Acquisition Protocols Research Articles

FetMRQC: A robust quality control system for multi-centric fetal brain MRI

Fetal brain MRI is becoming an increasingly relevant complement to neurosonography for perinatal diagnosis, allowing fundamental insights into fetal brain development throughout gestation. However, uncontrolled fetal motion and heterogeneity in acquisition protocols lead to data of variable quality, potentially biasing the outcome of subsequent studies. We present FetMRQC, an open-source machine-learning framework for automated image quality assessment and quality control that is robust to domain shifts induced by the heterogeneity of clinical data. FetMRQC extracts an ensemble of quality metrics from unprocessed anatomical MRI and combines them to predict experts’ ratings using random forests. We validate our framework on a pioneeringly large and diverse dataset of more than 1600 manually rated fetal brain T2-weighted images from four clinical centers and 13 different scanners. Our study shows that FetMRQC’s predictions generalize well to unseen data while being interpretable. FetMRQC is a step towards more robust fetal brain neuroimaging, which has the potential to shed new insights on the developing human brain.

Translating phenotypic prediction models from big to small anatomical MRI data using meta-matching

Abstract Individualized phenotypic prediction based on structural MRI is an important goal in neuroscience. Prediction performance increases with larger samples, but small-scale datasets with fewer than 200 participants are often unavoidable. We have previously proposed a “meta-matching” framework to translate models trained from large datasets to improve the prediction of new unseen phenotypes in small collection efforts. Meta-matching exploits correlations between phenotypes, yielding large improvement over classical machine learning when applied to prediction models using resting-state functional connectivity as input features. Here, we adapt the two best performing meta-matching variants (“meta-matching finetune” and “meta-matching stacking”) from our previous study to work with T1-weighted MRI data by changing the base neural network architecture to a 3D convolution neural network. We compare the two meta-matching variants with elastic net and classical transfer learning using the UK Biobank (N = 36,461), Human Connectome Project Young Adults (HCP-YA) dataset (N = 1,017) and HCP-Aging dataset (N = 656). We find that meta-matching outperforms elastic net and classical transfer learning by a large margin, both when translating models within the same dataset, as well as translating models across datasets with different MRI scanners, acquisition protocols and demographics. For example, when translating a UK Biobank model to 100 HCP-YA participants, meta-matching finetune yielded a 136% improvement in variance explained over transfer learning, with an average absolute gain of 2.6% (minimum = -0.9%, maximum = 17.6%) across 35 phenotypes. Overall, our results highlight the versatility of the meta-matching framework.

Determination of the optimal imaging protocol for [18F]PSMA-PET-CT for the detection of bone metastases in prostate cancer patients.

Prostate-specific membrane antigen-positron emission tomography (PSMA-PET) is a widely used diagnostic tool in patients with prostate cancer (PC).However, due to the limited availability of PET scanners and relevant acquisition costs, it is important to consider the indications and acquisition time.The aim of this investigation was to determine whether a PET scan from the skull base to the proximal thigh is sufficient to detect the presence of bone metastases. A retrospective analysis was conducted on 1050 consecutive [18F]PSMA-1007-PET-CT scans from the head to the proximal lower leg.The PET scans were categorised according to the presence and amount of bone metastases: (1) 1-5, (2) 6-19 and (3) ≥20. Additionally, the PET scans were evaluated for the presence of bone metastases below the proximal thigh as well as bone metastases above the skull base.Imaging results were compared to patients PSA values. Of the 391 patients with bone metastases, 146 (37.3%) exhibited metastases located below the proximal thigh and 104 (26.6%) above the skull base.The majority of bone metastases located below the proximal thigh (145, 99.3%) and above the skull base (94, 90.4%) were identified in patients with more than five bone metastases. No solitary distal metastasis was detected.The PSA value correlated significantly with number of bone metastases (e. g., 1-5 vs. ≥20 bone metastases, P < 0.001) and was significantly higher in patients with distal bone metastases (P < 0.001).ROC analysis showed that a PSA value of 11.15 ng/mL is the optimal cut-off for detecting bone metastases located below the proximal thigh, with an AUC of 0.919 (95% CI: 0.892-0.945, sensitivity 87%, specificity 86%).Similarly, the PSA value of 12.86 ng/mL is the optimal cut-off for detecting bone metastases above the skull base with an AUC of 0.904 (95% CI: 0.874-0.935, sensitivity 87%, specificity 83%). CONCLUSION: PSMA-PET acquisition protocols from the skull base to the proximal femur may be sufficient to accurately detect bone metastatic disease in PC. PSA values can provide decision support for individual PET acquisition protocols.

