Deep learning-based automatic left atrial appendage filling defects assessment on cardiac computed tomography for clinical and subclinical atrial fibrillation patients

Rationale and objectives

Selecting region of interest (ROI) for left atrial appendage (LAA) filling defects assessment can be time consuming and prone to subjectivity. This study aimed to develop and validate a novel artificial intelligence (AI), deep learning (DL) based framework for automatic filling defects assessment on CT images for clinical and subclinical atrial fibrillation (AF) patients.

Materials and methods

A total of 443,053 CT images were used for DL model development and testing. Images were analyzed by the AI framework and expert cardiologists/radiologists. The LAA segmentation performance was evaluated using Dice coefficient. The agreement between manual and automatic LAA ROI selections was evaluated using intraclass correlation coefficient (ICC) analysis. Receiver operating characteristic (ROC) curve analysis was used to assess filling defects based on the computed LAA to ascending aorta Hounsfield unit (HU) ratios.

Results

A total of 210 patients (Group 1: subclinical AF, n = 105; Group 2: clinical AF with stroke, n = 35; Group 3: AF for catheter ablation, n = 70) were enrolled. The LAA volume segmentation achieved 0.931-0.945 Dice scores. The LAA ROI selection demonstrated excellent agreement (ICC ≥0.895, p < 0.001) with manual selection on the test sets. The automatic framework achieved an excellent AUC score of 0.979 in filling defects assessment. The ROC-derived optimal HU ratio threshold for filling defects detection was 0.561.

Conclusion

The novel AI-based framework could accurately segment the LAA region and select ROIs while effectively avoiding trabeculae for filling defects assessment, achieving close-to-expert performance. This technique may help preemptively detect the potential thromboembolic risk for AF patients.

Imaging biomarkers to stratify lymph node metastases in abdominal CT - Is radiomics superior to dual-energy material decomposition?

Purpose

To assess the potential of radiomic features in comparison to dual-energy CT (DECT) material decomposition to objectively stratify abdominal lymph node metastases.

Materials and methods

In this retrospective study, we included 81 patients (m, 57; median age, 65 (interquartile range, 58.7-73.3) years) with either lymph node metastases (n = 36) or benign lymph nodes (n = 45) who underwent contrast-enhanced abdominal DECT between 06/2015-07/2019. All malignant lymph nodes were classified as unequivocal according to RECIST criteria and confirmed by histopathology, PET-CT or follow-up imaging. Three investigators segmented lymph nodes to extract DECT and radiomics features. Intra-class correlation analysis was applied to stratify a robust feature subset with further feature reduction by Pearson correlation analysis and LASSO. Independent training and testing datasets were applied on four different machine learning models. We calculated the performance metrics and permutation-based feature importance values to increase interpretability of the models. DeLong test was used to compare the top performing models.

Results

Distance matrices and t-SNE plots revealed clearer clusters using a combination of DECT and radiomic features compared to DECT features only. Feature reduction by LASSO excluded all DECT features of the combined feature cohort. The top performing radiomic features model (AUC = 1.000; F1 = 1.000; precision = 1.000; Random Forest) was significantly superior to the top performing DECT features model (AUC = 0.942; F1 = 0.762; precision = 0.800; Stochastic Gradient Boosting) (DeLong < 0.001).

Conclusion

Imaging biomarkers have the potential to stratify unequivocal lymph node metastases. Radiomics models were superior to DECT material decomposition and may serve as a support tool to facilitate stratification of abdominal lymph node metastases.

Spatial quantification of clinical biomarker pharmacokinetics through deep learning-based segmentation and signal-oriented analysis of MSOT data

Although multispectral optoacoustic tomography (MSOT) significantly evolved over the last several years, there is a lack of quantitative methods for analysing this type of image data. Current analytical methods characterise the MSOT signal in manually defined regions of interest outlining selected tissue areas. These methods demand expert knowledge of the sample anatomy, are time consuming, highly subjective and prone to user bias. Here we present our fully automated open-source MSOT cluster analysis toolkit Mcat that was designed to overcome these shortcomings. It employs a deep learning-based approach for initial image segmentation followed by unsupervised machine learning to identify regions of similar signal kinetics. It provides an objective and automated approach to quantify the pharmacokinetics and extract the biodistribution of biomarkers from MSOT data. We exemplify our generally applicable analysis method by quantifying liver function in a preclinical sepsis model whilst highlighting the advantages of our new approach compared to the severe limitations of existing analysis procedures.

