Voxel Analysis Research Articles

Impact of Apolipoprotein E4 on the Locus Coeruleus Functional Connectivity in Preclinical Alzheimer's Disease.

Recent interest has surged in the locus coeruleus (LC) for its early involvement in Alzheimer's disease (AD), notably concerning the apolipoprotein ɛ4 allele (APOE4). This study aimed to discern LC functional connectivity (FC) variations in preclinical AD subjects, dissecting the roles of APOE4 carrier status and amyloid-β (Aβ) deposition. A cohort of 112 cognitively intact individuals, all Aβ-positive, split into 70 APOE4 noncarriers and 42 carriers, underwent functional MRI scans, neuropsychological assessments, and APOE genotyping. The research utilized seed to voxel analysis for illustrating LC rsFC discrepancies between APOE4 statuses and employed a general linear model to examine the interactive influence of APOE4 carrier status and Aβ deposition on LC FC values. The investigation revealed no significant differences in sex, age, or SUVR between APOE4 carriers and noncarriers. It found diminished LC FC with the occipital cortex in APOE4 carriers and identified a significant interaction between APOE4 carrier status and temporal lobe SUVR in LC FC with the occipital cortex. This interaction suggested a proportional increase in LC FC for APOE4 carriers. Additional notable interactions were observed affecting LC FC with various brain regions, indicating a proportional decrease in LC FC for APOE4 carriers. These findings confirm that APOE4 carrier status significantly influences LC FC in preclinical AD, showcasing an intricate relationship with regional Aβ deposition. This underscores the critical role of genetic and pathological factors in early AD pathophysiology, offering insights into potential biomarkers for early detection and intervention strategies.

Assessment of the definition varying with display depth for three-dimensional light field displays

In the three-dimensional (3D) light field display (LFD), display quality is the most important concern, and from an intuitive perspective, the quality will decline with the increase of display depth. When evaluating display quality, definition is one of the indispensable indicators. However, there is no universally accepted quantitative measure in the 3D display like resolution in the two-dimensional (2D) display. In this work, a voxel analysis model is utilized to investigate the variation of definition with depth and two resolutions, spatial resolution and perceived visual resolution, with corresponding measurement methods are proposed to quantify the definition of 3D LFDs. The experiments are performed on two LFD devices, and the relationship between definition and display depth is obtained and analyzed. The display effect at different depths and the impact of human eye characteristics on the perceived display definition is studied.

White matter biomarker for predicting de novo Parkinson's disease using tract-based spatial statistics: a machine learning-based model.

Parkinson's disease (PD) is an irreversible, chronic degenerative disease of the central nervous system, potentially associated with cerebral white matter (WM) lesions. Investigating the microstructural alterations within the WM in the early stages of PD can help to identify the disease early and enable intervention to reduce the associated serious threats to health. This study selected 227 cases from the Parkinson's Progression Markers Initiative (PPMI) database, including 152 de novo PD patients and 75 normal controls (NC). Whole-brain voxel analysis of the WM was performed using the tract-based spatial statistics (TBSS) method. The WM regions with statistically significant differences (P<0.05) between the PD and NC groups were identified and used as masks. The mask was applied to each case's fractional anisotropy (FA) image to extract voxel values as feature vectors. Geometric dimensionality reduction was then applied to eliminate redundant values in the feature vectors. Subsequently, the cases were randomly divided into a training group (158 cases, including 103 PD patients and 55 NC) and a test group (69 cases, including 49 PD patients and 20 NC). The least absolute shrinkage and selection operator (LASSO) regression algorithm was employed to extract the minimal set of relevant features, then the random forest (RF) algorithm was utilized for classification using 5-fold cross validation. The resulting model was further integrated with clinical factors to create a comprehensive prediction model. In comparison to the NC group, the FA values in PD patients exhibited a statistically significant decrease (P<0.05), indicating the presence of widespread WM lesions across multiple brain regions. Moreover, the PD prediction model, constructed based on these WM lesion regions, yielded prediction accuracy (ACC) and area under the receiver operating characteristic (ROC) curve (AUC) values of 0.778 and 0.865 in the validation set, and 0.783 and 0.831 in the test set, respectively. Furthermore, the performance of the integrated model showed some improvement, with ACC and AUC values in the test set reaching 0.804 and 0.844, respectively. The quantitative calculation of WM lesion area on FA images using the TBSS method can serve as a neuroimaging biomarker for diagnosing and predicting early PD at the individual level. When integrated with clinical variables, the predictive performance improves.

Alzheimer Disease Pathology and Neurodegeneration in Midlife Obesity: A Pilot Study.