How to accurately quantify brain magnetic susceptibility in the context of Parkinson's disease: Validation on phantoms and healthy volunteers at 1.5 and 3 T.

Currently, brain iron content represents a new neuromarker for understanding the physiopathological mechanisms leading to Parkinson's disease (PD). In vivo quantification of biological iron is possible by reconstructing magnetic susceptibility maps obtained using quantitative susceptibility mapping (QSM). Applying QSM is challenging, as up to now, no standardization of acquisition protocols and phase image processing has emerged from referenced studies. Our objectives were to compare the accuracy and the sensitivity of 10 QSM pipelines built from algorithms from the literature, applied on phantoms data and on brain data. Two phantoms, with known magnetic susceptibility ranges, were created from several solutions of gadolinium chelate. Twenty healthy volunteers from two age groups were included. Phantoms and brain data were acquired at 1.5 and 3 T, respectively. Susceptibility-weighted images were obtained using a 3D multigradient-recalled-echo sequence. For brain data, 3D anatomical T1- and T2-weighted images were also acquired to segment the deep gray nuclei of interest. Concerning in vitro data, the linear dependence of magnetic susceptibility versus gadolinium concentration and deviations from the theoretically expected values were calculated. For brain data, the accuracy and sensitivity of the QSM pipelines were evaluated in comparison with results from the literature and regarding the expected magnetic susceptibility increase with age, respectively. A nonparametric Mann-Whitney U-test was used to compare the magnetic susceptibility quantification in deep gray nuclei between the two age groups. Our methodology enabled quantifying magnetic susceptibility in human brain and the results were consistent with those from the literature. Statistically significant differences were obtained between the two age groups in all cerebral regions of interest. Our results show the importance of optimizing QSM pipelines according to the application and the targeted magnetic susceptibility range, to achieve accurate quantification. We were able to define the optimal QSM pipeline for future applications on patients with PD.

Whole-body MRI in oncology: acquisition protocols, current guidelines, and beyond.

Acknowledging the increasing use of whole-body magnetic resonance imaging (WB-MRI) in the oncological setting, we conducted a narrative review focusing on practical aspects of the examination and providing a synthesis of various acquisition protocols described in the literature. Firstly, we addressed the topic of patient preparation, emphasizing methods to enhance examination acceptance. This included strategies for reducing anxiety and patient distress, improving staff-patient interactions, and increasing overall patient comfort. Secondly, we analysed WB-MRI acquisition protocols recommended in existing imaging guidelines, such as MET-RADS-P, MY-RADS, and ONCO-RADS, and provided an overview of acquisition protocols reported in the literature regarding other expanding applications of WB-MRI in oncology, in patients with breast cancer, ovarian cancer, melanoma, colorectal and lung cancer, lymphoma, and cancers of unknown primary. Finally, we suggested possible acquisition parameters for whole-body images across MR systems from three different vendors.

CT reconstruction using diffusion posterior sampling conditioned on a nonlinear measurement model.

Recently, diffusion posterior sampling (DPS), where score-based diffusion priors are combined with likelihood models, has been used to produce high-quality computed tomography (CT) images given low-quality measurements. This technique permits one-time, unsupervised training of a CT prior, which can then be incorporated with an arbitrary data model. However, current methods rely on a linear model of X-ray CT physics to reconstruct. Although it is common to linearize the transmission tomography reconstruction problem, this is an approximation to the true and inherently nonlinear forward model. We propose a DPS method that integrates a general nonlinear measurement model. We implement a traditional unconditional diffusion model by training a prior score function estimator and apply Bayes' rule to combine this prior with a measurement likelihood score function derived from the nonlinear physical model to arrive at a posterior score function that can be used to sample the reverse-time diffusion process. We develop computational enhancements for the approach and evaluate the reconstruction approach in several simulation studies. The proposed nonlinear DPS provides improved performance over traditional reconstruction methods and DPS with a linear model. Moreover, as compared with a conditionally trained deep learning approach, the nonlinear DPS approach shows a better ability to provide high-quality images for different acquisition protocols. This plug-and-play method allows the incorporation of a diffusion-based prior with a general nonlinear CT measurement model. This permits the application of the approach to different systems, protocols, etc., without the need for any additional training.

