Quantitative Imaging Tool Research Articles

Quadri-wave lateral shearing interferometry: a versatile tool for quantitative phase imaging

Quantitative phase imaging (QPI) has emerged as a powerful tool in label-free bioimaging, in situ microstructure characterization for advanced manufacturing, and high-speed imaging of material property changes. Among various QPI methods, quadri-wave lateral shearing interferometry (QWLSI) stands out for its unique advantages in compactness, robustness, and high temporal resolution, making it an ideal choice for a wide range of applications. The compact design of QWLSI allows for easy integration with existing microscopy systems, while its robustness is manifested in the ability to maintain precise interferometric sensitivity even in high-vibration environments. Moreover, QWLSI also enables single-shot measurements that facilitate the capture of fast dynamic processes. This paper provides an in-depth exploration of the technical aspects of QWLSI, focusing on the evolution of its optical system and the primary algorithms used in wavefront reconstruction. The review also showcases significant applications of QWLSI, with a particular emphasis on its contributions to biomedical imaging. By discussing the advantages, limitations, and potential future developments of QWLSI, this paper aims to provide a comprehensive overview of this powerful QPI technique and its impact on various research fields.

Association between Chronic Kidney Disease and Non-Alcoholic Fatty Liver Disease: A Cross Sectional Study

Abstract Background Nonalcoholic fatty liver disease (NAFLD) is a growing health problem that poses a significant burden as it is currently the most prevalent form of chronic liver disease, affecting an estimated 25% of the worldwide population. Aim of the Work to evaluate the role of plasma LCN2 levels in the diagnosis and prognosis of patients with non-alcoholic fatty liver disease. Patients and Methods Our case-control study was conducted on 102 Egyptian subjects between the ages of 18 and 70, recruited from the Internal Medicine and Hepatology Department and Clinics at Ain Shams University Hospital during the period from March 2023 to August 2023, and divided them into two groups. Group I: 51 Egyptian patients diagnosed with NAFLD (61% males; 39% females). Group II: 51 Egyptian healthy controls (51% males; 49% females) Results In this study, LCN2 levels were significantly higher in cases than in the control group (p < 0.001), with a mean of 1893.214 ±1002.852 in the cases and a mean of 466.020 ± 397.699 in the controls. This suggests the use of LCN2 as a possible diagnostic marker of NAFLD. The mean LCN2 levels in this study also significantly increased as the grade of fatty liver increased from I to III (p < 0.001). This in turn proposes the use of LCN2 as a prognostic marker for NAFLD progression. LCN2 also significantly correlated with the fatty liver index and NAFLD Fibrosis scoring systems, but not with Fib-4. It showed an excellent diagnostic performance in this study with an area under ROC of 0.906, an 84% sensitivity, a 90% specificity, 89.6% PPV and 85.2% NPV for the prediction of NAFLD patients. Conclusion Lipocalin-2 performs as a diagnostic marker for non-alcoholic liver disease, and a possible prognostic test that may be used to follow up the degree of steatosis. Other studies should be done on a larger size of participants to determine the prevalence of LCN2 in Egyptian population as well as other populations. Other studies should also be expanded to include NAFLD with degrees of fibrosis, and more quantitative imaging tools could be used to compare LCN2 levels with the NAFLD stage. The levels of LCN2 can be also measured in cohort studies before and after treatment of NAFLD to evaluate its sensitivity to the response of treatment.

Quantitative Three-Dimensional Imaging Analysis of HfO2 Nanoparticles in Single Cells via Deep Learning Aided X-ray Nano-Computed Tomography.

