The interaction effects of age, APOE and common environmental risk factors on human brain structure.

Mounting evidence suggests considerable diversity in brain aging trajectories, primarily arising from the complex interplay between age, genetic, and environmental risk factors, leading to distinct patterns of micro- and macro-cerebral aging. The underlying mechanisms of such effects still remain unclear. We conducted a comprehensive association analysis between cerebral structural measures and prevalent risk factors, using data from 36,969 UK Biobank subjects aged 44-81. Participants were assessed for brain volume, white matter diffusivity, Apolipoprotein E (APOE) genotypes, polygenic risk scores, lifestyles, and socioeconomic status. We examined genetic and environmental effects and their interactions with age and sex, and identified 726 signals, with education, alcohol, and smoking affecting most brain regions. Our analysis revealed negative age-APOE-ε4 and positive age-APOE-ε2 interaction effects, respectively, especially in females on the volume of amygdala, positive age-sex-APOE-ε4 interaction on the cerebellar volume, positive age-excessive-alcohol interaction effect on the mean diffusivity of the splenium of the corpus callosum, positive age-healthy-diet interaction effect on the paracentral volume, and negative APOE-ε4-moderate-alcohol interaction effects on the axial diffusivity of the superior fronto-occipital fasciculus. These findings highlight the need of considering age, sex, genetic, and environmental joint effects in elucidating normal or abnormal brain aging.

SIPAS: A comprehensive susceptibility imaging process and analysis studio.

Quantitative susceptibility mapping (QSM) is a rising MRI-based technology and quite a few QSM-related algorithms have been proposed to reconstruct maps of tissue susceptibility distribution from phase images. In this paper, we develop a comprehensive susceptibility imaging process and analysis studio (SIPAS) that can accomplish reliable QSM processing and offer a standardized evaluation system. Specifically, SIPAS integrates multiple methods for each step, enabling users to select algorithm combinations according to data conditions, and QSM maps could be evaluated by two aspects, including image quality indicators within all voxels and region-of-interest (ROI) analysis. Through a sophisticated design of user-friendly interfaces, the results of each procedure are able to be exhibited in axial, coronal, and sagittal views in real-time, meanwhile ROIs can be displayed in 3D rendering visualization. The accuracy and compatibility of SIPAS are demonstrated by experiments on multiple in vivo human brain datasets acquired from 3T, 5T, and 7T MRI scanners of different manufacturers. We also validate the QSM maps obtained by various algorithm combinations in SIPAS, among which the combination of iRSHARP and SFCR achieves the best results on its evaluation system. SIPAS is a comprehensive, sophisticated, and reliable toolkit that may prompt the QSM application in scientific research and clinical practice.

Structural changes in eloquent cortex secondary to glioma in sensorimotor area.

This study aims to investigate the structural reorganization in the sensorimotor area of the brain in patients with gliomas, distinguishing between those with impaired and unimpaired strength. Using voxel-based morphometry (VBM) and region of interest (ROI) analysis, gray matter volumes (GMV) were compared in the contralesional primary motor gyrus, primary sensory gyrus, premotor area, bilateral supplementary motor area, and medial Brodmann area 8 (BA8). The results revealed that in patients with right hemisphere gliomas, the right medial BA8 volume was significantly larger in the impaired group than in the unimpaired group, with both groups exceeding the volume in 16 healthy controls (HCs). In patients with left hemisphere gliomas, the right supplementary motor area (SMA) was more pronounced in the impaired group compared to the unimpaired group, and both groups were greater than HCs. Additionally, the volumes of the right medial BA8 in both the impaired group were greater than HCs. Contralateral expansions in the gray matter of hand- and trunk-related cortices of the premotor area, precentral gyrus, and postcentral gyrus were observed compared to HCs. Furthermore, a negative correlation was found between hand Medical Research Council (MRC) score and volumes of the contralateral SMA and bilateral medial BA8. Notably, our findings reveal consistent results across both analytical approaches in identifying significant structural reorganizations within the sensorimotor cortex. These consistent findings underscore the adaptive neuroplastic responses to glioma presence, highlighting potential areas of interest for further neurosurgical planning and rehabilitation strategies.