Obesity and excess adiposity at midlife are risk factors for Alzheimer disease (AD). Visceral fat is known to be associated with insulin resistance and a pro-inflammatory state, the two mechanisms involved in AD pathology. We assessed the association of obesity, MRI-determined abdominal adipose tissue volumes, and insulin resistance with PET-determined amyloid and tau uptake in default mode network areas, and MRI-determined brain volume and cortical thickness in AD cortical signature in the cognitively normal midlife population. Thirty-two middle-aged (age: 51.27±6.12 years, 15 males, body mass index (BMI): 32.28±6.39 kg/m2) cognitively normal participants, underwent bloodwork, brain and abdominal MRI, and amyloid and tau PET scan. Visceral and subcutaneous adipose tissue (VAT, SAT) were semi-automatically segmented using VOXel Analysis Suite (Voxa). FreeSurfer was used to automatically segment brain regions using a probabilistic atlas. PET scans were acquired using [11C]PiB and AV-1451 tracers and were analyzed using PET unified pipeline. The association of brain volumes, cortical thicknesses, and PiB and AV-1451 standardized uptake value ratios (SUVRs) with BMI, VAT/SAT ratio, and insulin resistance were assessed using Spearman's partial correlation. VAT/SAT ratio was associated significantly with PiB SUVRs in the right precuneus cortex (p=0.034) overall, controlling for sex. This association was significant only in males (p=0.044), not females (p=0.166). Higher VAT/SAT ratio and PiB SUVRs in the right precuneus cortex were associated with lower cortical thickness in AD-signature areas predominantly including bilateral temporal cortices, parahippocampal, medial orbitofrontal, and cingulate cortices, with age and sex as covariates. Also, higher BMI and insulin resistance were associated with lower cortical thickness in bilateral temporal poles. In midlife cognitively normal adults, we demonstrated higher amyloid pathology in the right precuneus cortex in individuals with a higher VAT/SAT ratio, a marker of visceral obesity, along with a lower cortical thickness in AD-signature areas associated with higher visceral obesity, insulin resistance, and amyloid pathology.

Visceral Abdominal Adipose Tissue and Insulin Resistance Respectively Influence Alzheimer Disease Amyloid Pathology and Neurodegeneration in Midlife

AbstractBackgroundObesity and adiposity at midlife, evidenced by high body mass index (BMI), are increasingly understood as a risk factor for Alzheimer’s disease (AD). Importantly, visceral fat is known to be associated with insulin resistance and proinflammatory state, the mechanisms involved in AD pathology. Herein, we aimed to assess the association between brain MRI volumes as well as amyloid and tau uptake with obesity, insulin resistance, and abdominal adipose tissue in cognitively normal midlife population.MethodA total of 34 middle‐aged (age: 51.27 ± 6.12 years, BMI: 32.28 ± 6.39 kg/m2), cognitively normal participants, underwent bloodwork, brain and abdominal MRI, as well as amyloid and tau PET scan. Homeostatic Model Assessment for Insulin Resistance (HOMAIR) &gt; 1.9 was used as a measure of insulin resistance. Visceral and subcutaneous adipose tissue (VAT, SAT) were semi‐automatically segmented using VOXel Analysis Suite (Voxa). FreeSurfer 7.1.1 was used for automatic segmentation of cortical and subcortical brain regions using a probabilistic atlas. Dynamic amyloid imaging was performed with a bolus injection of ∼15 mCi of [11C]PiB, followed by a 60‐min scan. A single intravenous bolus of between 7.2‐10.8 mCi of AV‐1451 was administered. Data from the 30‐60 minute, and 80‐100 minute post‐injection window for PiB and AV‐1451 were used for the analysis, respectively. The association of brain volumes and PiB and AV‐1451 SUVRs within the default mode network areas with BMI and VAT/SAT ratio were assessed using linear regression models.ResultWe observed lower right entorhinal white matter volumes in obese participants with insulin resistance compared to metabolically normal non‐obese group (p = 0.004), without any significant difference in PiB or AV‐1451 SUVRs. Regression models with sex, age and education as covariates showed a significant positive association between VAT/SAT ratio and left precuneus white matter PiB SUVRs (R2 = 0.31, p = 0.005), but no significant associations with AV‐1451 SUVRs.ConclusionIn our midlife obese sample with insulin resistance, there were lower right entorhinal white matter volume, which is involved in relaying information to the hippocampus. We also demonstrate higher early amyloid pathology in AD‐signature areas such as the precuneus in mid‐life persons with high VAT/SAT ratio, a marker of visceral obesity.