It is crucial for understanding mechanisms of drug action to quantify the three-dimensional (3D) drug distribution within a single cell at nanoscale resolution. Yet it remains a great challenge due to limited lateral resolution, detection sensitivities, and reconstruction problems. The preferable method is using X-ray nano-computed tomography (Nano-CT) to observe and analyze drug distribution within cells, but it is time-consuming, requiring specialized expertise, and often subjective, particularly with ultrasmall metal nanoparticles (NPs). Furthermore, the accuracy of batch data analysis through conventional processing methods remains uncertain. In this study, we used radioenhancer ultrasmall HfO2 nanoparticles as a model to develop a modular and automated deep learning aided Nano-CT method for the localization quantitative analysis of ultrasmall metal NPs uptake in cancer cells. We have established an ultrasmall objects segmentation method for 3D Nano-CT images in single cells, which can highly sensitively analyze minute NPs and even ultrasmall NPs in single cells. We also constructed a localization quantitative analysis method, which may accurately segment the intracellularly bioavailable particles from those of the extracellular space and intracellular components and NPs. The high bioavailability of HfO2 NPs in tumor cells from deeper penetration in tumor tissue and higher tumor intracellular uptake provide mechanistic insight into HfO2 NPs as advanced radioenhancers in the combination of quantitative subcellular image analysis with the therapeutic effects of NPs on 3D tumor spheroids and breast cancer. Our findings unveil the substantial uptake rate and subcellular quantification of HfO2 NPs by the human breast cancer cell line (MCF-7). This revelation explicates the notable efficacy and safety profile of HfO2 NPs in tumor treatment. These findings demonstrate that this 3D imaging technique promoted by the deep learning algorithm has the potential to provide localization quantitative information about the 3D distributions of specific molecules at the nanoscale level. This study provides an approach for exploring the subcellular quantitative analysis of NPs in single cells, offering a valuable quantitative imaging tool for minute amounts or ultrasmall NPs.

Quantitative Cartilage T2 and T1rho Mapping: Is There a Clinical Role? From the AJR Special Series on Quantitative Imaging.

Despite more than 20 years of development, the MRI-based cartilage compositional biomarkers T2 and T1rho have not been routinely applied in clinical practice. This review examines these measures' historical development and frames the challenges in the application of these quantitative imaging tools to the care of patients with cartilage injury and osteoarthritis using the hierarchical model of efficacy proposed by Fryback and Thornbury. T2 and T1rho have been validated for the evaluation of early compositional and structural changes in cartilage extracellular matrix. Yet, these biomarkers lack direct correlation with pain or function loss, lack standardization of methods for acquisition and analysis, and have a limited role in guiding therapeutic management given the absence of effective disease-modifying osteoarthritis drugs. These issues present significant challenges in the path to the biomarkers' future implementation in clinical care. Nonetheless, these MRI-based cartilage compositional biomarkers provide an essential tool for musculoskeletal research and can provide important information on the biophysical properties of cartilage that will continue to contribute to our understanding of cartilage injury and osteoarthritis pathogenesis.

Spinal cord metrics derived from diffusion MRI: improvement in prognostication in cervical spondylotic myelopathy compared with conventional MRI.

A major shortcoming in optimizing care for patients with cervical spondylotic myelopathy (CSM) is the lack of robust quantitative imaging tools offered by conventional MRI. Advanced MRI modalities, such as diffusion MRI (dMRI), including diffusion tensor imaging (DTI) and diffusion basis spectrum imaging (DBSI), may help address this limitation by providing granular evaluations of spinal cord microstructure. Forty-seven patients with CSM underwent comprehensive clinical assessments and dMRI, followed by DTI and DBSI modeling. Conventional MRI metrics included 10 total qualitative and quantitative assessments of spinal cord compression in both the sagittal and axial planes. The dMRI metrics included 12 unique measures including anisotropic tensors, reflecting axonal diffusion, and isotropic tensors, describing extraaxonal diffusion. The primary outcome was the modified Japanese Orthopaedic Association (mJOA) score measured at 2 years postoperatively. Extreme gradient boosting-supervised classification algorithms were used to classify patients into disease groups and to prognosticate surgical outcomes at 2-year follow-up. Forty-seven patients with CSM, including 24 (51%) with a mild mJOA score, 12 (26%) with a moderate mJOA score, and 11 (23%) with a severe mJOA score, as well as 21 control subjects were included. In the classification task, the traditional MRI metrics correctly assigned patients to healthy control versus mild CSM versus moderate/severe CSM cohorts, with an accuracy of 0.647 (95% CI 0.64-0.65). In comparison, the DTI model performed with an accuracy of 0.52 (95% CI 0.51-0.52) and the DBSI model's accuracy was 0.81 (95% CI 0.808-0.814). In the prognostication task, the traditional MRI metrics correctly predicted patients with CSM who improved at 2-year follow-up on the basis of change in mJOA, with an accuracy of 0.58 (95% CI 0.57-0.58). In comparison, the DTI model performed with an accuracy of 0.62 (95% CI 0.61-0.62) and the DBSI model had an accuracy of 0.72 (95% CI 0.718-0.73). Conventional MRI is a powerful tool to assess structural abnormality in CSM but is inherently limited in its ability to characterize spinal cord tissue injury. The results of this study demonstrate that advanced imaging techniques, namely DBSI-derived metrics from dMRI, provide granular assessments of spinal cord microstructure that can offer better diagnostic and prognostic utility.