Deep Learning Model Based on Multisequence MRI Images for Assessing Adverse Pregnancy Outcome in Placenta Accreta.

BACKGROUND: Preoperative assessment of adverse outcomes risk in placenta accreta spectrum (PAS) disorders is of high clinical relevance for perioperative management and prognosis. PURPOSE: To investigate the association of preoperative MRI multisequence images and adverse pregnancy outcomes by establishing a deep learning model in patients with PAS. STUDY TYPE: Retrospective. POPULATION: 323 pregnant women (age from 20 to 46, the median age is 33), suspected of PAS, underwent MRI to assess the PAS, divided into the training (N = 227) and validation datasets (N = 96). FIELD STRENGTH/SEQUENCE: 1.5T scanner/fast imaging employing steady-state acquisition sequence and single shot fast spin echo sequence. ASSESSMENT: Different deep learning models (i.e., with single MRI input sequence/two sequences/multisequence) were compared to assess the risk of adverse pregnancy outcomes, which defined as intraoperative bleeding ≥1500 mL and/or hysterectomy. Net reclassification improvement (NRI) was used for quantitative comparison of assessing adverse pregnancy outcome between different models. STATISTICAL TESTS: The AUC, sensitivity, specificity, and accuracy were used for evaluation. The Shapiro-Wilk test and t-test were used. A P value of <0.05 was considered statistically significant. RESULTS: 215 cases were invasive placenta accreta (67.44% of them with adverse outcomes) and 108 cases were non-invasive placenta accreta (9.25% of them with adverse outcomes). The model with four sequences assessed adverse pregnancy outcomes with AUC of 0.8792 (95% CI, 0.8645-0.8939), with ACC of 85.93% (95%, 84.43%-87.43%), with SEN of 86.24% (95% CI, 82.46%-90.02%), and with SPC of 85.62% (95%, 82.00%-89.23%) on the test cohort. The performance of model with four sequences improved above 0.10 comparing with that of model with two sequences and above 0.20 comparing with that of model with single sequence in terms of NRI. DATA CONCLUSION: The proposed model showed good diagnostic performance for assessing adverse pregnancy outcomes. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Return on investment in science: twenty years of European Commission funded research in Alzheimer's dementia, breast cancer and prostate cancer.

Alzheimer's disease (AD), breast cancer (BC) and prostate cancer (PC) continue to be high in the research and innovation agenda of the European Commission (EC). This is due to their exceptionally large burden to the national health systems, the profound economic effects of opportunity costs attributable to decreased working ability, premature mortality and the ever-increasing demand for both hospital and home-based medical care. Over the last two decades, the EC has been steadily increasing both the number of proposals being funded and the amounts of financial resources being allocated to these fields of research. This trend has continued throughout four consecutive science funding cycles, namely framework programme (FP)5, FP6, FP7 and Horizon 2020 (H2020). We performed a retrospective assessment of the outputs and outcomes of EC funding in AD, BC and PC research over the 1999-2019 period by means of selected indicators. These indicators were assessed for their ability to screen the past, present and future for an array of causal relationships and long-term trends in clinical, epidemiological and public health sphere, while considering also the broader socioeconomic impact of funded research on the society at large. This analysis shows that public-private partnerships with large industry and university-based consortia have led to some of the most impactful proposals being funded over the analysed time period. New pharmaceuticals, small molecules and monoclonal antibodies alike, along with screening and prevention, have been the most prominent sources of innovation in BC and PC, extending patients' survival and enhancing their quality of life. Unlike oncology, dementia drug development has been way less successful, with only minor improvements related to the quality of supportive medical care for symptoms and more sensitive diagnostics, without any ground-breaking disease-modifying treatment(s). Significant progresses in imaging diagnostics and nanotechnology have been largely driven by the participation of medical device industry multinational companies. Clinical trials funded by the EC were conducted, leading to the development of brand-new drug molecules featuring novel mechanisms of action. Some prominent cases of breakthrough discoveries serve as evidence for the European capability to generate cutting-edge technological innovation in biomedicine. Less productive areas of research may be reconsidered as priorities when shaping the new agenda for forthcoming science funding programmes.