Comparison of choroidal structural changes between children born preterm without retinopathy of prematurity and age-matched children born at full term

AIM

To evaluate the effect of prematurity on choroidal structure in children born preterm with no history of retinopathy of prematurity (ROP) by comparing them with age-matched healthy children born at full term.

METHODS

Enhanced depth imaging optical coherence tomography (EDI-OCT) scans of children aged 5 to 9 years with a history of prematurity but no history of ROP, and age-matched full-term healthy children were evaluated, retrospectively. Choroidal thicknesses (CTs) were measured at subfoveal (SFCT), 1000 µm temporal and nasal from the fovea (T1, N1), and 2000 µm temporal and nasal (T2, N2) from the fovea. The EDI-OCT images were binarized to stromal (SA) and luminal areas (LA) using the ImageJ software. The choroidal vascularity index (CVI) was calculated by dividing LA by the total choroidal area (TCA).

RESULTS

Twenty-nine eyes of 15 preterm children and 41 eyes of 26 full-term children were included. Demographic characteristics including axial length (AL), eye side, age, and the sex of the children in the groups were similar (p>0.05). There was no statistically significant difference in the mean CVI, SFCT, N1, and T1 values between the groups (p>0.05); however, the mean T2 and N2 values were significantly higher in the full-term group than in the preterm group (p<0.05). There was a significant positive correlation between the birth week and the T1 (p<0.05) CONCLUSION: : Prematurity can affect CT even with no history of ROP. The decreases in CTs were significant at 2000 µm nasal and temporal from the fovea. The impairment of temporal choroidal region was more evident than nasal choroidal region. The mean CVI values were similar between the groups.

Semi-automated analysis of supraclavicular thermal images increases speed of brown adipose tissue analysis without increasing variation in results

Interest in brown adipose tissue remains high a decade after it was determined to be present outside of the neonatal period. In vivo imaging, however, has remained a challenge due to the lack of a imaging modality suitable for large healthy-volunteer studies, post-prandial investigations and vulnerable groups, such as children. Infrared thermography is increasingly accepted as a valid, non-invasive and flexible alternative but there is a wide approach to analysis between different groups. Defining the region of interest with anatomical borders rather than using a simple polygon may have advantages in terms of consistency but makes image analysis slower, limiting some applications. Our novel semi-automated method, using a custom-built graphical user interface, allows an 86% improvement in speed of image analysis (54.9 (38.3–71.4) seconds/image) without increases in variation between analysers or with repeated analysis. The improved efficiency demonstrated makes feasible larger studies, longer imaging periods or increased image acquisition frequency, providing an opportunity to study novel features of brown adipose tissue function.

•

Brown adipose tissue is a key heat-generating tissue but is difficult to measure.

•

Thermal imaging can measure brown adipose tissue response without radiation.

•

A semi-automated approach increases image analysis efficiency.

•

Thermal video analysis and imaging over longer periods is now feasible.

Pleomorphic carcinoma of the lung: Prognostic models of semantic, radiomics and combined features from CT and PET/CT in 85 patients

Introduction

To demonstrate semantic, radiomics, and the combined risk models related to the prognoses of pulmonary pleomorphic carcinomas (PCs).

Methods

We included 85 patients (M:F = 71:14; age, 35–88 [mean, 63 years]) whose imaging features were divided into training (n = 60) and test (n = 25) sets. Nineteen semantic and 142 radiomics features related to tumors were computed. Semantic risk score (SRS) model was built using the Cox-least absolute shrinkage and selection operator (LASSO) approach. Radiomics risk score (RRS) from CT and PET features and combined risk score (CRS) adopting both semantic and radiomics features were also constructed. Risk groups were stratified by the median of the risk scores of the training set. Survival analysis was conducted with the Kaplan-Meier plots.