Radiomics Analysis of Quantitative Maps from Synthetic MRI for Predicting Grades and Molecular Subtypes of Diffuse Gliomas.

To investigate the feasibility of using radiomics analysis of quantitative maps from synthetic MRI to preoperatively predict diffuse glioma grades, isocitrate dehydrogenase (IDH) subtypes, and 1p/19q codeletion status. Data from 124 patients with diffuse glioma were used for analysis (n = 87 for training, n = 37 for testing). Quantitative T1, T2, and proton density (PD) maps were obtained using synthetic MRI. Enhancing tumour (ET), non-enhancing tumour and necrosis (NET), and peritumoral edema (PE) regions were segmented followed by manual fine-tuning. Features were extracted using PyRadiomics and then selected using Levene/T, BorutaShap and maximum relevance minimum redundancy algorithms. Asupport vector machine was adopted for classification. Receiver operating characteristic curve analysis and integrated discrimination improvement analysis were implemented to compare the performance of different radiomics models. Radiomics models constructed using features from multiple tumour subregions (ET + NET + PE) in the combined maps (T1 + T2 + PD) achieved the highest AUC in all three prediction tasks, among which the AUC for differentiating lower-grade and high-grade diffuse gliomas, predicting IDH mutation status and predicting 1p/19q codeletion status were 0.92, 0.95 and 0.86 respectively. Compared with those constructed on individual T1, T2, and PD maps, the discriminant ability of radiomics models constructed on the combined maps separately increased by 11, 17 and 10% in predicting glioma grades, 35, 52 and 19% in predicting IDH mutation status, and 16, 15 and 14% in predicting 1p/19q codeletion status (p < 0.05). Radiomics analysis of quantitative maps from synthetic MRI provides anew quantitative imaging tool for the preoperative prediction of grades and molecular subtypes in diffuse gliomas.

Ultra-miniature dual-wavelength spatial frequency domain imaging for micro-endoscopy.

There is a need for a cost-effective, quantitative imaging tool that can be deployed endoscopically to better detect early stage gastrointestinal cancers. Spatial frequency domain imaging (SFDI) is a low-cost imaging technique that produces near-real time, quantitative maps of absorption and reduced scattering coefficients, but most implementations are bulky and suitable only for use outside the body. We aim to develop an ultra-miniature SFDI system comprising an optical fiber array (diameter 0.125mm) and a micro camera ( package) to displace conventionally bulky components, in particular, the projector. First, we fabricated a prototype with an outer diameter of 3mm, although the individual component dimensions could permit future packaging to a diameter. We developed a phase-tracking algorithm to rapidly extract images with fringe projections at three equispaced phase shifts to perform SFDI demodulation. To validate the performance, we first demonstrate comparable recovery of quantitative optical properties between our ultra-miniature system and a conventional bench-top SFDI system with an agreement of 15% and 6% for absorption and reduced scattering, respectively. Next, we demonstrate imaging of absorption and reduced scattering of tissue-mimicking phantoms providing enhanced contrast between simulated tissue types (healthy and tumour), done simultaneously at wavelengths of 515 and 660nm. Using a support vector machine classifier, we estimate that sensitivity and specificity values of are feasible for detecting simulated squamous cell carcinoma. This device shows promise as a cost-effective, quantitative imaging tool to detect variations in optical absorption and scattering as indicators of cancer.