Network dysfunction of sadness facial expression processing and morphometry in euthymic bipolar disorder.

Facial emotion recognition (FER), including sadness, is altered in bipolar disorder (BD). However, the relationship between this impairment and the brain structure in BD is relatively unexplored. Furthermore, its association with clinical variables and with the subtypes of BD remains to be clarified. Twenty euthymic patients with BD type I (BD-I), 28 BD type II (BD-II), and 45 healthy controls completed a FER test and a 3D-T1-weighted magnetic resonance imaging. Gray matter volume (GMV) of the cortico-limbic regions implicated in emotional processing was estimated and their relationship with FER performance was investigated using network analysis. Patients with BD-I had worse total and sadness-related FER performance relative to the other groups. Total FER performance was significantly negatively associated with illness duration and positively associated with global functioning in patients with BD-I. Sadness-related FER performance was also significantly negatively associated with the number of previous manic episodes. Network analysis showed a reduced association of the GMV of the frontal-insular-occipital areas in patients with BD-I, with a greater edge strength between sadness-related FER performance and amygdala GMV relative to controls. Our results suggest that FER performance, particularly for facial sadness, may be distinctively impaired in patients with BD-I. The pattern of reduced interrelationship in the frontal-insular-occipital regions and a stronger positive relationship between facial sadness recognition and the amygdala GMV in BD may reflect altered cortical modulation of limbic structures that ultimately predisposes to emotional dysregulation. Future longitudinal studies investigating the effect of mood state on FER performance in BD are warranted.

Commentary: Value of information case study strongly supports use of the Threshold of Toxicological Concern (TTC).

A Value of Information (VOI) analysis can play a key role in decision-making for adopting new approach methodologies (NAMs). We applied EPA's recently developed VOI framework to the Threshold of Toxicological Concern (TTC). Obtaining/deriving a TTC value for use as a toxicity reference value (TRV) for substances with limited toxicity data was shown to provide equivalent or greater health protection, immense return on investment (ROI), greater net benefit, and substantially lower costs of delay (CoD) compared with TRVs derived from either traditional human health assessment (THHA) chronic toxicity testing in lab animals or the 5-day in vivo EPA Transcriptomic Assessment Product (ETAP). For all nine exposure scenarios examined, the TTC was more economical terms of CoD and ROI than the ETAP or the THHA; expected net benefit was similar for the TTC and ETAP with both of these more economical than the THHA The TTC ROI was immensely greater (5,000,000-fold on average) than the ROI for THHA and the ETAP ROI (100,000-fold on average). These results support the use of the TTC for substances within its domain of applicability to waive requiring certain in vivo tests, or at a minimum, as an initial screening step before conducting either the ETAP or THHA in vivo studies.

Lossless compression-based detection of osteoporosis using bone X-ray imaging.

BACKGROUND: Digital X-ray imaging is essential for diagnosing osteoporosis, but distinguishing affected patients from healthy individuals using these images remains challenging. OBJECTIVE: This study introduces a novel method using deep learning to improve osteoporosis diagnosis from bone X-ray images. METHODS: A dataset of bone X-ray images was analyzed using a newly proposed procedure. This procedure involves segregating the images into regions of interest (ROI) and non-ROI, thereby reducing data redundancy. The images were then processed to enhance both spatial and statistical features. For classification, a Support Vector Machine (SVM) classifier was employed to distinguish between osteoporotic and non-osteoporotic cases. RESULTS: The proposed method demonstrated a promising Area under the Curve (AUC) of 90.8% in diagnosing osteoporosis, benchmarking favorably against existing techniques. This signifies a high level of accuracy in distinguishing osteoporosis patients from healthy controls. CONCLUSIONS: The proposed method effectively distinguishes between osteoporotic and non-osteoporotic cases using bone X-ray images. By enhancing image features and employing SVM classification, the technique offers a promising tool for efficient and accurate osteoporosis diagnosis.

A methodology for projecting the return on investment of training technologies.