Results

Of 85 PCs, adenocarcinoma was the most common epithelial component found in 63 (73 %) tumors. In SRS model, four features were stratified into high- and low-risk groups (HR, 4.119; concordance index ([C-index], 0.664) in the test set. In RRS model, five features helped improve the stratification (HR, 3.716; C-index, 0.591) and in CRS model, three features helped perform the best stratification (HR, 4.795; C-index, 0.617). The two significant features of CRS models were the SUVmax and the histogram feature of energy ([CT Firstorder Energy]).

Conclusion

In PCs of the lungs, the combined model leveraging semantic and radiomics features provides a better prognosis compared to using semantic and radiomics features separately. The high SUVmax of solid portion (CT Firstorder Energy) of tumors is associated with poor prognosis in lung PCs.

Ultrasound delta-radiomics during radiotherapy to predict recurrence in patients with head and neck squamous cell carcinoma

PURPOSE

This study investigated the use of quantitative ultrasound (QUS) obtained during radical radiotherapy (RT) as a radiomics biomarker for predicting recurrence in patients with node-positive head-neck squamous cell carcinoma (HNSCC).

METHODS

Fifty-one patients with HNSCC were treated with RT (70 Gy/33 fractions) (±concurrent chemotherapy) were included. QUS Data acquisition involved scanning an index neck node with a clinical ultrasound device. Radiofrequency data were collected before starting RT, and after weeks 1, and 4. From this data, 31 spectral and related-texture features were determined for each time and delta (difference) features were computed. Patients were categorized into two groups based on clinical outcomes (recurrence or non-recurrence). Three machine learning classifiers were used for the development of a radiomics model. Features were selected using a forward sequential selection method and validated using leave-one-out cross-validation.

RESULTS

The median follow up for the entire group was 38 months (range 7-64 months). The disease sites involved neck masses in patients with oropharynx (39), larynx (5), carcinoma unknown primary (5), and hypopharynx carcinoma (2). Concurrent chemotherapy and cetuximab were used in 41 and 1 patient(s), respectively. Recurrence was seen in 17 patients. At week 1 of RT, the support vector machine classifier resulted in the best performance, with accuracy and area under the curve (AUC) of 80% and 0.75, respectively. The accuracy and AUC improved to 82% and 0.81, respectively, at week 4 of treatment.

CONCLUSION

QUS Delta-radiomics can predict higher risk of recurrence with reasonable accuracy in HNSCC.Clinical trial registration: clinicaltrials.gov.in identifier NCT03908684.

Differentiating benign from malignant pancreatic cysts on computed tomography

•

CT can distinguish between benign and premalignant or malignant pancreatic cysts.

•

Solid component and septation were the only CT features that could differentiate benign from malignant cysts.

•

Cyst wall enhancements on CT were more commonly observed in premalignant or malignant cysts than in benign cysts.

•

CT is a necessary diagnostic modality to preoperatively detect and characterize pancreatic lesions.

Purpose

It is important to identify features on computed tomography (CT) that can distinguish between benign and premalignant or malignant pancreatic cysts to avoid unnecessary surgeries. This study investigated the preoperative diagnostic evaluation of cystic pancreatic lesions to determine how advanced imaging and clinical factors should guide management.

Methods

In total, 53 patients with 27 benign and 26 premalignant or malignant cysts were enrolled. CT features of the cysts were compared using univariate and multivariate analyses.

Results

On univariate analysis, a solid component (p < 0.01), septation (p < 0.01), location (p < 0.01), border (p < 0.01), wall enhancement (p = 0.01), lesion margins (p < 0.01), pancreatic atrophy (p = 0.04), and a cystic wall (p < 0.01) were all significantly different between benign and premalignant or malignant cysts. On multivariate analysis, only a solid component (p < 0.01) and septation (p < 0.01) were significant.

