Investigative Radiology最新文献

Objectives: Image acquisition in ultra-high-resolution (UHR) scan mode does not impose a dose penalty in photon-counting CT (PCCT). This study aims to investigate the dose saving potential of using UHR instead of standard-resolution PCCT for lumbar spine imaging.

Materials and methods: Eight cadaveric specimens were examined with 7 dose levels (5-35 mGy) each in UHR (120 × 0.2 mm) and standard-resolution acquisition mode (144 × 0.4 mm) on a first-generation PCCT scanner. The UHR images were reconstructed with 3 dedicated bone kernels (Br68 [spatial frequency at 10% of the modulation transfer function 14.5 line pairs/cm], Br76 [21.0], and Br84 [27.9]), standard-resolution images with Br68 and Br76. Using automatic segmentation, contrast-to-noise ratios (CNRs) were established for lumbar vertebrae and psoas muscle tissue. In addition, image quality was assessed subjectively by 19 independent readers (15 radiologists, 4 surgeons) using a browser-based forced choice comparison tool totaling 16,974 performed pairwise tests. Pearson's correlation coefficient ( r ) was used to analyze the relationship between CNR and subjective image quality rankings, and Kendall W was calculated to assess interrater agreement.

Results: Irrespective of radiation exposure level, CNR was higher in UHR datasets than in standard-resolution images postprocessed with the same reconstruction parameters. The use of sharper convolution kernels entailed lower CNR but higher subjective image quality depending on radiation dose. Subjective assessment revealed high interrater agreement ( W = 0.86; P < 0.001) with UHR images being preferred by readers in the majority of comparisons on each dose level. Substantial correlation was ascertained between CNR and the subjective image quality ranking (all r 's ≥ 0.95; P < 0.001).

Conclusions: In PCCT of the lumbar spine, UHR mode's smaller pixel size facilitates a considerable CNR increase over standard-resolution imaging, which can either be used for dose reduction or higher spatial resolution depending on the selected convolution kernel.

Background: Intra-articular injections are routinely used for conservative treatment of knee osteoarthritis (OA). The detailed comparative therapeutic effects of these injections on cartilage tissue are still unclear.

Objective: The aim of this study was to detect and compare knee cartilage changes after intra-articular injection of glucocorticoid, hyaluronic acid, or platelet-rich plasma (PRP) to placebo using quantitative (T2 and T2* mapping) and morphological magnetic resonance imaging parameters in patients with mild or moderate osteoarthritis.

Materials and methods: In a double-blinded, placebo-controlled, single-center trial, knees with mild or moderate osteoarthritis (Kellgren-Lawrence grade 1-3) were randomly assigned to an intra-articular injection with 1 of these substances: glucocorticoid, hyaluronic acid, PRP, or placebo. Cartilage degeneration on baseline and follow-up magnetic resonance imaging scans (after 3 and 12 months) was assessed by 2 readers using quantitative T2 and T2* times (milliseconds) and morphological parameters (modified Outerbridge grading, subchondral bone marrow edema, subchondral cysts, osteophytes).

Results: One hundred twenty knees (30 knees per treatment group) were analyzed with a median patient age of 60 years (interquartile range, 54.0-68.0 years). Interreader reliability was good for T2 (ICC, 0.76; IQR, 0.68-0.83) and T2* (ICC, 0.83; IQR, 0.76-0.88) measurements. Morphological parameters showed no significant changes between all groups after 3 and 12 months. T2 mapping after 12 months showed the following significant ( P = 0.001-0.03) changes between groups in 6 of 14 compartments: values after PRP injection decreased compared with glucocorticoid in 4 compartments (complete medial femoral condyle and central part of lateral condyle) and compared with placebo in 2 compartments (anterior and central part of medial tibial plateau); values after glucocorticoid injection decreased compared with placebo in 1 compartment (central part of medial tibial plateau). No significant changes were seen for T2 and T2* times after 3 months and T2* times after 12 months. No correlation was found between T2/T2* times and Kellgren-Lawrence grade, age, body mass index, or pain (Spearman ρ, -0.23 to 0.18).

Conclusions: Platelet-rich plasma injection has a positive long-term effect on cartilage quality in the medial femoral compartment compared to glucocorticoid, resulting in significantly improved T2 values after 12 months. For morphological cartilage parameters, injections with glucocorticoid, PRP, or hyaluronic acid showed no better effect in the short or long term compared with placebo.

Objectives: The aim of this study was to clinically validate a fully automated AI model for magnetic resonance imaging (MRI)-based quantifications of lumbar spinal canal stenosis.