Each year significant tax dollars are spent on the development of new technologies to increase efficiency and/or reduce costs of military training. However, there are currently no validated methods or measures to quantify the return on investment for adopting these new technologies for military training. Estimating the return on investment (ROI) for training technology adoption involves 1) developing a methodology or framework, 2) validating measures and methods, and 3) assessing predictive validity. The current paper describes a projective methodology using the Kirkpatrick framework to compare projected tangible and intangible benefits against tangible and intangible costs to estimate future ROI. The use-case involved an advanced technology demonstration in which sixty aircrew participated in a series of live, virtual, and constructive (LVC) exercises over a five-week period. Participants evaluated the technology's potential costs and benefits according to the Kirkpatrick framework of training program evaluation, and analyses resulted in a nominal projection of $488 million dollars saved, significant enhancements in large-force proficiency, and 1.4 lives saved over ten years at an implementation rate of 0.5% of budgeted flight hours. A discussion of theoretical implications, data-based limitations, and recommendations for future research are provided.

Novel algorithms for improved detection and analysis of fluorescent signal fluctuations.

Fluorescent dyes and genetically encoded fluorescence indicators (GEFI) are common tools for visualizing concentration changes of specific ions and messenger molecules during intra- as well as intercellular communication. Using advanced imaging technologies, fluorescence indicators are a prerequisite for the analysis of physiological molecular signaling. Automated detection and analysis of fluorescence signals require to overcome several challenges, including correct estimation of fluorescence fluctuations at basal concentrations of messenger molecules, detection, and extraction of events themselves as well as proper segmentation of neighboring events. Moreover, event detection algorithms need to be sensitive enough to accurately capture localized and low amplitude events exhibiting a limited spatial extent. Here, we present two algorithms (PBasE and CoRoDe) for accurate baseline estimation and automated detection and segmentation of fluorescence fluctuations.

Outcome measures for electric field modeling in tES and TMS: A systematic review and large-scale modeling study.

BACKGROUND: Electric field (E-field) modeling is a potent tool to estimate the amount of transcranial magnetic and electrical stimulation (TMS and tES, respectively) that reaches the cortex and to address the variable behavioral effects observed in the field. However, outcome measures used to quantify E-fields vary considerably and a thorough comparison is missing. OBJECTIVES: This two-part study aimed to examine the different outcome measures used to report on tES and TMS induced E-fields, including volume- and surface-level gray matter, region of interest (ROI), whole brain, geometrical, structural, and percentile-based approaches. The study aimed to guide future research in informed selection of appropriate outcome measures. METHODS: Three electronic databases were searched for tES and/or TMS studies quantifying E-fields. The identified outcome measures were compared across volume- and surface-level E-field data in ten tES and TMS modalities targeting two common targets in 100 healthy individuals. RESULTS: In the systematic review, we extracted 308 outcome measures from 202 studies that adopted either a gray matter volume-level (n = 197) or surface-level (n = 111) approach. Volume-level results focused on E-field magnitude, while surface-level data encompassed E-field magnitude (n = 64) and normal/tangential E-field components (n = 47). E-fields were extracted in ROIs, such as brain structures and shapes (spheres, hexahedra and cylinders), or the whole brain. Percentiles or mean values were mostly used to quantify E-fields. Our modeling study, which involved 1,000 E-field models and > 1,000,000 extracted E-field values, revealed that different outcome measures yielded distinct E-field values, analyzed different brain regions, and did not always exhibit strong correlations in the same within-subject E-field model. CONCLUSIONS: Outcome measure selection significantly impacts the locations and intensities of extracted E-field data in both tES and TMS E-field models. The suitability of different outcome measures depends on the target region, TMS/tES modality, individual anatomy, the analyzed E-field component and the research question. To enhance the quality, rigor, and reproducibility in the E-field modeling domain, we suggest standard reporting practices across studies and provide four recommendations.

Economic Evaluation of Wastewater Surveillance Combined with Clinical COVID-19 Screening Tests, Japan.