Detection and Quantification of Cryptococcus Uptake by Phagocytic Cells Using Imaging Flow Cytometry.

Cryptococcus neoformans, the predominant etiological agent of cryptococcosis, is an encapsulated fungal pathogen found ubiquitously in the environment that causes pneumonia and life-threatening infections of the central nervous system. Following inhalation of yeasts or desiccated basidiospores into the lung alveoli, resident pulmonary phagocytic cells aid in the identification and eradication of Cryptococcus yeast through their arsenal of pattern recognition receptors (PRRs). PRRs recognize conserved pathogen-associated molecular patterns (PAMPs), such as branched mannans, β-glucans, and chitins that are the major components of the fungal cell wall. However, the key receptors/ligand interactions required for cryptococcal recognition and eventual fungal clearance have yet to be elucidated. Here we present an imaging flow cytometer (IFC) method that offers a novel quantitative cellular imaging and population statistics tool to accurately measure phagocytosis of fungal cells. It has the capacity to measure two distinct steps of phagocytosis: association/attachment and internalization in a high-throughput and quantitative manner that is difficult to achieve with other technologies. Results from these IFC studies allow for the potential to identify PRRs required for recognition, uptake, and subsequent activation of cytokine production, as well as other effector cell responses required for fungal clearance.

Interaction of smoking and aging on emphysema and small airways disease in asymptomatic healthy men by CT-based parametric response mapping analysis

To explore whether small airway disease and emphysema were affected by the interaction between smoking and aging on chest computed tomography (CT) images of asymptomatic healthy men analysed using a quantitative imaging tool parametric response mapping (PRM). In this retrospective study, 95 asymptomatic healthy men underwent biphasic chest CT. The PRM classifies lung as a percentage of normal (PRMNormal%), functional small airway disease (PRMfSAD%), and emphysema (PRMEmph%). The patients were divided into groups based on their age and smoking status. Multiple linear regression analysis was applied to explore the factors influencing lung injury. Simple effects analysis was performed to explore the interaction between different age groups and smoking status. The interaction between aging and smoking significantly affected PRMfSAD% and PRMEmph% (p<0.001). The age range 60-69 and smoking were associated with increased PRMfSAD% and PRMEmph% (p<0.05). Futher stratification into different age subgroups showed that smoking was associated with increased PRMfSAD% and PRMEmph% in the 50-59 year age group. Besides, smoking in the 50-59 and 60-69 years group was associated with decreased PRMNormal%, while smoking in the 60-69 years group did not significantly influence the prevalence of PRMfSAD% and PRMEmph% (p>0.05). PRM reveals the interplay between smoking and aging in the development of lung injury in asymptomatic healthy men. Aging and smoking are important factors of emphysema and small airway disease in the 50-69 years group. In the 60-69 years group, aging poses a greater risk of lung injury compared to smoking.

Dual-channel binary diffuser-based coherent modulation imaging.

To improve the performance of binary diffuser-based coherent modulation imaging (CMI), a double-channel optical alignment was proposed. Two diffraction patterns formed by the reflection and transmission of a binary diffuser were simultaneously captured and adopted for iterative reconstruction in combination. The information involved in reflected light, not considered in the traditional single-channel optical alignment, was also reconstructed in this dual-channel binary diffuser-based coherent modulation imaging (DB-CMI). The reconstruction quality and speed were improved and verified by both numerical simulations and proof-of-principle experiments. Therefore, DB-CMI improves traditional CMI and provides a powerful tool for quantitative phase imaging.