Body Mass Index, but not Visceral Abdominal Obesity and Insulin Resistance, Negatively Impacts White Matter Microstructure in Midlife

AbstractBackgroundMidlife obesity, as evidenced by high body mass index (BMI), is increasingly understood as a risk factor for development of Alzheimer’s disease. Importantly, visceral fat, by producing adipokines, contributes to inflammation as well as insulin resistance, mechanisms involved in development of Alzheimer’s disease. Herein, we aimed to assess association of white matter microstructure, measured by Diffusion tensor imaging (DTI), with obesity and abdominal adipose tissue composition at midlife.MethodA total of 34 middle‐aged (age: 51.27 ± 6.12 years, BMI: 32.28 ± 6.39 kg/m2), cognitively normal participants, underwent bloodwork, brain and abdominal MRI. Insulin resistance status was assessed using Homeostatic Model Assessment for Insulin Resistance. Abdominal visceral and subcutaneous adipose tissue (VAT, SAT) were automatically segmented using VOXel Analysis Suite (Voxa) and VAT/SAT ratio was calculated. A diffusion spectrum imaging scheme with a total of 98 diffusion samplings and a voxel size of 2 mm was acquired. Using DSI studio (https://dsi‐studio.labsolver.org/), the diffusion data were reconstructed using generalized q‐sampling imaging and the tensor metrics were calculated. Fractional anisotropy (FA), mean, axial, and radial diffusivity (MD, AD, RD, respectively) for white matter tracts were compared across study groups using one‐way ANOVA. Multiple regression models with sex, age, and education as covariates were used to assess the association of BMI, VAT/SAT ratio, and HOMA‐IR with DTI metrics.ResultOur data showed that BMI was negatively associated with AD values in right inferior fronto‐occipital fasciculus, tapetum and forceps major of Corpus Callosum (CC), superior and middle cerebellar peduncle, and bilateral reticular tract. The negative association of BMI with AD values in tapetum of CC was significant only in women. Right reticular tract MD and superior cerebellar peduncle MD and RD values were also significantly associated with BMI. There were no significant associations between neither of diffusion metrics and VAT/SAT ratio or HOMA‐IR.ConclusionAssociation of midlife obesity with lower axonal diffusivity, reflective of lower white matter integrity, with differential patterns in men and women especially in CC, is in line with previous findings in healthy individuals. Reduced axonal diffusivity in reticular and brain stem pathways could be indicative of early dysregulations observed in Alzheimer’s disease.

Hypoconnectivity of the Amygdala in Patients with Low-Back-Related Leg Pain Linked to Individual Mechanical Pain Sensitivity: A Resting-State Functional MRI Study.

To explore resting-state functional connectivity (rsFC) of the amygdala in patients with low-back-related leg pain (LBLP). For this prospective study, a total of 35 LBLP patients and 30 healthy controls (HCs) were included and underwent functional MRI and clinical assessments. Then, patients with LBLP were divided into acute LBLP (aLBLP) and chronic LBLP (cLBLP) subgroups. We further evaluated the between-group rsFC differences using left and right amygdala seeds in a whole-brain voxel analysis strategy. Finally, we performed correlation analysis between the rsFC values of altered regions and clinical indices. Compared to HCs, hypoconnectivity of the amygdala was observed in LBLP patients (P < 0.01, with correction). The amygdala's rsFC pattern was different between aLBLP and cLBLP patients: decreased the amygdala's FC to the right putamen, to the right paracentral lobule (PCL), or to the right posterior temporal lobe in aLBLP patients, while right amygdala to the bilateral anterior cingulate cortex (ACC) and the left postcentral gyrus (PoCG) in cLBLP patients. Correlation analysis showed that lower rsFC of the left amygdala to the right PCL was correlated with the von Frey filament (vF) test values of the left lumbar (p = 0.025) and right lumbar (p = 0.019) regions, and rsFC of the right amygdala to the left PoCG was correlated with lower vF test values of the left lumbar (p = 0.017), right lumbar spine (p = 0.003); to right PoCG was correlated with calf (p = 0.015); the rsFC of the right amygdala to bilateral ACC was negatively correlated with the pain rating index (p = 0.003). LBLP patients showed amygdala hypoconnectivity, and the altered pattern of amygdala rsFC was different in the acute and chronic phases. Moreover, the amygdala hypoconnectivity was related to individual mechanical sensitivity (vF test) in LBLP patients.

Intra voxel analysis in magnetic resonance imaging via deep learning

AbstractMagnetic Resonance Imaging (MRI) is a useful diagnostic method for producing anatomical images of the body. It differentiates tissues thanks to the measure of their proton density and relaxation times and . Since pathological tissues often present altered , and with respect to the physiological ones, MRI is largely used for the detection of large number of conditions. In addition, MRI scan presents an effective resolution able to detect very small anatomical elements, making the imaging system well suitable in contexts like prevention and early diagnosis. Nevertheless, system resolution imposes pathological volume to be larger than the voxel dimension. Since in some pathologies and conditions it could be very helpful to find tissues even smaller than the voxel dimension, this manuscript proposes an algorithm able to analyze voxel content and detect which one presents more than one tissue in its volume (i.e., which one is heterogeneous). More in detail, a machine learning algorithm is proposed, able to highlight, in the MR image, which pixel corresponds to an heterogeneous voxel. Method shows to be promising in combining good results and near real‐time processing in both simulated and real scenario.

