European Radiology最新文献

Background: Quantitative CT imaging, particularly iodine and calcium quantification, is an important CT-based biomarker.

Purpose: This study quantifies sources of errors in quantitative CT imaging in both single-energy and spectral CT.

Materials and methods: This work examines the theoretical relationship between CT numbers, linear attenuation coefficient, and material quantification. We derive four understandings: (1) CT numbers are not proportional with element mass in vivo, (2) CT numbers are proportional with element mass only when contained in a voxel of pure water, (3) iodine-water material decomposition is never accurate in vivo, and (4) for error-free material decomposition a voxel must only consist of the basis decomposition vectors. Misinterpretation-based errors are calculated using the National Institute of Standards and Technology (NIST) XCOM database for: tissue chemical compositions, clinical concentrations of hydroxyapatite (HAP), and iodine. Quantification errors are also demonstrated experimentally using phantoms.

Results: In single-energy CT, misinterpretation-induced errors for HAP density in adipose, muscle, lung, soft tissue, and blood ranged from 0-132%, i.e., a mass error of 0-749 mg/cm³. In spectral CT, errors with iodine in the same tissues resulted in a range of < 0.1-33% error, resulting in a mass error of < 0.1-1.2 mg/mL.

Conclusion: Our work demonstrates material quantification is fundamentally limited when measured in vivo due to measurement conditions differing from assumed and the errors are at or above detection limits for bone mineral density (BMD) and spectral iodine quantification. To define CT-derived biomarkers, the errors we demonstrate should either be avoided or built into uncertainty bounds.

Clinical relevance statement: Improving error bounds in quantitative CT biomarkers, specifically in iodine and BMD quantification, could lead to improvements in clinical care aspects based on quantitative CT.

Key points: CT numbers are only proportional with element mass only when contained in a voxel of pure water, therefore iodine-water material decomposition is never accurate in vivo. Misinterpretation-induced errors ranged from 0-132% for HAP density and < 0.1-33% in spectral CT with iodine. For error-free material decomposition, a voxel must only consist of the basis decomposition vectors.

Objectives: To perform a multi-reader comparison of multiparametric dual-energy computed tomography (DECT) images reconstructed with deep-learning image reconstruction (DLIR) and standard-of-care adaptive statistical iterative reconstruction-V (ASIR-V).

Methods: This retrospective study included 100 patients undergoing portal venous phase abdominal CT on a rapid kVp switching DECT scanner. Six reconstructed DECT sets (ASIR-V and DLIR, each at three strengths) were generated. Each DECT set included 65 keV monoenergetic, iodine, and virtual unenhanced (VUE) images. Using a Likert scale, three radiologists performed qualitative assessments for image noise, contrast, small structure visibility, sharpness, artifact, and image preference. Quantitative assessment was performed by measuring attenuation, image noise, and contrast-to-noise ratios (CNR). For the qualitative analysis, Gwet's AC2 estimates were used to assess agreement.

Results: DECT images reconstructed with DLIR yielded better qualitative scores than ASIR-V images except for artifacts, where both groups were comparable. DLIR-H images were rated higher than other reconstructions on all parameters (p-value < 0.05). On quantitative analysis, there was no significant difference in the attenuation values between ASIR-V and DLIR groups. DLIR images had higher CNR values for the liver and portal vein, and lower image noise, compared to ASIR-V images (p-value < 0.05). The subgroup analysis of patients with large body habitus (weight ≥ 90 kg) showed similar results to the study population. Inter-reader agreement was good-to-very good overall.

Conclusion: Multiparametric post-processed DECT datasets reconstructed with DLIR were preferred over ASIR-V images with DLIR-H yielding the highest image quality scores.

Clinical relevance statement: Deep-learning image reconstruction in dual-energy CT demonstrated significant benefits in qualitative and quantitative image metrics compared to adaptive statistical iterative reconstruction-V.

Key points: Dual-energy CT (DECT) images reconstructed using deep-learning image reconstruction (DLIR) showed superior qualitative scores compared to adaptive statistical iterative reconstruction-V (ASIR-V) reconstructed images, except for artifacts where both reconstructions were rated comparable. While there was no significant difference in attenuation values between ASIR-V and DLIR groups, DLIR images showed higher contrast-to-noise ratios (CNR) for liver and portal vein, and lower image noise (p value < 0.05). Subgroup analysis of patients with large body habitus (weight ≥ 90 kg) yielded similar findings to the overall study population.

Purpose: Compare the safety and efficacy of polyvinyl alcohol particles (PVA) versus trisacryl gelatin microspheres (Embospheres) versus hydrogel microspheres coated with polyzene-F (Embozenes) for prostatic artery embolization (PAE) to treat patients with benign prostatic hyperplasia (BPH).

Materials and methods: A single-center prospective cohort study from 2019 to 2023, including patients with international prostate symptom score (IPSS) ≥ 15 and/or quality of life score (QoL) ≥ 4. Allocation to embolic agents was performed chronologically: 100-300 µm PVA (n = 53), followed by 300-500 µm Embospheres (n = 50), and finally, 400 µm Embozenes (n = 50). All patients were evaluated at baseline and at 1 and 6 months after PAE with IPSS/QoL; peak urinary flow rate, post-void residual volume, and prostate volume with ultrasound and prostate-specific antigen. Adverse events and the need for prostatic re-interventions were assessed.