Custom-made flow phantoms for quantitative ultrasound microvessel imaging

Morphologically realistic flow phantoms are essential experimental tools for quantitative ultrasound-based microvessel imaging. As new quantitative flow imaging tools are developed, the need for more complex vessel-mimicking phantoms is indisputable. In this article, we propose a method for fabricating phantoms with sub-millimeter channels consisting of branches and curvatures in various shapes and sizes suitable for quantifying vessel morphological features. We used different tissue-mimicking materials (TMMs) compatible with ultrasound imaging as the base and metal wires of different diameters (0.15–1.25 mm) to create wall-less channels. The TMMs used are silicone rubber, plastisol, conventional gelatin, and medical gelatin. Mother channels in these phantoms were made in diameters of 1.25 mm or 0.3 mm and the daughter channels in diameters 0.3 mm or 0.15 mm. Bifurcations were created by soldering wires together at branch points. Quantitative parameters were assessed, and accuracy of measurements from the ground truth were determined. Channel diameters were seen to have increased (76–270%) compared to the initial state in the power Doppler images, partly due to blood mimicking fluid pressure. Amongst the microflow phantoms made from the different TMMs, the medical gelatin phantom was selected as the best option for microflow imaging, fulfilling the objective of being easy to fabricate with high transmittance while having a speed of sound and acoustic attenuation close to human tissue. A flow velocity of 0.85 ± 0.01 mm/s, comparable to physiological flow velocity was observed in the smallest diameter phantom (medical gelatin branch) presented here. We successfully constructed more complex geometries, including tortuous and multibranch channels using the medical gelatin as the TMM. We anticipate this will create new avenues for validating quantitative ultrasound microvessel imaging techniques.

Automatic 3D joint erosion detection for the diagnosis and monitoring of rheumatoid arthritis using hand HR-pQCT images.

Rheumatoid arthritis (RA) is a chronic inflammatory disease. It leads to bone erosion in joints and other complications, which severely affect patients' quality of life. To accurately diagnose and monitor the progression of RA, quantitative imaging and analysis tools are desirable. High-resolution peripheral quantitative computed tomography (HR-pQCT) is such a promising tool for monitoring disease progression in RA. However, automatic erosion detection tools using HR-pQCT images are not yet available. Inspired by the consensus among radiologists on the erosions in HR-pQCT images, in this paper we define erosion as the significant concave regions on the cortical layer, and develop a model-based 3D automatic erosion detection method. It mainly consists of two steps: constructing closed cortical surface, and detecting erosion regions on the surface. In the first step, we propose an initialization-robust region competition methods for joint segmentation, and then fill the surface gaps by using joint bone separation and curvature-based surface alignment. In the second step, we analyze the curvature information of each voxel, and then aggregate the candidate voxels into concave surface regions and use the shape information of the regions to detect the erosions. We perform qualitative assessments of the new method using 59 well-annotated joint volumes. Our method has shown satisfactory and consistent performance compared with the annotations provided by medical experts.

674 Diffusion Basis Spectrum Imaging Predicts Comprehensive Clinical Outcomes Following Surgery for Cervical Spondylotic Myelopathy

INTRODUCTION: A major shortcoming in improving care for cervical spondylotic myelopathy (CSM) patients is the lack of robust quantitative imaging tools to guide surgical decision-making. METHODS: A prospective cohort study enrolled fifty CSM patients who underwent cervical decompressive surgery. DBSI metrics assessed white matter tract integrity by fractional anisotropy, axial diffusivity, radial diffusivity, and fiber fraction. To evaluate extra-axonal diffusion, DBSI measures restricted and non-restricted fractions. Neurofunctional status was assessed by the mJOA, MDI, and DASH. Quality of life was measured by the SF-36 PCS and MCS. The NDI was used to measure self-reported neck pain. Patient satisfaction was assessed by the NASS satisfaction index. Support vector machine classification algorithms were used to predict surgical outcomes. Specifically, three models were built for each clinical outcome measure (e.g., mJOA), including clinical, DBSI, and combined models. RESULTS: Twenty-seven mild (mJOA 15-17), 12 moderate (12-14) and 11 severe (0-11) CSM patients were enrolled. Twenty-four (60%) underwent anterior surgery compared to 16 (40%) posterior surgery. The mean (SD) follow-up was 23.2 (5.6, range 6.1-32.8) months. The best performing model was for the prediction of the NASS satisfaction index, with an accuracy [95% CI] of 94.4 [94.3, 94.8]. Conversely, the worst performing model was for the NDI, with an accuracy of 73.8 [73.6, 74.5]. When predicting improvement in the mJOA, the clinically-driven model had an accuracy of 61.9 [61.6, 62.5], compared to 78.6 [78.4, 79.2] in the DBSI model, and 90.5 [90.2, 90.8] in the combined model. CONCLUSIONS: When combined with key clinical covariates, DBSI metrics predicted improvement after surgical decompression with high accuracy. These results suggest that DBSI may serve as a non-invasive imaging biomarker for CSM.