A geometric approach to robust medical image segmentation

Robustness of deep learning segmentation models is crucial for their safe incorporation into clinical practice. However, these models can falter when faced with distributional changes. This challenge is evident in magnetic resonance imaging (MRI) scans due to the diverse acquisition protocols across various domains, leading to differences in image characteristics such as textural appearances. We posit that the restricted anatomical differences between subjects could be harnessed to refine the latent space into a set of shape components. The learned set then aims to encompass the relevant anatomical shape variation found within the patient population.We explore this by utilising multiple MRI sequences to learn texture invariant and shape equivariant features which are used to construct a shape dictionary using vector quantisation. We investigate shape equivariance to a number of different types of groups. We hypothesise and prove that the greater the group order, i.e., the denser the constraint, the better becomes the model robustness. We achieve shape equivariance either with a contrastive based approach or by imposing equivariant constraints on the convolutional kernels. The resulting shape equivariant dictionary is then sampled to compose the segmentation output. Our method achieves state-of-the-art performance for the task of single domain generalisation for prostate and cardiac MRI segmentation. Code is available at https://github.com/AinkaranSanthi/A_Geometric_Perspective_For_Robust_Segmentation.

Imaging in Infective Endocarditis-Current Opinions and Trends in Cardiac Computed Tomography.

Infective endocarditis is a rare disease with an increasing incidence and an unaltered high mortality rate, despite medical development. Imaging plays an integrative part in the diagnosis of infective endocarditis, with echocardiography as the initial diagnostic test. Research data in the utility of cardiac computed tomography (CCT) in the diagnostic algorithm of IE are rising, which indicates its importance in detection of IE-related lesion along with the exclusion of coronary artery disease. The latest 2023 European Society of Cardiology Guidelines in the management of IE classified CCT as class of recommendation I and level of evidence B in detection of both valvular and paravalvular lesions in native and prosthetic valve endocarditis. This review article provides a comprehensive and contemporary review of the role of CCT in the diagnosis of IE, the optimization of acquisition protocols, the morphology characteristics of IE-related lesions, the published data of the diagnostic performance of CCT in comparison to echocardiography as the state-of-art method, as well as the limitations and future possibilities.

Doppler Sonography Evaluation of Median Nerve Intraneural Blood Flow: A Systematic Review

Objective: This systematic review aims to determine the extent, scope, and nature of research using the sonographic measurement of intraneural blood flow within the median nerve and to identify, characterize, and compare image acquisition and analysis protocols that have been reported as potential candidate techniques for standardizing research and clinical applications. Materials and Methods: This systematic review summarizes image acquisition protocols and analysis methodologies from 52 current research studies using Doppler ultrasound to examine median nerve intraneural blood flow. Results: Four types of Doppler technologies were identified: power Doppler, color Doppler, spectral Doppler, and Superb Microvascular Imaging, but there were inconsistencies in how images were acquired and analyzed. Intraneural blood flow measurements were categorized into four types based on a combination of measurement level (e.g., binary, ordinal, continuous) and type of flow indicator (e.g., pixel count, intensity, velocity). Conclusion: Standardized imaging protocols and reporting guidelines are needed to improve consistency. Future studies should evaluate the accuracy and reliability of different image acquisition methods and analysis measurements.

IMG-08. SYNTHESIZING MISSING MRI SEQUENCES IN PEDIATRIC BRAIN TUMORS USING GENERATIVE ADVERSARIAL NETWORKS; TOWARDS IMPROVED VOLUMETRIC TUMOR ASSESSMENT

