European Radiology Experimental最新文献

Background: Early detection and treatment of tendinopathy may prevent progression to partial tears or complete rupture. Shear wave elastography (SWE) may help address the need for better tendon pathology characterization. This study aimed to quantify the effect of structural damage in an ex vivo animal tendinopathy model using SWE.

Methods: Forty-two porcine flexor tendons were injected with a 0.05-mL bolus of 1.5% collagenase solution to induce focal structural damage without surface tears. Control tendons were injected with saline (n = 42). Twenty-one tendons from each group were incubated at 37 °C for 3.5 h, while the remaining 21 from each group were incubated for 7 h. Each group was then divided into three groups of seven, and tendon incisions were made at 25%, 50%, and 75% of the tendon thickness. Tendons were mechanically stretched axially during simultaneous collection of SWE at the injection site.

Results: There were significant differences in shear wave speed (SWS) (saline > collagenase) at 3.5-h incubation (p < 0.001) and 7-h incubation (p < 0.001). Additionally, there was a significant difference in SWS between tendons cut at 25% and tendons cut at 50% and 75% (p = 0.040 and p = 0.001, respectively). Collagenase-treated tendons ruptured at a lower force than saline-treated tendons at both incubation times (both p < 0.001) when controlling for cut depth. Tendons treated with collagenase ruptured at a lower force than the saline control group at each cut thickness (all p < 0.001) controlling for incubation time.

Conclusion: In a controlled ex vivo porcine model, SWE can be used to detect structural damage associated with tendinopathy.

Relevance statement: Shear wave elastography can be used to show differences in abnormal tendons that may be translatable to clinical use as an adjunctive measure of tendon elasticity and injury.

Key points: Tendon abnormality was quantitatively characterized using shear wave elastography in an ex vivo porcine experimental model. Shear wave speed was an accurate imaging biomarker for tendon health. Shear wave elastography was effective at detecting the extent of tendon damage. Tendons with decreased shear wave speed measurements rupture at smaller applied mechanical force.

Background: The cingulum bundle is a brain white matter fasciculus associated with the cingulate gyrus. It connects areas from the temporal to the frontal lobe. It is composed of fibers with different terminations, lengths, and structural properties, related to specific brain functions. We aimed to automatically reconstruct this fasciculus in patients with Alzheimer disease (AD) and mild cognitive impairment (MCI) and to assess whether trajectories have different microstructural properties in relation to dementia progression.

Methods: Multi-shell high angular resolution diffusion imaging-HARDI image datasets from the "Alzheimer's Disease Neuroimaging Initiative"-ADNI repository of 10 AD, 18 MCI, and 21 cognitive normal (CN) subjects were used to reconstruct three subdivisions of the cingulum bundle, using a probabilistic approach, combined with measurements of diffusion tensor and neurite orientation dispersion and density imaging metrics in each subdivision.

Results: The subdivisions exhibit different pathways, terminations, and structural characteristics. We found differences in almost all the diffusivity metrics among the subdivisions (p < 0.001 for all the metrics) and between AD versus CN and MCI versus CN subjects for mean diffusivity (p = 0.007-0.038), radial diffusivity (p = 0.008-0.049) and neurite dispersion index (p = 0.005-0.049).

Conclusion: Results from tractography analysis of the subdivisions of the cingulum bundle showed an association in the role of groups of fibers with their functions and the variance of their properties in relation to dementia progression.

Relevance statement: The cingulum bundle is a complex tract with several pathways and terminations related to many cognitive functions. A probabilistic automatic approach is proposed to reconstruct its subdivisions, showing different microstructural properties and variations. A larger sample of patients is needed to confirm results and elucidate the role of diffusion parameters in characterizing alterations in brain function and progression to dementia.

Key points: The microstructure of the cingulum bundle is related to brain cognitive functions. A probabilistic automatic approach is proposed to reconstruct the subdivisions of the cingulum bundle by diffusion-weighted images. The subdivisions showed different microstructural properties and variations in relation to the progression of dementia.