Reliable quantification of citrate isomers and isobars with direct-infusion tandem mass spectrometry

Direct-infusion tandem mass spectrometry (DI-MS/MS) is an excellent tool for large cohort high-throughput quantitative metabolomics, MS imaging and single cell studies but incapable of discriminating isomers/isobars with similar MS spectral features. With experimental and density-functional theory (DFT) approaches, here, we comprehensively investigated the fragmentation pathways and characteristics of differential ion-mobility spectrometry (DMS) for three citrate isomers (citrate, isocitrate, glucaro-1,4-lactone) and an isobar (quinate) co-existing in biological sample such as urine. Results showed that all these compounds gave better MS spectra in negative-ion mode than positive-ion one and had numerous fragment ions under collision-induced dissociation (CID) with sequential losses of H2O and CO2. All observed fragment ions were assignable by combining experimental with DFT calculation results. A DI-DMS-MS/MS method was then developed to simultaneously quantify these four isomers/isobars with m/z 191–87 (CoV, −5.5 V), 191–73 (CoV, −3.5 V), 191–85 (CoV, −29.5 V) and m/z 191–93 (CoV, −41.5 V) for citrate, isocitrate, glucaro-1,4-lactone and quinate, respectively. The low limit-of-quantification was below 5.5 nM whilst accuracy was above 94% for all above compounds. The urinary concentrations of them in human and C57BL/6 mouse samples were further quantified showing clear inter-individual and inter-species level differences with significantly higher levels of isocitrate, glucaro-1,4-lactone and quinate in human urine samples than mouse ones. This provides an approach to understand the detailed fragmentation pathways for organic isomers/isobars and a high-throughput MS strategy to quantify them in complex mixtures for metabolomics, lipidomics, foodomics and exposomics especially when chromatographic separations are not useable.

Diffusion Basis Spectrum Imaging Identifies Clinically Relevant Disease Phenotypes of Cervical Spondylotic Myelopathy.

Prospective cohort study. Apply a machine learning clustering algorithm to baseline imaging data to identify clinically relevant cervical spondylotic myelopathy (CSM) patient phenotypes. A major shortcoming in improving care for CSM patients is the lack of robust quantitative imaging tools to guide surgical decision-making. Advanced diffusion-weighted magnetic resonance imaging (MRI) techniques, such as diffusion basis spectrum imaging (DBSI), may help address this limitation by providing detailed evaluations of white matter injury in CSM. Fifty CSM patients underwent comprehensive clinical assessments and diffusion-weighted MRI, followed by DBSI modeling. DBSI metrics included fractional anisotropy, axial and radial diffusivity, fiber fraction, extra-axonal fraction, restricted fraction, and nonrestricted fraction. Neurofunctional status was assessed by the modified Japanese Orthopedic Association, myelopathic disability index, and disabilities of the arm, shoulder, and hand. Quality-of-life was measured by the 36-Item Short Form Survey physical component summary and mental component summary. The neck disability index was used to measure self-reported neck pain. K-means clustering was applied to baseline DBSI measures to identify 3 clinically relevant CSM disease phenotypes. Baseline demographic, clinical, radiographic, and patient-reported outcome measures were compared among clusters using one-way analysis of variance (ANOVA). Twenty-three (55%) mild, 9 (21%) moderate, and 10 (24%) severe myelopathy patients were enrolled. Eight patients were excluded due to MRI data of insufficient quality. Of the remaining 42 patients, 3 groups were generated by k-means clustering. When compared with clusters 1 and 2, cluster 3 performed significantly worse on the modified Japanese Orthopedic Association and all patient-reported outcome measures (P<0.001), except the 36-Item Short Form Survey mental component summary (P>0.05). Cluster 3 also possessed the highest proportion of non-Caucasian patients (43%, P=0.04), the worst hand dynamometer measurements (P<0.05), and significantly higher intra-axonal axial diffusivity and extra-axonal fraction values (P<0.001). Using baseline imaging data, we delineated a clinically meaningful CSM disease phenotype, characterized by worse neurofunctional status, quality-of-life, and pain, and more severe imaging markers of vasogenic edema. II.