Materials and methods: This retrospective study included lumbar spine MRI of 100 consecutive clinical patients (56 ± 17 years; 43 females, 57 males) performed on clinical 1.5 (51 examinations) and 3 T MRI scanners (49 examinations) with heterogeneous clinical imaging protocols. The AI model performed segmentations of the thecal sac on axial T2-weighted sequences. Based on these segmentations, the anteroposterior (AP) and mediolateral (ML) distance, and the area of the thecal sac were measured in a fully automated manner. For comparison, 2 fellowship-trained musculoskeletal radiologists performed the same segmentations and measurements independently. Statistics included 1-sample t tests, the intraclass correlation coefficient (ICC), Bland-Altman plots, and Dice coefficients. A P value of <0.05 was considered statistically significant.

Results: The average measurements of the AI model, reader 1, and reader 2 were 194 ± 72 mm 2 , 181 ± 71 mm 2 , and 179 ± 70 mm 2 for thecal sac area, 13 ± 3.3 mm, 12.6 ± 3.3 mm, and 12.6 ± 3.2 mm for AP distance, and 19.5 ± 3.9 mm, 20 ± 4.3 mm, and 19.4 ± 4 mm for ML distance, respectively. Significant differences existed for all pairwise comparisons, besides reader 1 versus AI model for the ML distance and reader 1 versus reader 2 for the AP distance ( P = 0.1 and P = 0.21, respectively). The pairwise mean absolute errors among reader 1, reader 2, and the AI model ranged from 0.59 mm and 0.75 mm for the AP distance, from 1.16 mm to 1.37 mm for the ML distance, and from 7.9 mm 2 to 15.54 mm 2 for the thecal sac area. Pairwise ICCs among reader 1, reader 2, and the AI model ranged from 0.91 and 0.94 for the AP distance and from 0.86 to 0.9 for the ML distance without significant differences. For the thecal sac area, the pairwise ICC between both readers and the AI model of 0.97 each was slightly, but significantly lower than the ICC between reader 1 and reader 2 of 0.99. Similarly, the Dice coefficient and Hausdorff distance between both readers and the AI model were significantly lower than the values between reader 1 and reader 2, overall ranging from 0.93 to 0.95 for the Dice coefficients and 1.1 to 1.44 for the Hausdorff distances.

Conclusions: The investigated AI model is reliable for assessing the AP and the ML thecal sac diameters with human level accuracies. The small differences for measurement and segmentation of the thecal sac area between the AI model and the radiologists are likely within a clinically acceptable range.

Objectives: Unexpected accumulations of gadolinium in various organs were reported after the administration of gadolinium-based contrast agents, making desirable to reduce the dose while maintaining equivalent diagnostic performance. The aim of this study was to evaluate the contrast enhancement performance of high relaxivity gadopiclenol compared with gadoterate meglumine in abdominal contrast-enhanced magnetic resonance angiography (CE-MRA).

Materials and methods: In a first study in healthy rabbits, axial 3D gradient echo sequences were applied at 4.7 T to study arterial enhancement as a function of gadopiclenol dose (0.025, 0.05, 0.075, and 0.1 mmol Gd/kg) or gadoterate meglumine at 0.1 mmol Gd/kg (n = 5-6/group). The increase in signal-to-noise ratio (ΔSNR) in the aorta at the first pass was measured and compared. In a second, crossover study in 6 healthy pigs, abdominal CE-MRA sequences were acquired at 3 T with gadopiclenol at 0.05 mmol Gd/kg or gadoterate meglumine at 0.1 mmol Gd/kg at a 1-week interval. Quantitatively on the maximum intensity projection (MIP) images, the mean MIP SNR within the aorta of both groups was compared. Qualitatively, a blinded comparison of the angiograms was performed by an experienced radiologist to determine the preferred contrast agent.

Results: In the rabbit, ∆SNR is linearly correlated with the gadopiclenol dose ( P = 0.0010). Compared with gadoterate meglumine 0.1 mmol Gd/kg, an increase in the ∆SNR is observed after 0.05, 0.075, and 0.1 mmol Gd/kg of gadopiclenol (+63% P = 0.0731, +78% P = 0.0081, and +72% P = 0.0773, respectively), whereas at 0.025 mmol Gd/kg, ∆SNR is in the same range as with gadoterate meglumine 0.1 mmol Gd/kg (+15% P > 0.9999). In pigs, contrast enhancement after gadopiclenol at 0.05 mmol/kg is +22% superior to MIP SNR after gadoterate meglumine at 0.1 mmol Gd/kg ( P = 0.3095). Qualitatively, a preference was shown for gadopiclenol images (3/6) over the gadoterate meglumine examinations (1/6), with no preference being shown for the remainder (2/6).