Background: Beam hardening (BH) artifacts negatively influence computed tomography (CT) measurements, especially when due to dense materials or materials with high effective atomic numbers. Photon-counting detectors (PCD) are more susceptible to BH due to equal weighting of photons regardless of their energies. The problem is further confounded by the use of contrast agents (CAs) with K-edge in the diagnostic CT energy range. We quantified the BH effect of different materials comparing energy-integrating detector (EID)-CT and PCD-CT.

Methods: Pairs of test tubes were filled with dense CA (iodine-, gadolinium-, and bismuth-based) and placed inside a water phantom. The phantoms were scanned on EID- and PCD-CT systems, at all available tube voltages for the PCD scanner. Images were reconstructed with standard water BH correction but without any iodine/bone BH corrections. Virtual monoenergetic images (VMI) were calculated from PCD-CT data.

Results: PCD-CT had higher CT numbers in all x-ray spectra for all CAs (p < 0.001) and produced larger cupping artifacts in all test cases (p < 0.001). Bismuth-based CA artifacts were 3- to 5-fold smaller than those of iodine- or gadolinium-based CA. PCD-CT-based VMI completely removed iodine BH artifacts. Iodine BH artifacts decreased with increasing tube voltage. However, gadolinium-based BH artifacts had a different trend increasing at 120 kVp.

Conclusion: EID had fewer BH artifacts compared to PCD at x-ray tube voltages of 120 kVp and higher. The inherent spectral information of PCDs can be used to eliminate BH artifacts. Special care is needed to correct BH artifacts for gadolinium- and bismuth-based CAs.

Relevance statement: With the increasing availability of clinical photon-counting CT systems offering the possibility of dual contrast imaging capabilities, addressing and comprehending the BH artifacts attributed to old and novel CT CAs grows in research and ultimately clinical relevance.

Key points: EID-CT provides fewer BH artifacts compared to PCD-CT at x-ray tube voltages of 120 kVp and higher. K-edge CAs, such as those based on gadolinium, further confound BH artifacts. The inherent spectral information of photon counting detector CT can be used to effectively eliminate BH artifacts.

Background: We studied the microvascular structure and function of in situ glioblastoma using ultrasound localization microscopy (ULM).

Methods: The in vivo study was conducted via craniotomy in six Sprague-Dawley rats. Capillary pattern, capillary hemodynamics, and functional quantitative parameters were compared among tumor core, invasive zone, and normal brain tissue with ex vivo micro-computed tomography (micro-CT) and scanning electron microscopy. Correlations between quantitative parameters and histopathological vascular density (VD-H), proliferation index, and histopathological vascular maturity index (VMI-H) were evaluated. Kruskal-Wallis H, ANOVA, Mann-Whitney U, Pearson, and Spearman correlation statistics were used.

Results: Compared to the tumor core, the invasive zone exhibited higher microvascularity structural disorder and complexity, increased hemodynamic heterogeneity, higher local blood flow perfusion (p ≤ 0.033), and slightly lower average flow velocity (p = 0.873). Significant differences were observed between the invasive zone and normal brain tissue across all parameters (p ≤ 0.001). ULM demonstrated higher microstructural resolution compared to micro-CT and a nonsignificant difference compared to scanning electron microscopy. The invasive zone vascular density correlated with VD-H (r = 0.781, p < 0.001). Vessel diameter (r = 0.960, p < 0.001), curvature (r = 0.438, p = 0.047), blood flow velocity (r = 0.487, p = 0.025), and blood flow volume (r = 0.858, p < 0.001) correlated with proliferation index. Vascular density (r = -0.444, p = 0.044) and fractal dimension (r = -0.933, p < 0.001) correlated with VMI-H.

Conclusion: ULM provided high-resolution, noninvasive imaging of glioblastoma microvascularity, offering insights into structural/functional abnormalities.