Abstract BACKGROUND Standard MRI sequences, such as pre- and post-contrast T1w, T2w, and FLAIR images, are essential for optimizing segmentation of tumor subregions and evaluating treatment responses in pediatric brain tumors (PBTs). However, MRI sets are often incomplete due to imaging artifacts or inconsistent acquisition protocols across various centers. Generative Adversarial Networks (GANs) have been effectively utilized to generate missing MRI sequences for adult brain tumors. This study applies image-to-image translation models using GANs to synthesize missing FLAIR images from T2w images in PBTs. METHODS This retrospective study developed two GAN models, pix2pix in both 2D and 3D, trained and validated on T2w and FLAIR image pairs from 79 and 19 patients, respectively, with pediatric-type diffuse high-grade gliomas (diffuse midline glioma (DMG) including diffuse intrinsic pontine glioma (DIPG)), collected from the Children’s Brain Tumor Network (CBTN). The 2D pix2pix model processes each T2w MRI volume slice-by-slice, generating corresponding FLAIR slices to reconstruct the complete image volume. The 3D pix2pix model allows translation of 3D FLAIR volumes directly from T2w volumes without the need for individual slice processing and reconstruction. The quality of the generated FLAIR volumes was evaluated using Structural Similarity Index (SSI; best closer to 1) and Mean Squared Error (MSE; best near 0) on the validation set. RESULTS The 2D and 3D models achieved robust performance with median SSI, MSE of 0.88, 0.004, and 0.92, 0.002, respectively. CONCLUSIONS The GAN models developed in this study effectively generate missing FLAIR MRI volumes from corresponding T2w images in PBTs. The 3D model, outperforming 2D, speeds the overall processing pipeline by eliminating volume slicing and reconstruction. Future work includes assessing the impact of these synthesized images on the accuracy of our pretrained autosegmentation models in differentiating tumor subregions and measuring tumor volumes in longitudinal MRIs.

A lesion‐aware automated processing framework for clinical stroke magnetic resonance imaging

AbstractMagnetic resonance imaging (MRI) and MRI based computational modelling studies provide insights into severity and recovery of ischemic stroke patients. The presence of brain lesions, however, can heavily distort and impair state‐of‐the‐art processing frameworks due to abnormal intensity values and tissue distortions. In this study, we introduce and validate the novel “Lesion Aware automated Processing Pipeline (LeAPP).” LeAPP automatically processes clinical stroke MRI data while significantly reducing the impact of pathological artefacts on processing outputs such as structural and functional connectomes (SC, FC). This helps to advance the identification of biomarkers for recovery and mechanism‐based intervention planning using MRI as well as computational approaches. We extended existing frameworks, such as the Human Connectome Project (HCP) minimal processing pipeline, introducing correction steps, and implementing and modifying functional and diffusion processing to cope with MRI acquisition protocols more typical for the clinical context. A total of 51 participants (36 patients, 15 age‐matched controls) were processed across four time points for patients (3–5, 30–40, 85–95, 340–380 days after stroke onset) and one time point for controls. We validated performance using artificial lesioned brains (N = 81), derived from healthy brains and informed by real stroke lesions. The quality of reconstructing the ground truth was quantified on whole brain level and for lesion affected and unaffected regions‐of‐interest (ROIs) for brain parcellations and for SCs. Volume based agreement was evaluated using metrics such as dice coefficient, volume difference or ROI center‐of‐gravity distance while SC based agreement was defined as the difference in network metrics (e.g., node strength, clustering coefficient, or centrality). The observed deviations in reconstructed ground truth brain parcellations and structural connectomes from lesioned brains were significantly reduced for LeAPP, compared to the performance of existing pipelines such as HCP minimal processing pipeline. For instance, in the case of lesion affected ROIS we achieved a mean dice coefficient (where a value of one represents total agreement as defined by an exact overlap of both ROIs) of 0.81 with LeAPP, compared to 0.75 for HCP (p &lt; .0001*). Additionally, the average measured ROI distance (where a value of zero represents no difference) for lesion ROIs was 0.87 for LeAPP in contrast to 1.7 for HCP (p &lt; .0001*), indicating an overall superior performance of LeAPP. The pipeline generates standardized output files ready for brain network modelling for instance with The Virtual Brain software. This novel open‐source automated processing framework contributes to reproducible research and provides a robust framework for automated processing of clinical stroke MRI data, supporting the identification of brain network‐based biomarkers of stroke recovery.