The COVID-19 pandemic has imposed substantial burdens on the global society. To find an optimal combination of wastewater surveillance and clinical testing for tracking COVID-19, we evaluated the economic efficiency of hypothetical screening options at a single facility in Japan. To conduct cost-benefit analyses, we developed standard decision models in which we assumed model parameters from literature and primary data, such as screening policies used at the Tokyo Olympic and Paralympic Village in 2021. We compared hypothetical 2-step screening options that used clinical PCR to diagnose COVID-19 after a positive result from primary screening using antigen tests (option 1) or wastewater surveillance (option 2). Our simulation results indicated that option 2 likely would be economically more justifiable than option 1, particularly at lower incidence levels. Our findings could help justify and promote the use of wastewater surveillance as a primary screening at a facility level for COVID-19 and other infectious diseases.

Rustrela Virus as Putative Cause of Nonsuppurative Meningoencephalitis in Lions.

Retrospective investigation of archived tissue samples from 3 lions displaying nonsuppurative meningoencephalitis and vasculitis led to the detection of rustrela virus (RusV). We confirmed RusV antigen and RNA in cortical neurons, axons, astrocytes and Purkinje cells by reverse transcription quantitative PCR, immunohistochemistry, and in situ hybridization.

Neural suppression in odor recognition memory.

Little is known about the neural basis of lower- and higher-order olfactory functions such as odor memory, compared with other sensory systems. The aim of this study was to explore neural networks and correlates associated with 3 functions: passive smelling (PS), odor encoding (OE), and in particular odor recognition memory (ORM). Twenty-six healthy participants were examined using functional magnetic resonance imaging conducted across 3 sessions, one for each function. Independent component analysis revealed a difference between sessions where a distinct ORM component incorporating hippocampus and posterior cingulate showed delayed triggering dissociated from odor stimulation and recognition. By contrasting Hit for ORM (target odors correctly recognized as old) and a combination of PS and detected odors from OE, we found significantly lower activations in amygdala, piriform cortex, insula, thalamus, and the inferior parietal lobule. Region of interest analysis including anterior insula, posterior cingulate gyrus, dentate gyrus, left middle frontal gyrus, amygdala, and piriform cortex demonstrated that Hit were associated with lower activations compared with other memory responses. In summary, our findings suggest that successful recognition of familiar odors (odor familiarity) is associated with neural suppression in the abovementioned regions of interest. Additionally, network including the hippocampus and posterior cingulate is engaged in a postrecognition process. This process may be related to incidental encoding of less familiar and more novel odors (odor novelty) and should be subject for future research.

Minimal resin embedding of SBF-SEM samples reduces charging and facilitates finding a surface-linked region of interest.

BACKGROUND: For decoding the mechanism of how cells and organs function information on their ultrastructure is essential. High-resolution 3D imaging has revolutionized morphology. Serial block face scanning electron microscopy (SBF-SEM) offers non-laborious, automated imaging in 3D of up to ~ 1 mm3 large biological objects at nanometer-scale resolution. For many samples there are obstacles. Quality imaging is often hampered by charging effects, which originate in the nonconductive resin used for embedding. Especially, if the imaged region of interest (ROI) includes the surface of the sample and neighbours the empty resin, which insulates the object. This extra resin also obscures the sample's morphology, thus making navigation to the ROI difficult. RESULTS: Using the example of small arthropods and a fish roe we describe a workflow to prepare samples for SBF-SEM using the minimal resin (MR) embedding method. We show that for imaging of surface structures this simple approach conveniently tackles and solves both of the two major problems-charging and ROI localization-that complicate imaging of SBF-SEM samples embedded in an excess of overlying resin. As the surface ROI is not masked by the resin, samples can be precisely trimmed before they are placed into the imaging chamber. The initial approaching step is fast and easy. No extra trimming inside the microscope is necessary. Importantly, charging is absent or greatly reduced meaning that imaging can be accomplished under good vacuum conditions, typically at the optimal high vacuum. This leads to better resolution, better signal to noise ratio, and faster image acquisition. CONCLUSIONS: In MR embedded samples charging is minimized and ROI easily targeted. MR embedding does not require any special equipment or skills. It saves effort, microscope time and eventually leads to high quality data. Studies on surface-linked ROIs, or any samples normally surrounded by the excess of resin, would benefit from adopting the technique.

Automated piezoresponse force microscopy domain tracking during fast thermally stimulated phase transition in CuInP2S6.