Relevance statement: ULM technology based on ultrafast ultrasound can accurately quantify the microvessels of glioblastoma, providing a new method for evaluating the effectiveness of antiangiogenic therapy and visualizing disease progression. This method may facilitate early therapeutic assessment.

Key points: ULM reliably captures the vascular structures and hemodynamic features of glioblastoma in rats. Micro-CT and scanning electron microscopy validated its effectiveness in microvascular non-invasion characterization. ULM is expected to effectively evaluate glioblastoma anti-vascular therapy response.

Background: We retrospectively evaluated the quality of deep learning (DL) reconstructions of on-scanner accelerated intraoperative MRI (iMRI) during respective brain tumor surgery.

Methods: Accelerated iMRI was performed using dual surface coils positioned around the area of resection. A DL model was trained on the fastMRI neuro dataset to mimic the data from the iMRI protocol. The evaluation was performed on imaging material from 40 patients imaged from Nov 1, 2021, to June 1, 2023, who underwent iMRI during tumor resection surgery. A comparative analysis was conducted between the conventional compressed sense (CS) method and the trained DL reconstruction method. Blinded evaluation of multiple image quality metrics was performed by two neuroradiologists and one neurosurgeon using a 1-to-5 Likert scale (1, nondiagnostic; 2, poor; 3, acceptable; 4, good; and 5, excellent), and the favored reconstruction variant.

Results: The DL reconstruction was strongly favored or favored over the CS reconstruction for 33/40, 39/40, and 8/40 of cases for readers 1, 2, and 3, respectively. For the evaluation metrics, the DL reconstructions had a higher score than their respective CS counterparts for 72%, 72%, and 14% of the cases for readers 1, 2, and 3, respectively. Still, the DL reconstructions exhibited shortcomings such as a striping artifact and reduced signal.

Conclusion: DL shows promise in allowing for high-quality reconstructions of iMRI. The neuroradiologists noted an improvement in the perceived spatial resolution, signal-to-noise ratio, diagnostic confidence, diagnostic conspicuity, and spatial resolution compared to CS, while the neurosurgeon preferred the CS reconstructions across all metrics.

Relevance statement: DL shows promise to allow for high-quality reconstructions of iMRI, however, due to the challenging setting of iMRI, further optimization is needed.

Key points: iMRI is a surgical tool with a challenging image setting. DL allowed for high-quality reconstructions of iMRI. Additional optimization is needed due to the challenging intraoperative setting.

Substantial endeavors have been recently dedicated to developing artificial intelligence (AI) solutions, especially deep learning-based, tailored to enhance radiological procedures, in particular algorithms designed to minimize radiation exposure and enhance image clarity. Thus, not only better diagnostic accuracy but also reduced potential harm to patients was pursued, thereby exemplifying the intersection of technological innovation and the highest standards of patient care. We provide herein an overview of recent AI developments in computed tomography and magnetic resonance imaging. Major AI results in CT regard: optimization of patient positioning, scan range selection (avoiding "overscanning"), and choice of technical parameters; reduction of the amount of injected contrast agent and injection flow rate (also avoiding extravasation); faster and better image reconstruction reducing noise level and artifacts. Major AI results in MRI regard: reconstruction of undersampled images; artifact removal, including those derived from unintentional patient's (or fetal) movement or from heart motion; up to 80-90% reduction of GBCA dose. Challenges include limited generalizability, lack of external validation, insufficient explainability of models, and opacity of decision-making. Developing explainable AI algorithms that provide transparent and interpretable outputs is essential to enable seamless AI integration into CT and MRI practice. RELEVANCE STATEMENT: This review highlights how AI-driven advancements in CT and MRI improve image quality and enhance patient safety by leveraging AI solutions for dose reduction, contrast optimization, noise reduction, and efficient image reconstruction, paving the way for safer, faster, and more accurate diagnostic imaging practices. KEY POINTS: Advancements in AI are revolutionizing the way radiological images are acquired, reconstructed, and interpreted. AI algorithms can assist in optimizing radiation doses, reducing scan times, and enhancing image quality. AI techniques are paving the way for a future of more efficient, accurate, and safe medical imaging examinations.