Solving the Pervasive Problem of Protocol Non-Compliance in MRI using an Open-Source tool mrQA

Pooling data across diverse sources acquired by multisite consortia requires compliance with a predefined reference protocol i.e., ensuring different sites and scanners for a given project have used identical or compatible MR physics parameter values. Traditionally, this has been an arduous and manual process due to difficulties in working with the complicated DICOM standard and lack of resources allocated towards protocol compliance. Moreover, issues of protocol compliance is often overlooked for lack of realization that parameter values are routinely improvised/modified locally at various sites. The inconsistencies in acquisition protocols can reduce SNR, statistical power, and in the worst case, may invalidate the results altogether. An open-source tool, mrQA was developed to automatically assess protocol compliance on standard dataset formats such as DICOM and BIDS, and to study the patterns of non-compliance in over 20 open neuroimaging datasets, including the large ABCD study. The results demonstrate that the lack of compliance is rather pervasive. The frequent sources of non-compliance include but are not limited to deviations in Repetition Time, Echo Time, Flip Angle, and Phase Encoding Direction. It was also observed that GE and Philips scanners exhibited higher rates of non-compliance relative to the Siemens scanners in the ABCD dataset. Continuous monitoring for protocol compliance is strongly recommended before any pre/post-processing, ideally right after the acquisition, to avoid the silent propagation of severe/subtle issues. Although, this study focuses on neuroimaging datasets, the proposed tool mrQA can work with any DICOM-based datasets.

Line-field dynamic optical coherence tomography platform for volumetric assessment of biological tissues.

Dynamic optical coherence tomography (dOCT) utilizes time-dependent signal intensity fluctuations to enhance contrast in OCT images and indirectly probe physiological processes in cells. Majority of the dOCT studies published so far are based on acquisition of 2D images (B-scans or C-scans) by utilizing point-scanning Fourier domain (spectral or swept-source) OCT or full-field OCT respectively, primarily due to limitations in the image acquisition rate. Here we introduce a novel, high-speed spectral domain line-field dOCT (SD-LF-dOCT) system and image acquisition protocols designed for fast, volumetric dOCT imaging of biological tissues. The imaging probe is based on an exchangeable afocal lens pair that enables selection of combinations of transverse resolution (from 1.1 µm to 6.4 µm) and FOV (from 250 × 250 µm2 to 1.4 × 1.4 mm2), suitable for different biomedical applications. The system offers axial resolution of ∼ 1.9 µm in biological tissue, assuming an average refractive index of 1.38. Maximum sensitivity of 90.5 dB is achieved for 3.5 mW optical imaging power at the tissue surface and maximum camera acquisition rate of 2,000 fps. Volumetric dOCT images acquired with the SD-LF-dOCT system from plant tissue (cucumber), animal tissue (mouse liver) and human prostate carcinoma spheroids allow for volumetric visualization of the tissues' cellular and sub-cellular structures and assessment of cellular motility.

Time-of-flight MRA of intracranial vessels at 7 T

BackgroundThree-dimensional time-of-flight magnetic resonance angiography (TOF-MRA) is a largely adopted non-invasive technique for assessing cerebrovascular diseases. We aimed to optimize the 7-T TOF-MRA acquisition protocol, confirm that it outperforms conventional 3-T TOF-MRA, and compare 7-T TOF-MRA with digital subtraction angiography (DSA) in patients with different vascular pathologies.MethodsSeven-tesla TOF-MRA sequences with different spatial resolutions acquired in four healthy subjects were compared with 3-T TOF-MRA for signal-to-noise and contrast-to-noise ratios as well as using a qualitative scale for vessel visibility and the quantitative Canny algorithm. Four patients with cerebrovascular disease (primary arteritis of the central nervous system, saccular aneurism, arteriovenous malformation, and dural arteriovenous fistula) underwent optimized 7-T TOF-MRA and DSA as reference. Images were compared visually and using the complex-wavelet structural similarity index.ResultsContrast-to-noise ratio was higher at 7 T (4.5 ± 0.8 (mean ± standard deviation)) than at 3 T (2.7 ± 0.9). The mean quality score for all intracranial vessels was higher at 7 T (2.89) than at 3 T (2.28). Angiogram quality demonstrated a better vessel border detection at 7 T than at 3 T (44,166 versus 28,720 pixels). Of 32 parameters used for diagnosing cerebrovascular diseases on DSA, 27 (84%) were detected on 7-T TOF-MRA; the similarity index ranged from 0.52 (dural arteriovenous fistula) to 0.90 (saccular aneurysm).ConclusionsSeven-tesla TOF-MRA outperformed conventional 3-T TOF-MRA in evaluating intracranial vessels and exhibited an excellent image quality when compared to DSA. Seven-tesla TOF-MRA might improve the non-invasive diagnostic approach to several cerebrovascular diseases.Relevance statementAn optimized TOF-MRA sequence at 7 T outperforms 3-T TOF-MRA, opening perspectives to its clinical use for noninvasive diagnosis of paradigmatic pathologies of intracranial vessels.Key points• An optimized 7-T TOF-MRA protocol was selected for comparison with clinical 3-T TOF-MRA for assessing intracranial vessels.• Seven-tesla TOF-MRA outperformed 3-T TOF-MRA in both quantitative and qualitative evaluation.• Seven-tesla TOF-MRA is comparable to DSA for the diagnosis and characterization of intracranial vascular pathologies.Graphical