Characterization on the mesostructural distribution of asphalt mixtures via digital image processing and relation to macroscopic performances

The distribution characteristics of the mesostructure of asphalt mixtures have an important influence on the macroscopic performance of asphalt mixtures. In this study, by means of industrial computed tomography and voxel analysis, the distribution of voids and skeleton microstructure of asphalt mixture is characterized from multiple perspectives. The relationship between mesostructural characteristic parameters and macroscopic properties of asphalt mixtures was established by the indirect tensile test, uniaxial compression test, and digital image correlation test. The results show that the mesostructural distribution of the voids of different types of asphalt mixtures shows a certain degree of transverse isotropy. Air voids content and fractal dimension of voids are the mesostructural characteristic parameters that affect the cracking resistance of asphalt mixtures at low temperatures. The air voids content has a negative exponential relationship with the strength of asphalt mixture. When the void fractal dimension is 1.3, the splitting tensile strength of asphalt mixture reaches the maximum value. Skeleton structure is a mesostructure characteristic parameter that affects the high temperature stability of asphalt mixture. The lower voids in coarse aggregate skeletons and degree of anisotropy are positively correlated with the uniaxial compressive strength. After voids in coarse aggregate skeletons are lower than 35 %, the uniaxial compressive strength of asphalt mixture is significantly improved. The proposed skeleton evaluation index can effectively characterize the material composition differences of different types of asphalt mixtures. The strain cloud diagram of the digital correlation experiment shows the place where the void distribution first reached the maximum strain of about 0.003 during loading, and the high strain area corresponds to the void centroid distribution. The air voids centroid distribution is the mesostructural characteristic parameter of the indirect tensile strain distribution.

A right convergence area of the prefrontal lobe is involved in the improvement of semantic fluency in patients with post-stroke aphasia

ABSTRACT Objectives This study aimed to longitudinally observe the improvement mechanism of semantic fluency in subacute post-stroke aphasia (PSA) patients using resting-state functional magnetic resonance imaging (rs-fMRI). Methods Twelve PSA patients, about one month after onset, were enrolled in this study and received speech-language therapy (SLT) for one month. Auditory comprehension and semantic fluency were evaluated using the Western Aphasia Battery (WAB) and the Animal Fluency Test. Before and after treatment, rs-fMRI data were collected, and the dice similarity coefficient was used to measure the spatial similarity between each patient’s lesion and a reference lesion. The left posterior inferior temporal gyrus (pITG) was used as a seed to calculate the normalized functional connectivity in whole-brain voxel analysis using DPABI software for statistical analysis. Results The dice similarity coefficient between each patient’s lesion and the reference lesion showed moderate to high intensity (0.57 ± 0.14) in the Montreal Neurological Institute space. After treatment, we found a significant increase in functional connectivity between the left pITG and the right prefrontal lobe convergence area (peak t = 8.219, Gaussian random field multiple comparison correction, voxel p < 0.001, cluster p < 0.05). The increase in functional connectivity was negatively correlated with the improvement in auditory comprehension (r =–0.707, p = 0.033) and positively correlated with the improvement in semantic fluency (r = 0.79, p = 0.02). Conclusion The improvement of semantic fluency in subacute PSA patients may require the participation of the right convergence area of the prefrontal lobe.

Structural and functional changes of the cerebellum in temporal lobe epilepsy.

This study aimed to comprehensively explore the cerebellar structural and functional changes in temporal lobe epilepsy (TLE) and its association with clinical information. The SUIT toolbox was utilized to perform cerebellar volume and diffusion analysis. In addition, we extracted the average diffusion values of cerebellar peduncle tracts to investigate microstructure alterations. Seed-based whole-brain analysis was used to investigate cerebellar-cerebral functional connectivity (FC). Subgroup analyses were performed to identify the cerebellar participation in TLE with/without hippocampal sclerosis (HS)/focal-to-bilateral tonic-clonic seizure (FBTCS) and TLE with different lateralization. TLE showed widespread gray matter atrophy in bilateral crusII, VIIb, VIIIb, left crusI, and left VIIIa. Both voxel and tract analysis observed diffusion abnormalities in cerebellar afferent peduncles. Reduced FC between the right crus II and the left parahippocampal cortex was found in TLE. Additionally, TLE showed increased FCs between left lobules VI-VIII and cortical nodes of the dorsal attention and visual networks. Across all patients, decreased FC was associated with poorer cognitive function, while increased FCs appeared to reflect compensatory effects. The cerebellar structural changes were mainly observed in HS and FBTCS subgroups and were regardless of seizure lateralization, while cerebellar-cerebral FC alterations were similar in all subgroups. TLE exhibited microstructural changes in the cerebellum, mainly related to HS and FBTCS. In addition, altered cerebellar-cerebral functional connectivity is associated with common cognitive alterations in TLE.