Conclusion

A thin cystic wall, uniform homogeneity, a clear border, the presence of septation, pancreatic atrophy, and the absence of both wall enhancements and solid components were more frequently seen in benign cysts. A thick wall, lack of homogeneity, the presence of wall enhancements and solid components, absence of septation, only a small degree of pancreatic atrophy, and unclear borders were more frequent among premalignant or malignant cysts. The only CT features to differentiate benign from premalignant or malignant cysts were a solid component and septation.

The Effect of Vascular Segmentation Methods on Stereotactic Trajectory Planning for Drug-Resistant Focal Epilepsy: A Retrospective Cohort Study

Background

Stereotactic neurosurgical procedures carry a risk of intracranial hemorrhage, which may result in significant morbidity and mortality. Vascular imaging is crucial for planning stereotactic procedures to prevent conflicts with intracranial vasculature. There is a wide range of vascular imaging methods used for stereoelectroencephalography (SEEG) trajectory planning. Computer-assisted planning (CAP) improves planning time and trajectory metrics. We aimed to quantify the effect of different vascular imaging protocols on CAP trajectories for SEEG.

Methods

Ten patients who had undergone SEEG (95 electrodes) following preoperative acquisition of gadolinium-enhanced magnetic resonance imaging (MR + Gad), magnetic resonance angiography and magnetic resonance angiography (MRV + MRA), and digital subtraction catheter angiography (DSA) were identified from a prospectively maintained database. SEEG implantations were planned using CAP using DSA segmentations as the gold standard. Strategies were then recreated using MRV + MRA and MR + Gad to define the “apparent” and “true” risk scores associated with each modality. Vessels of varying diameter were then iteratively removed from the DSA segmentation to identify the size at which all 3 vascular modalities returned the same safety metrics.

Results

CAP performed using DSA vessel segmentations resulted in significantly lower “true” risk scores and greater minimum distances from vasculature compared with the “true” risk associated with MR + Gad and MRV + MRA. MRV + MRA and MR + Gad returned similar risk scores to DSA when vessels <2 mm and <4 mm were not considered, respectively.

Conclusions

Significant variability in vascular imaging and trajectory planning practices exist for SEEG. CAP performed with MR + Gad or MRV + MRA alone returns “falsely” lower risk scores compared with DSA. It is unclear whether DSA is oversensitive and thus restricting potential trajectories.

The bone metastasis niche in breast cancer-potential overlap with the haematopoietic stem cell niche in vivo

BACKGROUND

Bone metastasis is one of the most common complications of advanced breast cancer. During dissemination to bone, breast cancer cells locate in a putative 'metastatic niche', a microenvironment that regulates the colonisation, maintenance of tumour cell dormancy and subsequent tumour growth. The precise location and composition of the bone metastatic niche is not clearly defined. We have used in vivo models of early breast cancer dissemination to provide novel evidence that demonstrates overlap between endosteal, perivascular, HSC and the metastatic niche in bone.

METHODS

Estrogen Receptor (ER) +ve and -ve breast cancer cells were labelled with membrane dyes Vybrant-DiD and Vybrant-CM-DiI and injected via different routes in BALBc/nude mice of different ages. Two-photon microscopy was used to detect and quantitate tumour cells and map their location within the bone microenvironment as well as their distance to the nearest bone surface compared to the nearest other tumour cell. To investigate whether the metastatic niche overlapped with the HSC niche, animals were pre-treated with the CXCR4 antagonist AMD3100 to mobilise hematopoietic (HSCs) prior to injection of breast cancer cells.

RESULTS

Breast cancer cells displayed a characteristic pattern of homing in the long bones, with the majority of tumour cells seeded in the trabecular regions, regardless of the route of injection, cell-line characteristics (ER status) or animal age. Breast cancer cells located in close proximity to the nearest bone surface and the average distance between individual tumour cells was higher than their distance to bone. Mobilisation of HSCs from the niche to the circulation prior to injection of cell lines resulted in increased numbers of tumour cells disseminated in trabecular regions.