Subcortical tau is linked to hypoperfusion in connected cortical regions in 4-repeat tauopathies.

Four-repeat (4R) tauopathies are neurodegenerative diseases characterized by cerebral accumulation of 4R tau pathology. The most prominent 4R tauopathies are progressive supranuclear palsy (PSP) and corticobasal degeneration characterized by subcortical tau accumulation and cortical neuronal dysfunction, as shown by PET-assessed hypoperfusion and glucose hypometabolism. Yet, there is a spatial mismatch between subcortical tau deposition patterns and cortical neuronal dysfunction, and it is unclear how these two pathological brain changes are interrelated. Here, we hypothesized that subcortical tau pathology induces remote neuronal dysfunction in functionally connected cortical regions to test a pathophysiological model that mechanistically links subcortical tau accumulation to cortical neuronal dysfunction in 4R tauopathies. We included 51 Aβ-negative patients with clinically diagnosed PSP variants (n = 26) or corticobasal syndrome (n = 25) who underwent structural MRI and 18F-PI-2620 tau-PET. 18F-PI-2620 tau-PET was recorded using a dynamic one-stop-shop acquisition protocol to determine an early 0.5-2.5 min post tracer-injection perfusion window for assessing cortical neuronal dysfunction, as well as a 20-40 min post tracer-injection window to determine 4R-tau load. Perfusion-PET (i.e. early window) was assessed in 200 cortical regions, and tau-PET was assessed in 32 subcortical regions of established functional brain atlases. We determined tau epicentres as subcortical regions with the highest 18F-PI-2620 tau-PET signal and assessed the connectivity of tau epicentres to cortical regions of interest using a resting-state functional MRI-based functional connectivity template derived from 69 healthy elderly controls from the ADNI cohort. Using linear regression, we assessed whether: (i) higher subcortical tau-PET was associated with reduced cortical perfusion; and (ii) cortical perfusion reductions were observed preferentially in regions closely connected to subcortical tau epicentres. As hypothesized, higher subcortical tau-PET was associated with overall lower cortical perfusion, which remained consistent when controlling for cortical tau-PET. Using group-average and subject-level PET data, we found that the seed-based connectivity pattern of subcortical tau epicentres aligned with cortical perfusion patterns, where cortical regions that were more closely connected to the tau epicentre showed lower perfusion. Together, subcortical tau-accumulation is associated with remote perfusion reductions indicative of neuronal dysfunction in functionally connected cortical regions in 4R-tauopathies. This suggests that subcortical tau pathology may induce cortical dysfunction, which may contribute to clinical disease manifestation and clinical heterogeneity.

Ultrasound backscatter coefficient for fat quantification is affected by the measurement depth.