Conclusions: First-pass CE-MRA is feasible with gadopiclenol at 0.05 mmol Gd/kg with at least the same arterial signal enhancement and image quality as gadoterate meglumine at 0.1 mmol Gd/kg.

Objectives: Accurately acquiring and assigning different contrast-enhanced phases in computed tomography (CT) is relevant for clinicians and for artificial intelligence orchestration to select the most appropriate series for analysis. However, this information is commonly extracted from the CT metadata, which is often wrong. This study aimed at developing an automatic pipeline for classifying intravenous (IV) contrast phases and additionally for identifying contrast media in the gastrointestinal tract (GIT).

Materials and methods: This retrospective study used 1200 CT scans collected at the investigating institution between January 4, 2016 and September 12, 2022, and 240 CT scans from multiple centers from The Cancer Imaging Archive for external validation. The open-source segmentation algorithm TotalSegmentator was used to identify regions of interest (pulmonary artery, aorta, stomach, portal/splenic vein, liver, portal vein/hepatic veins, inferior vena cava, duodenum, small bowel, colon, left/right kidney, urinary bladder), and machine learning classifiers were trained with 5-fold cross-validation to classify IV contrast phases (noncontrast, pulmonary arterial, arterial, venous, and urographic) and GIT contrast enhancement. The performance of the ensembles was evaluated using the receiver operating characteristic area under the curve (AUC) and 95% confidence intervals (CIs).

Results: For the IV phase classification task, the following AUC scores were obtained for the internal test set: 99.59% [95% CI, 99.58-99.63] for the noncontrast phase, 99.50% [95% CI, 99.49-99.52] for the pulmonary-arterial phase, 99.13% [95% CI, 99.10-99.15] for the arterial phase, 99.8% [95% CI, 99.79-99.81] for the venous phase, and 99.7% [95% CI, 99.68-99.7] for the urographic phase. For the external dataset, a mean AUC of 97.33% [95% CI, 97.27-97.35] and 97.38% [95% CI, 97.34-97.41] was achieved for all contrast phases for the first and second annotators, respectively. Contrast media in the GIT could be identified with an AUC of 99.90% [95% CI, 99.89-99.9] in the internal dataset, whereas in the external dataset, an AUC of 99.73% [95% CI, 99.71-99.73] and 99.31% [95% CI, 99.27-99.33] was achieved with the first and second annotator, respectively.

Conclusions: The integration of open-source segmentation networks and classifiers effectively classified contrast phases and identified GIT contrast enhancement using anatomical landmarks.

Objective: Development of spectral photon-counting computed tomography (SPCCT) for ultra-high-resolution coronary CT angiography (CCTA) has the potential to accurately evaluate the coronary arteries of very-high-risk patients. The aim of this study was to compare the diagnostic performances of SPCCT against conventional CT for quantifying coronary stenosis in very-high-risk patients, with invasive coronary angiography (ICA) as the reference method.

Materials and methods: In this prospective institutional review board-approved study, very-high-risk patients addressed for ICA following an acute coronary syndrome were consecutively included. CCTA was performed for each patient with both SPCCT and conventional CT before ICA within 3 days. Stenoses were assessed using the minimal diameter over proximal and distal diameters method for CCTA and the quantitative coronary angiography method for ICA. Intraclass correlation coefficients and mean errors were assessed. Sensitivity and specificity were calculated for a >50% diameter stenosis threshold. Reclassification rates for conventional CT and SPCCT were assessed according to CAD-RADS 2.0, using ICA as the gold standard.

Results: Twenty-six coronary stenoses were identified in 26 patients (4 women [15%]; age 64 ± 8 years) with 19 (73%) above 50% and 9 (35%) equal or above 70%. The median stenosis value was 64% (interquartile range, 48%-73%). SPCCT showed a lower mean error (6% [5%, 8%]) than conventional CT (12% [9%, 16%]). SPCCT demonstrated greater sensitivity (100%) and specificity (90%) than conventional CT (75% and 50%, respectively). Ten (38%) stenoses were reclassified with SPCCT and one (4%) with conventional CT.

Conclusions: In very-high-risk patients, ultra-high-resolution SPCCT coronary angiography showed greater accuracy, sensitivity, and specificity, and led to more stenosis reclassifications than conventional CT.