Background: We evaluated the diagnostic capability of photon-counting detector computed tomography (PCD-CT) spectral variables in late arterial phase (LAP) and portal venous phase (PVP) to discriminate between local tumor recurrence (LTR) and postoperative changes (POC) after pancreatic ductal adenocarcinoma (PDAC) resection.

Methods: Seventy-three consecutive PCD-CT scans in 73 patients with postoperative soft-tissue lesions (PSLs) were included, 42 with POC and 31 with LTR. Regions of interest were drawn in each PSL, and spectral variables were calculated: iodine concentration (IC), normalized IC (NIC), fat fraction, attenuation at 40, 70, and 90 keV, and slope of the spectral curve between 40-90 keV. Multivariable binary logistic regression models were constructed. Diagnostic performance was assessed for LAP and PVP using receiver operating characteristic analysis.

Results: In LAP, all variables except fat fraction showed significant differences between LTR and POC (p ≤ 0.025). In PVP, all variables except NIC and fat fraction demonstrated significant differences between LTR and POC (p ≤ 0.005). Logistic regression analysis included NIC and 70 keV in the LAP-based model and IC and 90 keV in the PVP-based model. Both models achieved a higher area under the curve (AUC) than individual spectral variables in each phase. The LAP-based model achieved an AUC of 0.919 with 94% sensitivity, 84% specificity, and 87% accuracy, while the PVP-based model reached 0.820, 71%, 88%, and 81%, respectively.

Conclusion: Spectral variables from PCD-CT help distinguish between LTR and POC in LAP and PVP post-PDAC resection. Multivariable logistic regression improves diagnostic performance, especially in LAP.

Relevance statement: Measuring normalized iodine concentration and attenuation at 70 keV in late arterial phase, or iodine concentration and attenuation at 90 keV in portal venous phase, and incorporating these values into a logistic regression model can help differentiate between local tumor recurrence and postoperative changes after pancreatic ductal adenocarcinoma resection.

Key points: Distinguishing recurrence from postoperative changes on CT after pancreatic ductal adenocarcinoma resection is challenging. PCD-CT spectral variable values differed significantly between local tumor recurrence (LTR) and postoperative changes (POC). Logistic regression of spectral variables can help distinguish LTR from POC. The late arterial phase-based model reached an AUC of 0.919 with 94% sensitivity and 84% specificity.

Augmented reality (AR) is a new technique enabling interaction with three-dimensional (3D) holograms of cinematic rendering (CR) reconstructions. Research in this field is in its very early steps, and data is scarce. We evaluated image quality, usability, and potential applications of AR in cardiovascular image datasets. Ten CR reconstructions of cardiovascular computed tomography (CT) datasets with complex anatomical abnormalities were presented to six radiologists and three cardiologists first on diagnostic screens and subsequently in AR. Subjective image quality and user experience were rated on 5-point Likert scales to assess usability and potential applications of AR. CR of CT datasets covering multiple images series of the same exam with differing kernels was performed in 143 ± 31 s (mean ± standard deviation); reconstruction of single CT image series took 84 ± 30 s. Mean subjective image quality was excellent, and observers showed high endorsement of the intuitive usability of the AR device and improvement of anatomical comprehensibility. AR devices were expected to have the greatest impact on patient and student education as well as multidisciplinary discussions, with less potential in clinical care. Clinical testing and preclinical implementation of AR seem feasible due to reasonable computation times and intuitive usability even for first-time users. RELEVANCE STATEMENT: The presentation of 3D cinematic rendering in augmented reality provides excellent image quality, facilitating the comprehension of anatomical structures in CT datasets. Concurrently, reasonable computation times and the intuitive usability of augmented reality devices make preclinical implementation and clinical testing feasible. KEY POINTS: 3D cinematic reconstructions presented in augmented reality improve the anatomical comprehensibility of chest CT scans. Augmented reality devices are expected to be highly beneficial in educational settings and multidisciplinary discussions. Usability and computation times are feasible for initial preclinical use cases.