Real-time tracking of dynamic nanoscale processes such as phase transitions by scanning probe microscopy is a challenging task, typically requiring extensive and laborious human supervision. Smart strategies to track specific regions of interest (ROI) in the system during such transformations in a fast and automated manner are necessary to study the evolution of the microscopic changes in such dynamic systems. In this work, we realize automated ROI tracking in piezoresponse force microscopy during a fast (≈0.8°C s-1) thermally stimulated ferroelectric-to-paraelectric phase transition in CuInP2S6. We use a combination of fast (1 frame per second) sparse scanning with compressed sensing image reconstruction and real-time offset correction via phase cross correlation. The applied methodology enablesin situfast and automated functional nanoscale characterization of a certain ROI during external stimulation that generates sample drift and changes local functionality.

Pulmonary fibrosis model of mice induced by different administration methods of bleomycin.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive disease of the lung. How to build a typical human mimicking animal model has been a challenge. Thus, to reveal the mechanism and to make it useful for IPF clinical treatment, a different type of mice model and inspection methods are used to evaluate which one is applicable for the study of IPF. METHOD: 69 Twelve-weeks-old C57BL/6 mice were divided into 3 type groups (n = 7 for each control group, n = 8 for each BLM-induced pulmonary fibrosis groups), as intraperitoneal injection, intratracheal administration, and intravenous administration of bleomycin (BLM) to initiate lung fibrosis. Changes of the lung function measured through mice Pulmonary function test (PFT). Morphological changes in mice were observed by PET/CT, Masson and Picro-Sirius staining, Transmission electron microscopy (TEM). Biochemical changes were tested by Enzyme-linked immunosorbent assay (Elisa). RESULTS: PET/CT of BLM-receiving mice showed an increase in fibrotic consolidations and an increase in non-aerated lung area in BLM-treated mice compared with that in controls. TGF-b1, TNF-a, IL-6, GM-CSF in BALF and serum. PAI-1, HYP in the lung tissue of mice were significantly different in each BLM groups than those in the controls. The results of Masson staining in mice indicate that the lung tissues of all BLM received groups, the intratracheal groups, the intravenous groups, and the intraperitoneal groups have a higher degree of pulmonary septal thickening and collagen fiber consolidation compare to saline control. Picro-Sirius staining results are consistent with the results of Masson staining. Compared with the saline control group, the ratio of Col 1/Col 3 was significantly increased in each BLM group. TEM results found that in BLM group, type I alveolar epithelial cells were degenerated. Exfoliated endothelial cells were swelling, and type II alveolar epithelial cells were proliferated, the shape of the nucleus was irregular, and some tooth-like protrusions were seen. CONCLUSIONS: With three different methods of animal model construction, high dose of each show more compliable, and BLM can successfully induce animal models of pulmonary fibrosis, however, certain differences in the fibrosis formation sites of them three, and tail vein injection of BLM induced PF model is closer to the idiopathic pulmonary interstitial fibrosis.

How is return on investment from quality improvement programmes conceptualised by mental healthcare leaders and why: a qualitative study.

BACKGROUND: Return on Investment (ROI), whereby the ratio of costs to benefits is assessed, is encouraged in-order to justify the value of Quality Improvement (QI) programmes. We previously performed a literature review to develop a ROI conceptual framework for QI programmes. We concluded that, QI-ROI is conceptualised as any monetary and non-monetary benefit. In the current study, we explored if this finding is shared by mental healthcare leaders. We also investigated the stability of this conceptualisation against influencing factors and potential for disinvestment. METHODS: We performed qualitative interviews with leaders in an NHS mental health organisation. There were 16 participants: nine board members and seven senior leaders. The interviews were held online via Microsoft Teams and lasted an hour on average. We performed deductive-inductive analysis to seek data from our initial ROI framework and any new data. RESULTS: We found that in mental healthcare, QI-ROI is also conceptualised as any valued monetary and non-monetary benefits. There was a strong emphasis on benefits to external partners and a de-emphasis of benefit monetisation. This conceptualisation was influenced by the 1) perceived mandates to improve quality and manage scarce resources, 2) expectations from QI, 3) health and social care values, 4) ambiguity over expectations, and 5) uncertainty over outcomes. Uncertainty, ambiguity, and potential for disinvestment posed a threat to the stability of this conceptualisation but did not ultimately change it. Health and social care values supported maintaining the QI-ROI as any benefit, with a focus on patients and staff outcomes. Socio-political desires to improve quality were strong drivers for QI investment. CONCLUSION: Mental healthcare leaders primarily conceptualise QI-ROI as any valued benefit. The inclusion of externalised outcomes which are hard to attribute may be challenging. However, mental healthcare services do collaborate with external partners. The de-emphases of benefit monetisation may also be controversial due to the need for financial accountability. Mental healthcare leaders recognise the importance of efficiency savings. However, they raised concerns over the legitimacy and utility of traditional ROI as a tool for assessing QI value. Further research is needed to bring more clarity on these aspects of the QI-ROI concept.