CT-Based Deep Learning Model for Predicting Local Recurrence-Free Survival in Esophageal Squamous Cell Carcinoma Patients Received Concurrent Chemo-Radiotherapy: A Multicenter Study

<h3>Purpose/Objective(s)</h3> For esophageal squamous cell carcinomas (ESCC) patients received concurrent chemo-radiotherapy (CCRT), local recurrence is the most common failure pattern and reliable markers for prognosis are lacking. Previous studies have demonstrated the predictive role of traditional radiomics features for prediction of local recurrence-free survival (LRFS) in ESCC. Nevertheless, traditional radiomics based on human-defined handcrafted features may not fully characterize tumor heterogeneity. Some studies have provided evidence that deep learning with advantages in voxel analysis could provide remarkable performance. This study aims to establish and validate a deep learning model for predicting LRFS in ESCC patients received CCRT. <h3>Materials/Methods</h3> We retrospectively included 302 patients from Xijing Hospital and randomly divided them into training set (201) and internal validation set (101) according to 2:1. 95 patients from Tianjin Cancer Hospital and Shandong Cancer Hospital were included as the external validation set. All patients underwent a contrast-enhanced computed tomography (CE-CT) scan before CCRT and were followed up for more than 24 months after CCRT. The deep learning model was developed by using 3D-Densenet deep learning architecture. Manually segmented tumors based on CE-CT were used as model input, LRFS was the prediction target. The deep learning signature was built using the deep-score output by the model. <h3>Results</h3> The median follow-up time of all patients was 26.77 months and 257 of 397 patients (64.74%) were confirmed local recurrence or death during the follow-up period. The deep learning model for prediction of LRFS in ESCC patients received CCRT showed good prognostic performance, with a C-index of 0.7337 (95% CI: 0.6800–0.7874) in the training set, which was validated in the internal (0.7203 [95% CI: 0.6450–0.7957]) and external validation (0.7167 [95% CI: 0.6416–0.7918]) sets, respectively. Kaplan-Meier survival analysis showed that the median of deep-score (-0.06) could stratify patients into high and low-risk groups for different LRFS. The low-risk group with the lower deep-score had a significantly higher LRFS than that of the high-risk group with a high deep-score (2-year LRFS 71.1% vs 33.0%, p<0.0001) in the training set. The result was validated in the internal (2-year LRFS 58.8% vs34.8%, p<0.01) and external validation (2-year LRFS 61.9% vs 22.4%, p<0.0001) sets, respectively. <h3>Conclusion</h3> Deep learning signature can be used as a non-invasive radiomics marker to predict LRFS in ESCC patients received CCRT. This is the first multicenter-based study using deep learning to predict local recurrence in esophageal cancer.

Alzheimer's Disease Diagnosis and Biomarker Analysis Using Resting-State Functional MRI Functional Brain Network With Multi-Measures Features and Hippocampal Subfield and Amygdala Volume of Structural MRI.

Accurate diagnosis of the initial phase of Alzheimer’s disease (AD) is essential and crucial. The objective of this research was to employ efficient biomarkers for the diagnostic analysis and classification of AD based on combining structural MRI (sMRI) and resting-state functional MRI (rs-fMRI). So far, several anatomical MRI imaging markers for AD diagnosis have been identified. The use of cortical and subcortical volumes, the hippocampus, and amygdala volume, as well as genetic patterns, has proven to be beneficial in distinguishing patients with AD from the healthy population. The fMRI time series data have the potential for specific numerical information as well as dynamic temporal information. Voxel and graphical analyses have gained popularity for analyzing neurodegenerative diseases, such as Alzheimer’s and its prodromal phase, mild cognitive impairment (MCI). So far, these approaches have been utilized separately for the diagnosis of AD. In recent studies, the classification of cases of MCI into those that are not converted for a certain period as stable MCI (MCIs) and those that converted to AD as MCIc has been less commonly reported with inconsistent results. In this study, we verified and validated the potency of a proposed diagnostic framework to identify AD and differentiate MCIs from MCIc by utilizing the efficient biomarkers obtained from sMRI, along with functional brain networks of the frequency range .01–.027 at the resting state and the voxel-based features. The latter mainly included default mode networks (amplitude of low-frequency fluctuation [ALFF], fractional ALFF [ALFF], and regional homogeneity [ReHo]), degree centrality (DC), and salience networks (SN). Pearson’s correlation coefficient for measuring fMRI functional networks has proven to be an efficient means for disease diagnosis. We applied the graph theory to calculate nodal features (nodal degree [ND], nodal path length [NL], and between centrality [BC]) as a graphical feature and analyzed the connectivity link between different brain regions. We extracted three-dimensional (3D) patterns to calculate regional coherence and then implement a univariate statistical t-test to access a 3D mask that preserves voxels showing significant changes. Similarly, from sMRI, we calculated the hippocampal subfield and amygdala nuclei volume using Freesurfer (version 6). Finally, we implemented and compared the different feature selection algorithms to integrate the structural features, brain networks, and voxel features to optimize the diagnostic identifications of AD using support vector machine (SVM) classifiers. We also compared the performance of SVM with Random Forest (RF) classifiers. The obtained results demonstrated the potency of our framework, wherein a combination of the hippocampal subfield, the amygdala volume, and brain networks with multiple measures of rs-fMRI could significantly enhance the accuracy of other approaches in diagnosing AD. The accuracy obtained by the proposed method was reported for binary classification. More importantly, the classification results of the less commonly reported MCIs vs. MCIc improved significantly. However, this research involved only the AD Neuroimaging Initiative (ADNI) cohort to focus on the diagnosis of AD advancement by integrating sMRI and fMRI. Hence, the study’s primary disadvantage is its small sample size. In this case, the dataset we utilized did not fully reflect the whole population. As a result, we cannot guarantee that our findings will be applicable to other populations.