Predictive modeling of signal transduction anchored by quantitative imaging.

Signal transduction is challenging to investigate because it depends upon both spatial organization of signaling and receptor molecules and alterations of gene expression spanning multiple time and space scales. We argue that mathematical modeling provides the tools to build upon the results from quantitative imaging to analyze and interpret cell signaling across these scales. Quantitative imaging tools such as fluorescence fluctuation spectroscopy methods provide many of the key parameters needed for predictive modeling, including measurements of: protein mobility, oligomerization, and concentrations, as functions of time and space. Comprehensive predictive models can integrate such multi-dimensional and multi-scale data into frameworks that can be used to expand our understanding of biological signaling networks.

Diffusion basis spectrum imaging predicts long-term clinical outcomes following surgery in cervical spondylotic myelopathy

BACKGROUND CONTEXTA major shortcoming in improving care for cervical spondylotic myelopathy (CSM) patients is the lack of robust quantitative imaging tools to guide surgical decision-making. Diffusion basis spectrum imaging (DBSI), an advanced diffusion-weighted MRI technique, provides objective assessments of white matter tract integrity that may help prognosticate outcomes in patients undergoing surgery for CSM. PURPOSETo examine the ability of DBSI to predict clinically important CSM outcome measures at 2-years follow-up. STUDY DESIGN/SETTINGProspective cohort study. PATIENT SAMPLEPatients undergoing decompressive cervical surgery for CSM. OUTCOME MEASURESNeurofunctional status was assessed by the mJOA, MDI, and DASH. Quality-of-life was measured by the SF-36 PCS and SF-36 MCS. The NDI evaluated self-reported neck pain, and patient satisfaction was assessed by the NASS satisfaction index. METHODSFifty CSM patients who underwent cervical decompressive surgery were enrolled. Preoperative DBSI metrics assessed white matter tract integrity through fractional anisotropy, fiber fraction, axial diffusivity, and radial diffusivity. To evaluate extra-axonal diffusion, DBSI measures restricted and nonrestricted fractions. Patient-reported outcome measures were evaluated preoperatively and up to 2-years follow-up. Support vector machine classification algorithms were used to predict surgical outcomes at 2-years follow-up. Specifically, three feature sets were built for each of the seven clinical outcome measures (eg, mJOA), including clinical only, DBSI only, and combined feature sets. RESULTSTwenty-seven mild (mJOA 15–17), 12 moderate (12–14) and 11 severe (0–11) CSM patients were enrolled. Twenty-four (60%) patients underwent anterior decompressive surgery compared with 16 (40%) posterior approaches. The mean (SD) follow-up was 23.2 (5.6, range 6.1–32.8) months. Feature sets built on combined data (ie, clinical+DBSI metrics) performed significantly better for all outcome measures compared with those only including clinical or DBSI data. When predicting improvement in the mJOA, the clinically driven feature set had an accuracy of 61.9 [61.6, 62.5], compared with 78.6 [78.4, 79.2] in the DBSI feature set, and 90.5 [90.2, 90.8] in the combined feature set. CONCLUSIONSWhen combined with key clinical covariates, preoperative DBSI metrics predicted improvement after surgical decompression for CSM with high accuracy for multiple outcome measures. These results suggest that DBSI may serve as a noninvasive imaging biomarker for CSM valuable in guiding patient selection and informing preoperative counseling. LEVEL OF EVIDENCEII

Left ventricular non-compaction in paediatrics: a novel semi-automated imaging technique bridging imaging findings and clinical outcomes.