Liver Fat Quantification With Ultrasound: Depth Dependence of Attenuation Coefficient.

OBJECTIVES: The primary aim was to estimate the influence of various depths on ultrasound attenuation coefficient (AC) of multiple vendors in the liver. The secondary aim was to evaluate the impact of region of interest (ROI) size on AC measurements in a subset of participants. METHODS: This Institutional Review Board (IRB)-approved Health Insurance Portability and Accountability Act (HIPAA)-compliant retrospective study was carried out in two centers using AC-Canon and AC-Philips algorithms and extracting AC-Siemens values from ultrasound-derived fat fraction algorithm. Measurements were performed positioning ROI upper edge (3 cm size) at 2, 3, 4, 5 cm from the liver capsule with AC-Canon and AC-Philips and at 1.5, 2, 3 cm with Siemens algorithm. In a subset of participants, measurements were obtained with 1 and 3 cm ROI size. Univariate and multivariate linear regression models and Lin's concordance correlation coefficient (CCC) were used for statistical analysis as appropriate. RESULTS: Three different cohorts were studied. Sixty-three participants (34 females; mean age: 51 ± 14 years) were studied with AC-Canon, 60 (46 females; mean age: 57 ± 11 years) with AC-Philips, and 50 (25 females; 61 ± 13 years) with AC-Siemens. There was a decrease in AC values per 1 cm increase in depth in all. In multivariable analysis, the coefficient was -0.049 (-0.060; -0.038 P < .001) with AC-Canon, -0.058 (-0.066; -0.049 P < .001) with AC-Philips and -0.081 (-0.112; -0.050 P < .001) with AC-Siemens. AC values with 1 cm ROI were significantly higher than those obtained with 3 cm ROI at all depths (P < .001) but the agreement between AC values obtained with different ROI size was excellent (CCC 0.82 [0.77-0.88]). CONCLUSIONS: There is depth dependence in AC measurement that affects results. A standardized protocol with fixed ROI's depth and size is needed.

A Multiscale Attentional Unet Model for Automatic Segmentation in Medical Ultrasound Images.

Ultrasonography has become an essential part of clinical diagnosis owing to its noninvasive, and real-time nature. To assist diagnosis, automatically segmenting a region of interest (ROI) in ultrasound images is becoming a vital part of computer-aided diagnosis (CAD). However, segmenting ROIs on medical images with relatively low contrast is a challenging task. To better achieve medical ROI segmentation, we propose an efficient module denoted as multiscale attentional convolution (MSAC), utilizing cascaded convolutions and a self-attention approach to concatenate features from various receptive field scales. Then, MSAC-Unet is constructed based on Unet, employing MSAC instead of the standard convolution in each encoder and decoder for segmentation. In this study, two representative types of ultrasound images, one of the thyroid nodules and the other of the brachial plexus nerves, were used to assess the effectiveness of the proposed approach. The best segmentation results from MSAC-Unet were achieved on two thyroid nodule datasets (TND-PUH3 and DDTI) and a brachial plexus nerve dataset (NSD) with Dice coefficients of 0.822, 0.792, and 0.746, respectively. The analysis of segmentation results shows that our MSAC-Unet greatly improves the segmentation accuracy with more reliable ROI edges and boundaries, decreasing the number of erroneously segmented ROIs in ultrasound images.