Voxel characteristic estimation of integral imaging display system using self-interference incoherent digital holography.

Three-dimensional (3D) images reconstructed by integral imaging display are captured as a complex hologram using self-interference incoherent digital holography (SIDH) and analyzed for the volumetric image characteristics. The integrated images can present 3D perception through not only binocular disparity but also volumetric property, which is represented in forming a volume picture element, called 'voxel', and an important criterion to distinguish the integral imaging from the multiview 3D display. Since SIDH can record the complex hologram under incoherent lighting conditions, the SIDH camera system has the advantage to measure the voxel formed with the incoherent light fields. In this paper, we propose a technique to estimate and analyze the voxel characteristics of the integral imaging system such as the depth location and resolution. The captured holograms of the integrated images are numerically reconstructed by depth for the voxel analysis. The depth location of the integrated image can be calculated and obtained using the autofocus algorithms and the focus metrics values, which also show the modalities of depth resolution. The estimation method of this paper can be applied to the accurate and quantitative analysis of the volumetric characteristics of light field 3D displays.

Complexity Assessment of Chronic Pain in Elderly Knee Osteoarthritis Based on Neuroimaging Recognition Techniques.

The chronic pain of knee osteoarthritis in the elderly is investigated in detail in this paper, as well as the complexity of chronic pain utilising neuroimaging recognition techniques. Chronic pain in knee osteoarthritis (KOA) has a major effect on patients' quality of life and functional activities; therefore, understanding the causes of KOA pain and the analgesic advantages of different therapies is important. In recent years, neuroimaging techniques have become increasingly important in basic and clinical pain research. Thanks to the application and development of neuroimaging techniques in the study of chronic pain in KOA, researchers have found that chronic pain in KOA contains both injury-receptive and neuropathic pain components. The neuropathic pain mechanism that causes KOA pain is complicated, and it may be produced by peripheral or central sensitization, but it has not gotten enough attention in clinical practice, and there is no agreement on how to treat combination neuropathic pain KOA. As a result, using neuroimaging techniques such as magnetic resonance imaging (MRI), electroencephalography (EEG), magnetoencephalography (MEG), and near-infrared spectroscopy (NIRS), this review examines the changes in brain pathophysiology-related regions caused by KOA pain, compares the latest results in pain assessment and prediction, and clarifies the central brain analgesic mechanistic. The capsule network model is introduced in this paper from the perspective of deep learning network structure to construct an information-complete and reversible image low-level feature bridge using isotropic representation, predict the corresponding capsule features from MRI voxel responses, and then, complete the accurate reconstruction of simple images using inverse transformation. The proposed model improves the structural similarity index by about 10%, improves the reconstruction performance of low-level feature content in simple images by about 10%, and achieves feature interpretation and analysis of low-level visual cortical fMRI voxels by visualising capsule features, according to the experimental results.

Alterations in Resting-State Functional MRI Connectivity Related to Cognitive Changes in Intracranial Dural Arteriovenous Fistulas Before and After Embolization Treatment.