It has been reported that the estimate of ultrasound attenuation coefficient (AC) is affected by depth of measurement, with linear decrease of values with depth. It is unknown whether backscatter coefficient (BSC) has similar behavior. This retrospective study was performed with Sequoia US system equipped with ultrasound derived fat fraction (UDFF) algorithm (Siemens Healthineers, Issaquah, WA, USA) that combines BSC with AC. UDFF was obtained positioning upper edge of the region of interest at 1.5,2,3,4,5cm below liver capsule. BSC data were extracted from UDFF offline. A fractional polynomial regression, which selects the best model considering the polynomial development of the variables of interest, was used. Covariates included were age, sex, skin-to-liver-capsule distance, stiffness. Distance was included as linear factor or with a power ranging from -2 to 3, and the best fitting model was chosen according to partial F test. Body mass index (BMI) was not included because of collinearity with skin-to-liver capsule distance. 104 individuals (56 females; age: 57.9 ± 13.0years; BMI: 29.0 ± 6.5kg/m2; skin-to-liver-capsule distance: 2.3 ± 0.7cm; liver stiffness: 7.5 ± 5.5 kiloPascal) were studied. Best fitting model for BSC included a combination of depth as linear factor and square root. BSC showed a decrease of -13.98dB/cm-steradian for each logarithmic increase of 1cm depth (coefficient: -13.98; 95% CI: -21.016; -5.379; p = .001). Skin-to-liver-capsule distance and stiffness also were independent predictors of BSC. The estimation of the BSC in the liver exhibits a depth dependence that significantly affects results. A standardized acquisition protocol is needed to compare results and reliably assess changes over time.

Integrating deep learning network and liquid biopsy for the evaluation and prediction of response in patients with advanced non-small cell lung cancer: Initial results from the RATIONALE prospective study.

e20036 Background: Lung cancerstill represents one of the most common and lethal diseases worldwide. There remains an unmet clinical need for the discovery of biomarkers to refine the management of patients with advanced non-small cell lung cancer (NSCLC). The combined evaluation of circulating cell-free DNA (cfDNA) and deep learning-based radiomic methods remains to be validated in the clinical setting. Methods: The RATIONALE study is a retro-prospective observational clinical trial consecutively enrolling treatment-naïve patients with advanced NSCLC who received standard first-line systemic treatments at Paolo Giaccone University Hospital, Palermo (Italy). Baseline and follow-up blood samples were collected according to computed tomography (CT) examinations performed using the same equipment (Siemens SOMATOM 128 slices), the same acquisition protocol, and the same contrast medium (Iomeron 400) every twelve weeks. All primary lung tumor internal regions of interest (ROIs) were segmented. Radiomic feature extraction was performed on the ROIs using Pyradiomics, an extension of the 3DSlicer. Circulating cfDNA was extracted from 1 ml of plasma using a QIAamp Circulating Nucleic Acid Kit (Qiagen) and quantified in terms of ng/μL using a QubitTM dsDNA HS Assay Kit (ThermoFisher Scientific). The diagnostic performance was evaluated using receiver operating characteristic (ROC) curves. Results: Between June 2021 and November 2023, fifty-five patients were included. Here we present the first results from the cohort of twenty patients undergoing first-line pembrolizumab-based treatments. Among the thirteen features extracted, the feature 'original_glcm_Id' evaluating the local image homogeneity emerged as statistically significant for differentiating adenocarcinoma and squamous cell carcinoma (sensitivity of 83.7%, specificity of 81.0%, accuracy of 82.8%; p &lt; 0.001). When correlating cfDNA dynamics and radiologic response, out of the eight features examined, the feature 'first order_mean' measuring the average intensity of gray levels proved to be statistically significant (sensitivity of 86.0%, specificity of 87.7%, accuracy of 86.9%; p &lt; 0.006). Finally, the feature 'size zone non-uniformity' measuring the variability of volumes in different gray level zones within the image showed a good performance in predicting overall survival (sensitivity of 65.8%, specificity of 100.0%, accuracy of 77.8%; p &lt; 0.001). Conclusions: The preliminary results of this ongoing study suggest the combined evaluation of liquid biopsy and radiomic features for obtaining minimally invasive and cost-effective biomarkers in the first-line clinical setting of NSCLC undergoing first-line pembrolizumab.

Implementing ABCD studyⓇ MRI sequences for multi-site cohort studies: Practical guide to necessary steps, preprocessing methods, and challenges