CONCLUSION

Our data provide evidence that homing of breast cancer cells is independent of their ER status and that the breast cancer bone metastasis niche is located within the trabecular region of bone, an area rich in osteoblasts and microvessels. The increased number of breast cancer cells homing to bone after mobilisation of HSCs suggests that the HSC and the bone metastasis niche overlap.

Decreased Value of Highly Accurate Fractional Anisotropy Using 3-Tesla ZOOM Diffusion Tensor Imaging After Decompressive Surgery in Patients with Cervical Spondylotic Myelopathy: Aligned Fibers Effect

Background

Diffusion tensor imaging (DTI) is widely used; however, most of the prior studies have resulted in presurgical decreased fractional anisotropy (FA) values in patients with cervical spondylotic myelopathy (CSM). We used ZOOM DTI and could acquire highly accurate FA values during perioperative periods, which indicated different insights than preceding studies. The objective of this study was to assess the perioperative FA change in patients with CSM and determine the prognostic factor.

Methods

Twenty-eight patients with CSM and healthy control subjects were enrolled in this study. Twenty patients (71%) had intracordal high intensity before surgery. All patients underwent decompressive surgery. ZOOM DTI and the Japanese Orthopaedic Association (JOA) assessment were performed before and after surgery. The region of interest was manually contoured to omit the surrounding cerebrospinal fluid. The axial plane of the most stenotic cervical level was assessed.

Results

FA values before surgery and at 1 week after surgery, and FA values at 1 week after surgery and at 6 months after surgery differed significantly as determined. The FA values of patients with intracordal high intensity significantly decreased after surgery and significantly increased from 1 week to 6 months, whereas those of patients without intracordal high intensity did not significantly change. JOA scores at 6 months after surgery (13.1) improved significantly compared with JOA scores before surgery (10.8). Only FA values at 1 week after surgery had a significant positive relationship with JOA scores presurgery and at 6 months after surgery.

Conclusions

The presurgical FA value in patients with CSM did not differ from that of normal control subjects, but significantly decreased after surgery, and significantly increased 6 months after surgery. We concluded that the postsurgical FA value approximates the proper state of the damaged cord and the presurgical FA value includes a masked effect as an aligned fiber effect because of compression by degenerative construction. Only the FA value at 1 week had a significant positive relationship with the JOA score presugery and at 6 months, which established that the postsurgical FA value may be a more accurate prognostic factor than the presurgical FA value.

Beyond binary parcellation of the vestibular cortex - A dataset

The data-set presented in this data article is supplementary to the original publication, doi:10.1016/j.neuroimage.2018.05.018 (Kirsch et al., 2018). Named article describes handedness-dependent organizational patterns of functional subunits within the human vestibular cortical network that were revealed by functional magnetic resonance imaging (fMRI) connectivity parcellation. 60 healthy volunteers (30 left-handed and 30 right-handed) were examined on a 3T MR scanner using resting state fMRI. The multisensory (non-binary) nature of the human (vestibular) cortex was addressed by using masked binary and non-binary variations of independent component analysis (ICA). The data have been made publicly available via github (https://github.com/RainerBoegle/BeyondBinaryParcellationData).

Anatomic & metabolic brain markers of the m.3243A>G mutation: A multi-parametric 7T MRI study