Abstract: Invasive open surgery used to be compulsory to access tumor mass to perform excision or resection. Development of minimally invasive laparoscopic procedures followed, as well as catheter-based approaches, such as stenting, endovascular surgery, chemoembolization, brachytherapy, which minimize side effects and reduce the risks to patients. Completely noninvasive procedures bring further benefits in terms of reducing risk, procedure time, recovery time, potential of infection, or other side effects. Focusing ultrasound waves from the outside of the body specifically at the disease site has proven to be a safe noninvasive approach to localized ablative hyperthermia, mechanical ablation, and targeted drug delivery. Focused ultrasound as a medical intervention was proposed decades ago, but it only became feasible to plan, guide, monitor, and control the treatment procedures with advanced radiological imaging capabilities. The purpose of this review is to describe the imaging capabilities and approaches to perform these tasks, with the emphasis on magnetic resonance imaging and ultrasound. Some procedures already are in clinical practice, with more at the clinical trial stage. Imaging is fully integrated in the workflow and includes the following: (1) planning, with definition of the target regions and adjacent organs at risk; (2) real-time treatment monitoring via thermometry imaging, cavitation feedback, and motion control, to assure targeting and safety to adjacent normal tissues; and (3) evaluation of treatment efficacy, via assessment of ablation and physiological parameters, such as blood supply. This review also focuses on sonosensitive microparticles and nanoparticles, such as microbubbles injected in the bloodstream. They enable ultrasound energy deposition down to the microvascular level, induce vascular inflammation and shutdown, accelerate clot dissolution, and perform targeted drug delivery interventions, including focal gene delivery. Especially exciting is the ability to perform noninvasive drug delivery via opening of the blood-brain barrier at the desired areas within the brain. Overall, focused ultrasound under image guidance is rapidly developing, to become a choice noninvasive interventional radiology tool to treat disease and cure patients.

Abstract: Recent technological advancements have revolutionized routine brain magnetic resonance imaging (MRI) sequences, offering enhanced diagnostic capabilities in intracranial disease evaluation. This review explores 2 pivotal breakthrough areas: deep learning reconstruction (DLR) and quantitative MRI techniques beyond conventional structural imaging. DLR using deep neural networks facilitates accelerated imaging with improved signal-to-noise ratio and spatial resolution, enhancing image quality with short scan times. DLR focuses on supervised learning applied to clinical implementation and applications. Quantitative MRI techniques, exemplified by 2D multidynamic multiecho, 3D quantification using interleaved Look-Locker acquisition sequences with T2 preparation pulses, and magnetic resonance fingerprinting, enable precise calculation of brain-tissue parameters and further advance diagnostic accuracy and efficiency. Potential DLR instabilities and quantification and bias limitations will be discussed. This review underscores the synergistic potential of DLR and quantitative MRI, offering prospects for improved brain imaging beyond conventional methods.

Objectives: The aim of this study was to compare the performances of photon-counting detector computed tomography (PCD-CT) and energy-integrating detector computed tomography (EID-CT) for visualizing nodules and airways in human cadaveric lungs.

Materials and methods: Previously obtained 20 cadaveric lungs were scanned, and images were prospectively acquired by EID-CT and PCD-CT at a radiation dose with a noise level equivalent to the diagnostic reference level. PCD-CT was scanned with ultra-high-resolution mode. The EID-CT images were reconstructed with a 512 matrix, 0.6-mm thickness, and a 350-mm field of view (FOV). The PCD-CT images were reconstructed at 3 settings: PCD-512: same as EID-CT; PCD-1024-FOV350: 1024 matrix, 0.2-mm thickness, 350-mm FOV; and PCD-1024-FOV50: 1024 matrix, 0.2-mm thickness, 50-mm FOV. Two specimens per lung were examined after hematoxylin and eosin staining. The CT images were evaluated for nodules on a 5-point scale and for airways on a 4-point scale to compare the histology. The Wilcoxon signed rank test with Bonferroni correction was performed for statistical analyses.

Results: Sixty-seven nodules (1321 μm; interquartile range [IQR], 758-3105 μm) and 92 airways (851 μm; IQR, 514-1337 μm) were evaluated. For nodules and airways, scores decreased in order of PCD-1024-FOV50, PCD-1024-FOV350, PCD-512, and EID-CT. Significant differences were observed between series other than PCD-1024-FOV350 versus PCD-1024-FOV50 for nodules (PCD-1024-FOV350 vs PCD-1024-FOV50, P = 0.063; others P < 0.001) and between series other than EID-CT versus PCD-512 for airways (EID-CT vs PCD-512, P = 0.549; others P < 0.005). On PCD-1024-FOV50, the median size of barely detectable nodules was 604 μm (IQR, 469-756 μm) and that of barely detectable airways was 601 μm (IQR, 489-929 μm). On EID-CT, that of barely detectable nodules was 837 μm (IQR, 678-914 μm) and that of barely detectable airways was 1210 μm (IQR, 674-1435 μm).

Conclusions: PCD-CT visualized small nodules and airways better than EID-CT and improved with high spatial resolution and potentially can detect submillimeter nodules and airways.