Cognitive decline is a non-hemorrhagic, major complication of intracranial dural arteriovenous fistula (DAVF), thought to be primarily related to venous hypertension. However, imaging features to predict cognitive decline are scanty in the literature. To evaluate functional connectivity (FC) changes of resting-state networks (RSNs) in DAVF before and after treatment and its relation to cognitive impairment. Prospective. DAVF subjects were screened for inclusion. Pre-embolization (N=33, mean age 45.9 years, 29 males), 1 month post-embolization (N=20, mean age 42.7 years, 19 males), and healthy controls (HC, N=33, mean age 45.09 years, 27 males). 3.0 T, resting-state functional magnetic resonance imaging (MRI), three-dimensional (3D) T1, T2 fast spin echo (FSE), diffusion weighted imaging (DWI), susceptibility weighted imaging (SWI), fluid-attenuated inversion recovery, and time of flight. Data quality assessment was performed. FC analysis was done using group independent component analysis (ICA) and seed to voxel analysis. Neuropsychology (NP) scores of patients were compared with HC and correlated with FC changes. Voxel-wise parametric T-statistics for F-test was executed in FC analysis (p-FDR corrected <0.05). NP scores between DAVF group and HC group were compared using one-way analysis of variance with post hoc Bonferroni correction (P < 0.05). Both RSNs analysis methods showed reduced FC at the precuneus-posterior cingulate cortex (PC-PCC) of default mode network (DMN), anterior cingulate cortex (ACC) of the salience network (SN), and possible compensatory increased connectivity at the frontoparietal (FPN) and dorsal attention (DAN) networks. DAVF with low NP scores showed reduced FC at DMN and SN and minimal to absent connectivity at FPN and DAN. At post-embolization 1-month follow-up, improvement in FC at PC-PCC of DMN and ACC of SN were noted. RS-fMRI in DAVF displayed FC changes that may be related to cognitive decline and its subsequent reversibility after treatment. FC changes at DMN, SN, FPN, and DAN were linked to cognitive decline and the corresponding NP scores. 2 TECHNICAL EFFICACY: Stage 2.

MLTI-01. Study on the Association between Prone Locations and Prognosis of Breast Cancer Brain Metastases via Voxel Analysis

Abstract Purpose This study aimed to analyze the preferred locations of secondary brain tumors of breast carcinoma according to different biological characteristics. Method 161 Breast cancer brain metastasis (BCBM) patients diagnosed between January 2007 and February 2018 were retrospectively analyzed. MR images when brain metastases occurred were collected, registered, segmented. The frequency and p-value heatmaps were constructed to compare two biological phenotypes using two-tailed Fisher’s exact test. Age, treatments, the status of ER, PR, and HER2, luminal subtype, tumor marker levels in peripheral blood including CEA, CA19-9, and CA15-3 were statistically analyzed. Survival data were analyzed by Kaplan–Meier method, log-rank test, and multivariate logistic regression. Result The frequency heat map shows lesions of patients with BCBM are more inclined to the cerebellar hemisphere. Older patients(&amp;gt;49 years old, median age) mainly occur in the left frontal lobe, the right parietal lobe, and adjacent meninges comparing with white matter of the left parietal lobe, cerebellar vermis, and areas around the fourth ventricle among younger patients and the difference is significant. Patients with tumors located on the surface of the brain are more likely to undergo surgical treatment, however, conservative treatment was considered if metastases are located at the midline structure. ER and PR-positive and HER-2 enriched patients have more significance in metastases, at the left parieto-occipital junction area, frontal lobe, parietal lobe, cerebellar hemisphere, and adjacent meninges. Metastases with high levels of CEA are found significantly at areas around the central anterior gyrus. Lesions with an elevated level of CA19-9 and CA15-3 tend to be frontal, parietal, and occipital. Besides, HER-2 enriched in primary sites and a normal level of CA15-3 in peripheral blood were two independent protective factors in determining prognostic outcomes. Conclusion The preferred locations of BCBM could be clues of further study and helpful for clinical strategies.

Abstract PS3-07: Molecular imaging of hypoxia and granzyme B alterations during combination treatment with immunotherapy in triple negative breast cancer