Background: Super-resolution ultrasound imaging (SRUI) is a rapidly expanding field with the potential to impact cancer management. Image processing algorithms applied to contrast-enhanced ultrasound (CEUS) video data can track the path of the contrast agent and produce high-resolution maps of vascular networks. Our aim was to develop SRUI for mapping prostate vascular dynamics and to assess the feasibility of identifying vascular patterns associated with prostate cancer.

Methods: Tracking algorithms for SRUI were developed using in silico data and validated in pre-clinical CEUS video collected from the sheep ovary. Algorithm performance was then assessed in a retrospective study of 54 image planes within 14 human prostates. CEUS data was collected for each plane, and regions of suspected cancer in each were identified from biopsy data.

Results: Of three algorithms assessed, utilising vascular knowledge was found to be the most robust method. Regions of suspected cancer were associated with increased blood flow volume and speed while avascular regions were also identified. Ten scan planes had confirmed Gleason 7 cancer; of these 10 planes, 7 had distinct regions of fast and high-volume flow, while 6 had both avascular and high flow regions. The cancer-free planes had more consistent, low blood flow values across the plane.

Conclusion: SRUI can be used to identify imaging biomarkers associated with vascular architecture and dynamics. These multiparameter biomarkers may be useful in pinpointing regions of significant prostate cancer.

Relevance statement: Super-resolution ultrasound imaging can generate microvascular maps of the prostate, revealing tissue patterns and presenting significant potential for the identification of multiple biomarkers associated with the localisation of prostate cancer.

Trial registration: Retrospectively registered NCT02831920, date 5/7/2016 https://www.

Clinicaltrials: gov/study/NCT02831920 .

Key points: An algorithm was developed and tested in synthetic pre-clinical and clinical data. Maps of blood vessels were created using contrast-enhanced ultrasound imaging. Specific presentations of vasculature at regions of prostate cancer have been identified.

Background: The human placenta is critical in supporting fetal development, and placental dysfunction may compromise maternal-fetal health. Early detection of placental dysfunction remains challenging due to the lack of reliable biomarkers. This study compares placental quantitative susceptibility mapping and T2* values between healthy and high-risk pregnancies and investigates their association with maternal and fetal parameters and their ability to predict birth weight (BW).

Methods: A total of 105 pregnant individuals were included: 68 healthy controls and 37 high-risk due to fetal growth restriction (FGR), chronic or gestational hypertension, and pre-eclampsia. Placental magnetic resonance imaging data were collected using a three-dimensional multi-echo radiofrequency-spoiled gradient-echo, and mean susceptibility and T2* values were calculated. To analyze associations and estimate BW, we employed linear regression and regression forest models.

Results: No significant differences were found in susceptibility between high-risk pregnancies and controls (p = 0.928). T2* values were significantly lower in high-risk pregnancies (p = 0.013), particularly in pre-eclampsia and FGR, emerging as a predictor of BW. The regression forest model showed placental T2* as a promising mode for BW estimation.

Conclusion: Our findings underscore the potential of mean placental T2* as a more sensitive marker for detecting placental dysfunction in high-risk pregnancies than mean placental susceptibility. Moreover, the high-risk status emerged as a significant predictor of BW. These results call for further research with larger and more diverse populations to validate these findings and enhance prediction models for improved pregnancy management.

Relevance statement: This study highlights the potential of placental T2* magnetic resonance imaging measurements as reliable indicators for detecting placental dysfunction in high-risk pregnancies, aiding in improved prenatal care and birth weight prediction.

Key points: Placental dysfunction in high-risk pregnancies is evaluated using MRI T2* values. Lower T2* values significantly correlate with pre-eclampsia and fetal growth restriction. T2* MRI may predict birth weight, enhancing prenatal care outcomes.