We set out to design a reliable, semi-automated, and quantitative imaging tool using cardiac magnetic resonance (CMR) imaging that captures LV trabeculations in relation to the morphologic endocardial and epicardial surface, or perimeter-derived ratios, and assess its diagnostic and prognostic utility. We queried our institutional database between January 2008 and December 2018. Non-compacted (NC)-to-compacted (C) (NC/C) myocardium ratios were calculated and our tool was used to calculate fractal dimension (FD), total mass ratio (TMR), and composite surface ratios (SRcomp). NC/C, FD, TMR, and SRcomp were assessed in relation to LVNC diagnosis and outcomes. Univariate hazard ratios with cut-offs were performed using clinically significant variables to find 'at-risk' patients and imaging parameters were compared in 'at-risk' patients missed by Petersen Index (PI). Ninety-six patients were included. The average time to complete the semi-automated measurements was 3.90 min (SEM: 0.06). TMR, SRcomp, and NC/C were negatively correlated with LV ejection fraction (LVEF) and positively correlated with indexed LV end-systolic volumes (iLVESVs), with TMR showing the strongest correlation with LVEF (-0.287; P = 0.005) and SRcomp with iLVESV (0.260; P = 0.011). We found 29 'at-risk' patients who were classified as non-LVNC by PI and hence, were missed. When compared with non-LVNC and 'low-risk' patients, only SRcomp differentiated between both groups (1.91 SEM 0.03 vs. 1.80 SEM 0.03; P = 0.019). This method of semi-automatic calculation of SRcomp captured changes in at-risk patients missed by standard methods, was strongly correlated with LVEF and LV systolic volumes and may better capture outcome events.

Quantitative imaging of magnetic nanoparticles in an unshielded environment using a large AC susceptibility array

BackgroundNon-invasive magnetic imaging techniques are necessary to assist magnetic nanoparticles in biomedical applications, mainly detecting their distribution inside the body. In Alternating Current Biosusceptometry (ACB), the magnetic nanoparticle's magnetization response under an oscillating magnetic field, which is applied through an excitation coil, is detected with a balanced detection coil system.ResultsWe built a Multi-Channel ACB system (MC-ACB) containing nineteen pick-up coils and obtained 2D quantitative images of magnetic nanoparticle distributions by solving an inverse problem. We reconstructed the magnetic nanoparticles spatial distributions in a field of view of 14 × 14 cm2 with a spatial resolution of 2.0 cm and sensitivity in the milligram scale. A correlation coefficient between quantitative reconstructed and nominal magnetic nanoparticle distributions above 0.6 was found for all measurements.ConclusionBesides other interesting features such as sufficient large field of view dimension for mice and rat studies, portability, and the ability to assess the quantitative magnetic nanoparticles distributions in real-time, the MC-ACB system is a promising tool for quantitative imaging of magnetic nanoparticles distributions in real-time, offering an affordable setup for easy access in clinical or laboratory environments.

Contrast-enhanced CT texture analysis for the prediction of delayed graft function following kidney transplantation from cadaveric donors.

Delayed graft function (DGF) is a common complication after transplantation of deceased donor kidneys. The aim of this study was to investigate the feasibility of using computed tomography texture analysis (CT-TA) of the donor kidney to predict delayed graft function (DGF) following kidney transplantation from cadaveric donors. We made a retrospective review of all consecutive DBD and DCD kidney donors admitted to our institution and their corresponding KTRs between December 2014 and January 2019. We extracted 15 image features from unenhanced CT and contrast-enhanced CT corresponding to first order and second order Haralick textural features. Predictors of DGF were evaluated by univariable and multivariable analysis. Receiver operating characteristic (ROC) analysis was performed and the area under the ROC curve (AUC) to predict DGF was calculated for the predictors. A total of 115 patients were included in the study. DGF occurred in 15 patients (13%). Recipient body mass index (BMI) (P=0.003) and Skewness (P=0.05) represented independent predictors in the multivariate model. The combination of both clinical and textural features in a bivariate model reached a ROC-AUC of 0.79 (95% CI: 0.64-0.94) in predicting the probability of DGF. Results from this preliminary study suggest that CT texture analysis might be a promising quantitative imaging tool to help physician predict DFG after kidney transplantation from cadaveric donors. 4/5.