One of the most common mitochondrial DNA (mtDNA) mutations, the A to G transition at base pair 3243, has been linked to changes in the brain, in addition to commonly observed hearing problems, diabetes and myopathy. However, a detailed quantitative description of m.3243A>G patients' brains has not been provided so far. In this study, ultra-high field MRI at 7T and volume- and surface-based data analyses approaches were used to highlight morphology (i.e. atrophy)-, microstructure (i.e. myelin and iron concentration)- and metabolism (i.e. cerebral blood flow)-related differences between patients (N = 22) and healthy controls (N = 15). The use of quantitative MRI at 7T allowed us to detect subtle changes of biophysical processes in the brain with high accuracy and sensitivity, in addition to typically assessed lesions and atrophy. Furthermore, the effect of m.3243A>G mutation load in blood and urine epithelial cells on these MRI measures was assessed within the patient population and revealed that blood levels were most indicative of the brain's state and disease severity, based on MRI as well as on neuropsychological data. Morphometry MRI data showed a wide-spread reduction of cortical, subcortical and cerebellar gray matter volume, in addition to significantly enlarged ventricles. Moreover, surface-based analyses revealed brain area-specific changes in cortical thickness (e.g. of the auditory cortex), and in T₁, T₂* and cerebral blood flow as a function of mutation load, which can be linked to typically m.3243A>G-related clinical symptoms (e.g. hearing impairment). In addition, several regions linked to attentional control (e.g. middle frontal gyrus), the sensorimotor network (e.g. banks of central sulcus) and the default mode network (e.g. precuneus) were characterized by alterations in cortical thickness, T₁, T₂* and/or cerebral blood flow, which has not been described in previous MRI studies. Finally, several hypotheses, based either on vascular, metabolic or astroglial implications of the m.3243A>G mutation, are discussed that potentially explain the underlying pathobiology. To conclude, this is the first 7T and also the largest MRI study on this patient population that provides macroscopic brain correlates of the m.3243A>G mutation indicating potential MRI biomarkers of mitochondrial diseases and might guide future (longitudinal) studies to extensively track neuropathological and clinical changes.

Temperature-dependent optoacoustic response and transient through zero Grüneisen parameter in optically contrasted media

Non-invasive optoacoustic mapping of temperature in tissues with low blood content can be enabled by administering external contrast agents. Some important clinical applications of such approach include temperature mapping during thermal therapies in a prostate or a mammary gland. However, the technique would require a calibration that establishes functional relationship between the measured normalized optoacoustic response and local tissue temperature. In this work, we investigate how a key calibration parameter - the temperature of zero optoacoustic response (T₀ ) - behaves in different environments simulating biological tissues augmented with either dissolved or particulate (nanoparticles) contrast agents. The observed behavior of T₀ in ionic and molecular solutions suggests that in-vivo temperature mapping is feasible for contrast agents of this type, but requires knowledge of local concentrations. Oppositely, particulate contrast agents (plasmonic or carbon nanoparticles) demonstrated concentration-independent thermal behavior of optoacoustic response with T₀ defined by the thermoelastic properties of the local environment.

Differential involvement of corticospinal tract (CST) fibers in UMN-predominant ALS patients with or without CST hyperintensity: A diffusion tensor tractography study

Diagnosis of amyotrophic lateral sclerosis (ALS) depends on clinical evidence of combined upper motor neuron (UMN) and lower motor neuron (LMN) degeneration, although ALS patients can present with features predominantly of one or the other. Some UMN-predominant patients show hyperintense signal along the intracranial corticospinal tract (CST) on T2- and proton density (PD)-weighted images (ALS-CST +), and appear to have faster disease progression when compared to those without CST hyperintensity (ALS-CST -). The reason for this is unknown. We hypothesized that diffusion tensor tractography (DTT) would reveal differences in DTI abnormalities along the intracranial CST between these two patient subgroups. Clinical DTI scans were obtained at 1.5T in 14 neurologic controls and 45 ALS patients categorized into two UMN phenotypes based on clinical measures and MRI. DTT was used to quantitatively assess the CST in control and ALS groups. DTT revealed subcortical loss ('truncation') of virtual motor CST fibers (presumably) projecting from the precentral gyrus (PrG) in ALS patients but not in controls; in contrast, virtual fibers (presumably) projecting to the adjacent postcentral gyrus (PoG) were spared. No significant differences in virtual CST fiber length were observed between controls and ALS patients. However, the frequency of CST truncation was significantly higher in the ALS-CST + subgroup (9 of 21) than in the ALS-CST - subgroup (4 of 24; p = 0.049), suggesting this finding could differentiate these ALS subgroups. Also, because virtual CST truncation occurred only in the ALS patient group and not in the control group (p = 0.018), this DTT finding could prove to be a diagnostic biomarker of ALS. Significantly shorter disease duration and faster disease progression rate were observed in ALS patients with CST fiber truncation than in those without (p < 0.05). DTI metrics of fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD) were also determined in four regions of interest (ROIs) along the CST, namely: cerebral peduncle (CP), posterior limb of internal capsule (PLIC), centrum semiovale at top of lateral ventricle (CSoLV) and subcortical to primary motor cortex (subPMC). Of note, FA values along the left hemisphere virtual CST tract were significantly different between controls and ALS-CST + patients (p < 0.05) only at the PLIC level, but not at the CSoLV or subPMC level. Also, no significant differences in FA values were observed between ALS subgroups or between control and ALS-CST - groups (p > 0.05) in any of the ROIs. In addition, comparing FA values between ALS patients with CST truncation and those without in the aforementioned four ROIs, revealed no significant differences in either hemisphere. However, visual evaluation of DTT was able to identify UMN degeneration in patients with ALS, particularly in those with a more aggressive clinical disease course and possibly different pathologic processes.