Abstract Background: Although a portion of triple negative breast cancer (TNBC) is sensitive to chemotherapeutic treatment with agents such as paclitaxel (PTX), patients have a high risk of recurrence and short overall and progression-free survival. Clinical trials for TNBC using immune checkpoint inhibitors such as anti-programmed cell death 1 (PD1) have met some success; however, only a small percentage of patients have a positive response to immunotherapy and there is risk for severe side effects. Therefore, there is a need for improvements in monitoring and predicting patient-specific response. The goal of this study is to use non-invasive visualization of the tumor microenvironment to collect longitudinal information that can be used to study tumor response to immunotherapy. By applying positron emission tomography (PET) imaging techniques to investigate changes in the tumor microenvironment (hypoxia and immune cell activation), we seek to predict early response to immunotherapy and better identify which treatments will be effective for individual tumors in TNBC. Methods: A TNBC mouse mammary carcinoma cell line, 4T1, was transduced with CMV-luciferase and 2x105 cells were injected into the third mammary fat pad of 5-6 week old female Balb/c mice (N=28). Mice in cohort 1 received: PTX (10 mg/kg), anti-PD1 (200 µg), both, or vehicle control (saline) (n=4/group). Mice in cohort 2 received: anti-PD1 (n=5), combination PTX/anti-PD1 (n=4), or vehicle control (saline; n=3). Treatments were administered intraperitoneally on days 0, 2, and 5 for cohort 1 (n=16) who underwent granzyme B-PET imaging ([68Ga]-NOTA-GZP-PET) and on days 0, 2, 5, and 8 for cohort 2 (n=12) who underwent hypoxia imaging with [18F]-fluoromisonidazole (FMISO)-PET imaging. Bioluminescence (BLI) imaging and caliper measurements were performed to track tumor size changes at multiple timepoints and tumors were collected for histological validation on day 20. Mean standard uptake value (SUVmean) was calculated as percent of day 0, and statistical analyses were performed with unpaired t-tests and Wilcoxon-rank sum tests. Results: Voxel analysis of GZP-PET images revealed an 42.9% increase in T cell activation of TNBC tumors treated with single-agent PTX compared to anti-PD1 alone on day 3 (p=0.08). FMISO-PET revealed that tumors treated with anti-PD1 alone had lower hypoxic fraction compared to control group tumors (p=0.10) and tumors treated with combination PTX/anti-PD1 (p=0.17) on day 3. BLI data showed that treatment with PTX and anti-PD1 significantly decreased viability signal between days 3 and 6 for cohort 1 (p=0.04). Non-responders to treatment had a significantly higher tumor volume compared to responders starting on day 6 (p&amp;lt;0.05). SUVmean, indicating T cell activation, was significantly higher for responders compared to non-responders on days 3 and 6 (p&amp;lt;0.05). There was a signifciant decrease in hypoxia, as measured thorugh FMISO-PET SUVmean on day 6 for responders compared to non-responders (p=0.04). Conclusion: It is known that the tumor microenvironment can impact neovascularization, tumor cell invasion, tumor oxygenation and growth. Results from this study show that noninvasive PET imaging can provide data on changes in T cell activation and hypoxia in response to chemotherapeutic treatment and immunotherapy in TNBC. Utilizing clinically-translatable advanced imaging strategies to understand the biologically distinct features of the TNBC tumor microenvironment can aid in personalizing anti-cancer therapies. Acknowledgements: We thank the American Cancer Society for RSG-18-006-01-CCE, NIH NCI R01 CA240589, and NIH P30CA013148. Citation Format: Tiara S. Napier, Chanelle L. Hunter, Patrick N. Song, Ben M. Larimer, Anna G. Sorace. Molecular imaging of hypoxia and granzyme B alterations during combination treatment with immunotherapy in triple negative breast cancer [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS3-07.

Investigation of Deep-Learning-Driven Identification of Multiple Sclerosis Patients Based on Susceptibility-Weighted Images Using Relevance Analysis.

The diagnosis of multiple sclerosis (MS) is usually based on clinical symptoms and signs of damage to the central nervous system, which is assessed using magnetic resonance imaging. The correct interpretation of these data requires excellent clinical expertise and experience. Deep neural networks aim to assist clinicians in identifying MS using imaging data. However, before such networks can be integrated into clinical workflow, it is crucial to understand their classification strategy. In this study, we propose to use a convolutional neural network to identify MS patients in combination with attribution algorithms to investigate the classification decisions. The network was trained using images acquired with susceptibility-weighted imaging (SWI), which is known to be sensitive to the presence of paramagnetic iron components and is routinely applied in imaging protocols for MS patients. Different attribution algorithms were used to the trained network resulting in heatmaps visualizing the contribution of each input voxel to the classification decision. Based on the quantitative image perturbation method, we selected DeepLIFT heatmaps for further investigation. Single-subject analysis revealed veins and adjacent voxels as signs for MS, while the population-based study revealed relevant brain areas common to most subjects in a class. This pattern was found to be stable across different echo times and also for a multi-echo trained network. Intensity analysis of the relevant voxels revealed a group difference, which was found to be primarily based on the T1w magnitude images, which are part of the SWI calculation. This difference was not observed in the phase mask data.

Forging a path to mesoscopic imaging success with ultra-high field functional magnetic resonance imaging

Functional magnetic resonance imaging (fMRI) studies with ultra-high field (UHF, 7+ Tesla) technology enable the acquisition of high-resolution images. In this work, we discuss recent achievements in UHF fMRI at the mesoscopic scale, on the order of cortical columns and layers, and examine approaches to addressing common challenges. As researchers push to smaller and smaller voxel sizes, acquisition and analysis decisions have greater potential to degrade spatial accuracy, and UHF fMRI data must be carefully interpreted. We consider the impact of acquisition decisions on the spatial specificity of the MR signal with a representative dataset with 0.8 mm isotropic resolution. We illustrate the trade-offs in contrast with noise ratio and spatial specificity of different acquisition techniques and show that acquisition blurring can increase the effective voxel size by as much as 50% in some dimensions. We further describe how different sources of degradations to spatial resolution in functional data may be characterized. Finally, we emphasize that progress in UHF fMRI depends not only on scientific discovery and technical advancement, but also on informal discussions and documentation of challenges researchers face and overcome in pursuit of their goals.This article is part of the theme issue ‘Key relationships between non-invasive functional neuroimaging and the underlying neuronal activity’.