine based on the quantified diffusion coefficient is presented to delineate acute ischemic stroke tissue in rat ischemia. This routine also determines hemispheric differences in qT₁ and qT₂ relaxation times and the location and volume of abnormal qT₁ and qT₂ voxels within the lesion. Uncertainties associated with onset time estimates of qT₁ and qT₂ MRI data vary from ± 25 min to ± 47 min for the first 5 hours of stroke. The most accurate onset time estimates can be obtained by quantifying the volume of overlapping abnormal qT₁ and qT₂ lesion volumes, termed 'V^overlap' (± 25 min) or by quantifying hemispheric differences in qT₂ relaxation times only (± 28 min). Overall, qT₂ derived parameters outperform those from qT₁. The current MRI protocol is tested in the hyperacute phase of a permanent focal ischemia model, which may not be applicable to transient focal brain ischemia.

Stroke Onset Time Determination Using MRI Relaxation Times without Non-Ischaemic Reference in A Rat Stroke Model

BACKGROUND

Objective timing of stroke in emergency departments is expected to improve patient stratification. Magnetic resonance imaging (MRI) relaxations times, T₂ and T_1ρ , in abnormal diffusion delineated ischaemic tissue were used as proxies of stroke time in a rat model.

METHODS

Both 'non-ischaemic reference'-dependent and -independent estimators were generated. Apparent diffusion coefficient (ADC), T₂ and T_1ρ , were sequentially quantified for up to 6 hours of stroke in rats (n = 8) at 4.7T. The ischaemic lesion was identified as a contiguous collection of voxels with low ADC. T₂ and T_1ρ in the ischaemic lesion and in the contralateral non-ischaemic brain tissue were determined. Differences in mean MRI relaxation times between ischaemic and non-ischaemic volumes were used to create reference-dependent estimator. For the reference-independent procedure, only the parameters associated with log-logistic fits to the T₂ and T_1ρ distributions within the ADC-delineated lesions were used for the onset time estimation.

RESULT

The reference-independent estimators from T₂ and T_1ρ data provided stroke onset time with precisions of ±32 and ±27 minutes, respectively. The reference-dependent estimators yielded respective precisions of ±47 and ±54 minutes.

CONCLUSIONS

A 'non-ischaemic anatomical reference'-independent estimator for stroke onset time from relaxometric MRI data is shown to yield greater timing precision than previously obtained through reference-dependent procedures.

Determining Stroke Onset Time Using Quantitative MRI: High Accuracy, Sensitivity and Specificity Obtained from Magnetic Resonance Relaxation Times

Many ischaemic stroke patients are ineligible for thrombolytic therapy due to unknown onset time. Quantitative MRI (qMRI) is a potential surrogate for stroke timing. Rats were subjected to permanent middle cerebral artery occlusion and qMRI parameters including hemispheric differences in apparent diffusion coefficient, T₂-weighted signal intensities, T₁ and T₂ relaxation times (qT₁, qT₂) and f₁, f₂ and V_overlap were measured at hourly intervals at 4.7 or 9.4 T. Accuracy and sensitivity for identifying strokes scanned within and beyond 3 h of onset was determined. Accuracy for V_overlap, f₂ and qT₂ (>90%) was significantly higher than other parameters. At a specificity of 1, sensitivity was highest for V_overlap (0.90) and f₂ (0.80), indicating promise of these qMRI indices in the clinical assessment of stroke onset time.

Stroke onset time estimation from multispectral quantitative magnetic resonance imaging in a rat model of focal permanent cerebral ischemia

Background

Quantitative T2 relaxation magnetic resonance imaging allows estimation of stroke onset time.

Aims

We aimed to examine the accuracy of quantitative T1 and quantitative T2 relaxation times alone and in combination to provide estimates of stroke onset time in a rat model of permanent focal cerebral ischemia and map the spatial distribution of elevated quantitative T1 and quantitative T2 to assess tissue status.

Methods

Permanent middle cerebral artery occlusion was induced in Wistar rats. Animals were scanned at 9.4T for quantitative T1, quantitative T2, and Trace of Diffusion Tensor (Dav) up to 4 h post-middle cerebral artery occlusion. Time courses of differentials of quantitative T1 and quantitative T2 in ischemic and non-ischemic contralateral brain tissue (ΔT1, ΔT2) and volumes of tissue with elevated T1 and T2 relaxation times ( f1, f2) were determined. TTC staining was used to highlight permanent ischemic damage.

Results

ΔT1, ΔT2, f1, f2, and the volume of tissue with both elevated quantitative T1 and quantitative T2 (VOverlap) increased with time post-middle cerebral artery occlusion allowing stroke onset time to be estimated. VOverlap provided the most accurate estimate with an uncertainty of ±25 min. At all times-points regions with elevated relaxation times were smaller than areas with Dav defined ischemia.

Conclusions

Stroke onset time can be determined by quantitative T1 and quantitative T2 relaxation times and tissue volumes. Combining quantitative T1 and quantitative T2 provides the most accurate estimate and potentially identifies irreversibly damaged brain tissue.

A spatiotemporal theory for MRI T2 relaxation time and apparent diffusion coefficient in the brain during acute ischaemia: Application and validation in a rat acute stroke model

The objective of this study is to present a mathematical model which can describe the spatiotemporal progression of cerebral ischaemia and predict magnetic resonance observables including the apparent diffusion coefficient (ADC) of water and transverse relaxation time T2 This is motivated by the sensitivity of the ADC to the location of cerebral ischaemia and T2 to its time-course, and that it has thus far proven challenging to relate observations of changes in these MR parameters to stroke timing, which is of considerable importance in making treatment choices in clinics. Our mathematical model, called the cytotoxic oedema/dissociation (CED) model, is based on the transit of water from the extra- to the intra-cellular environment (cytotoxic oedema) and concomitant degradation of supramacromolecular and macromolecular structures (such as microtubules and the cytoskeleton). It explains experimental observations of ADC and T2, as well as identifying the rate of spread of effects of ischaemia through a tissue as a dominant system parameter. The model brings the direct extraction of the timing of ischaemic stroke from quantitative MRI closer to reality, as well as providing insight on ischaemia pathology by imaging in general. We anticipate that this may improve patient access to thrombolytic treatment as a future application.