Large multi-site studies that combine magnetic resonance imaging (MRI) data across research sites present exceptional opportunities to advance neuroscience research. However, scanner or site variability and non-standardised image acquisition protocols, data processing and analysis pipelines can adversely affect the reliability and repeatability of MRI derived brain measures. We implemented a standardised MRI protocol based on that used in the Adolescent Brain Cognition Development (ABCD)Ⓡ study in two sites, and across four MRI scanners.Twice repeated measurements of a single healthy volunteer were obtained in two sites and in four 3T MRI scanners (vendors: Siemens, Philips, and GE). Imaging data included anatomical scans (T1 weighted, T2 weighted), diffusion weighted imaging (DWI) and resting state functional MRI (rs-fMRI). Standardised containerized pipelines were utilised to pre-process the data and different image quality metrics and test-retest variability of different brain metrics were evaluated.The implementation of the MRI protocols was possible with minor adjustments in acquisition (e.g. repetition time (TR), higher b-values) and exporting (DICOM formats) of images due to different technical performance of the scanners. This study provides practical insights into the implementation of standardised sequences and data processing for multisite studies, showcase the benefits of containerised preprocessing tools, and highlights the need for careful optimisation of multisite image acquisition.

Diagnostic reference levels in spinal CT: Jordanian assessments and global benchmarks.

To reduce radiation dose and subsequent risks, several legislative documents in different countries describe the need for Diagnostic Reference Levels (DRLs). Spinal radiography is a common and high-dose examination. Therefore, the aim of this work was to establish the DRL for Computed Tomography (CT) examinations of the spine in healthcare institutions across Jordan. Data was retrieved from the picture archiving and communications system (PACS), which included the CT Dose Index (CTDI(vol)) and Dose Length Product (DLP). The median radiation dose values of the dosimetric indices were calculated for each site. DRL values were defined as the 75th percentile distribution of the median CTDI(vol) and DLP values. Data was collected from 659 CT examinations (316 cervical spine and 343 lumbar-sacral spine). Of the participants, 68% were males, and the patients' mean weight was 69.7 kg (minimum = 60; maximum = 80, SD = 8.9). The 75th percentile for the DLP of cervical and LS-spine CT scans in Jordan were 565.2 and 967.7 mGy.cm, respectively. This research demonstrates a wide range of variability in CTDI(vol) and DLP values for spinal CT examinations; these variations were associated with the acquisition protocol and highlight the need to optimize radiation dose in spinal CT examinations.

Feasibility of virtual non-iodine coronary calcium scoring on dual source photon-counting coronary CT angiography: a dynamic phantom study

BackgroundThe aim of our current systematic dynamic phantom study was first, to optimize reconstruction parameters of coronary CTA (CCTA) acquired on photon counting CT (PCCT) for coronary artery calcium (CAC) scoring, and second, to assess the feasibility of calculating CAC scores from CCTA, in comparison to reference calcium scoring CT (CSCT) scans.MethodsIn this phantom study, an artificial coronary artery was translated at velocities corresponding to 0, < 60, and 60–75 beats per minute (bpm) within an anthropomorphic phantom. The density of calcifications was 100 (very low), 200 (low), 400 (medium), and 800 (high) mgHA/cm3, respectively. CCTA was reconstructed with the following parameters: virtual non-iodine (VNI), with and without iterative reconstruction (QIR level 2, QIR off, respectively); kernels Qr36 and Qr44f; slice thickness/increment 3.0/1.5 mm and 0.4/0.2 mm. The agreement in risk group classification between CACCCTA and CACCSCT scoring was measured using Cohen weighted linear κ with 95% CI.ResultsFor CCTA reconstructed with 0.4 mm slice thickness, calcium detectability was perfect (100%). At < 60 bpm, CACCCTA of low, and medium density calcification was underestimated by 53%, and 15%, respectively. However, CACCCTA was not significantly different from CACCSCT of very low, and high-density calcifications. The best risk agreement was achieved when CCTA was reconstructed with QIR off, Qr44f, and 0.4 mm slice thickness (κ = 0.762, 95% CI 0.671–0.853).ConclusionIn this dynamic phantom study, the detection of calcifications with different densities was excellent with CCTA on PCCT using thin-slice VNI reconstruction. Agatston scores were underestimated compared to CSCT but agreement in risk classification was substantial.Clinical relevance statementPhoton counting CT may enable the implementation of coronary artery calcium scoring from coronary CTA in daily clinical practice.Key PointsPhoton-counting CTA allows for excellent detectability of low-density calcifications at all heart rates.Coronary artery calcium scoring from coronary CTA acquired on photon counting CT is feasible, although improvement is needed.Adoption of the standard acquisition and reconstruction protocol for calcium scoring is needed for improved quantification of coronary artery calcium to fully employ the potential of photon counting CT.