The diminishing dominance of the dominant hemisphere: Language fMRI in focal epilepsy

“Which is the dominant hemisphere?” is a question that arises frequently in patients considered for neurosurgery. The concept of the dominant hemisphere implies uniformity of language lateralisation throughout the brain. It is increasingly recognised that this is not the case in the healthy control brain, and it is especially not so in neurological diseases such as epilepsy.

In the present work we adapt our published objective lateralisation method (based on the construction of laterality curves) for use with sub-lobar cortical, subcortical and cerebellar regions of interest (ROIs). We apply this method to investigate regional lateralisation of language activation in 12 healthy controls and 18 focal epilepsy patients, using three different block design language fMRI paradigms, each tapping different aspects of language processing. We compared lateralisation within each ROI across tasks, and investigated how the quantity of data collected affected the ability to robustly estimate laterality across ROIs.

In controls, lateralisation was stronger, and the variance across individuals smaller, in cortical ROIs, particularly in the Inferior Frontal (Broca) region. Lateralisation within temporal ROIs was dependent on the nature of the language task employed. One of the healthy controls was left lateralised anteriorly and right lateralised posteriorly. Consistent with previous work, departures from normality occurred in ~ 15–50% of focal epilepsy patients across the different ROIs, with atypicality most common in the Lateral Temporal (Wernicke) region. Across tasks and ROIs the absolute magnitude of the laterality estimate increased and its across participant variance decreased as more cycles of task and rest were included, stabilising at ~ 4 cycles (~ 4 min of data collection).

Our data highlight the importance of considering language as a complex task where lateralisation varies at the subhemispheric scale. This is especially important for presurgical planning for focal resections where the concept of ‘hemispheric dominance’ may be misleading. This is a precision medicine approach that enables objective evaluation of language dominance within specific brain regions and can reveal surprising and unexpected anomalies that may be clinically important for individual cases.

•

Different brain regions support different aspects of language function.

•

The degree of language lateralisation varies in different brain regions.

•

Atypical lateralisation is common in focal epilepsy patients, particularly in the temporal lobe.

•

Even in normal controls, frontal and temporal language systems can be in opposite hemispheres.

•

Language dominance is more complex than often thought.

Multimodal optoacoustic and multiphoton microscopy of human carotid atheroma

Carotid artery atherosclerosis is a main cause of stroke. Understanding atherosclerosis biology is critical in the development of targeted prevention and treatment strategies. Consequently, there is demand for advanced tools investigating atheroma pathology. We consider hybrid optoacoustic and multiphoton microscopy for the integrated and complementary interrogation of plaque tissue constituents and their mutual interactions. Herein, we visualize human carotid plaque using a hybrid multimodal imaging system that combines optical resolution optoacoustic (photoacoustic) microscopy, second and third harmonic generation microscopy, and two-photon excitation fluorescence microscopy. Our data suggest more comprehensive insights in the pathophysiology of atheroma formation and destabilization, by enabling congruent visualization of structural and biological features critical for the atherosclerotic process and its acute complications, such as red blood cells and collagen.