Results: There were no significant baseline differences between the three groups except for patient age (62.5 years PVA; 66.1 years Embospheres and 66.6 years Embozenes; p = 0.019). There were no major adverse events and no differences between groups regarding minor adverse events. All outcome measures improved significantly from baseline, with no significant differences between groups. Mean ± standard deviation IPSS/QoL improvement at 6 months: -10.7 ± 7.9/-2.2 ± 1.7 PVA; -10.4 ± 7.3/-2.0 ± 1.5 Embospheres; -10.4 ± 7.0/-2.2 ± 1.6 Embozenes (p = 0.987). Re-intervention rates after 6 months: 9% (n = 5/53) PVA; 14% (n = 7/50) Embospheres; 8% (n = 4/50) Embozenes (p = 0.591).

Conclusions: PAE with PVA particles, Embospheres, and Embozenes is equally safe and effective in treating BPH-related lower urinary tract symptoms.

Clinical relevance statement: This is the first prospective study showing equivalence between the most frequently used embolic agents for prostatic artery embolization.

Key points: Different particles can be used interchangeably for prostatic artery embolization. The improvements in measured metrics were the same between groups, with no differences in adverse events. The need for prostatic medication and re-intervention rates were the same at 1 and 6 months after embolization.

Objectives: The International Society of Paediatric Oncology-Renal Tumour Study Group (SIOP-RTSG) discourages invasive procedures to determine the histology of paediatric renal neoplasms at diagnosis. Therefore, the histological subtype of Wilms' tumours (WT) is unknown at the start of neoadjuvant chemotherapy. MR-DWI shows potential value as a non-invasive biomarker through apparent diffusion coefficients (ADCs). This study aimed to describe MR characteristics and ADC values of paediatric renal tumours to differentiate subtypes.

Materials and methods: Children with a renal tumour undergoing surgery within the SIOP-RTSG 2016-UMBRELLA protocol were prospectively included between May 2021 and 2023. In the case of a total nephrectomy, a patient-specific cutting guide based on the neoadjuvant MR was 3D-printed, allowing a correlation between imaging and histopathology. Whole-tumour volumes and ADC values were statistically compared with the Mann-Whitney U-test. Direct correlation on the microscopic slide level was analysed through mixed model analysis.

Results: Fifty-nine lesions of 54 patients (58% male, median age 3.0 years (range 0-17.7 years)) were included. Forty-four lesions involved a WT. Stromal type WT showed the lowest median decrease in volume after neoadjuvant chemotherapy (48.1 cm³, range 561.5-(+)332.7 cm³, p = 0.035). On a microscopic slide level (n = 240 slides) after direct correlation through the cutting guide, stromal areas showed a significantly higher median ADC value compared to epithelial and blastemal foci (p < 0.001). With a cut-off value of 1.195 * 10^-3 mm²/s, sensitivity, and specificity were 95.2% (95% confidence interval 87.6-98.4%) and 90.5% (95% confidence interval 68.2-98.3%), respectively.

Conclusion: Correlation between histopathology and MR-DWI through a patient-specific 3D-printed cutting guide resulted in significant discrimination of stromal type WT from epithelial and blastemal subtypes.

Clinical relevance statement: Stromal Wilms' tumours could be discriminated from epithelial- and blastemal lesions based on high apparent diffusion coefficient values and limited decrease in volume after neoadjuvant chemotherapy. This may aid in future decision-making, especially concerning discrimination between low- and high-risk neoplasms.

Key points: MR-DWI shows potential value as a non-invasive biomarker in paediatric renal tumours. The patient-specific cutting guide leads to a correlation between apparent diffusion coefficient values and Wilms' tumour subtype. Stromal areas could be discriminated from epithelial and blastemal foci in Wilms' tumours based on apparent diffusion coefficient values.

Objectives: To measure dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) biomarker repeatability in patients with non-small cell lung cancer (NSCLC). To use these statistics to identify which individual target lesions show early biological response.

Materials and methods: A single-centre, prospective DCE-MRI study was performed between September 2015 and April 2017. Patients with NSCLC were scanned before standard-of-care radiotherapy to evaluate biomarker repeatability and two weeks into therapy to evaluate biological response. Volume transfer constant (K^trans), extravascular extracellular space volume fraction (v_e) and plasma volume fraction (v_p) were measured at each timepoint along with tumour volume. Repeatability was assessed using a within-subject coefficient of variation (wCV) and repeatability coefficient (RC). Cohort treatment effects on biomarkers were estimated using mixed-effects models. RC limits of agreement revealed which individual target lesions changed beyond that expected with biomarker daily variation.

Results: Fourteen patients (mean age, 67 years +/- 12, 8 men) had 22 evaluable lesions (12 primary tumours, 8 nodal metastases, 2 distant metastases). The wCV (in 8/14 patients) was between 9.16% to 17.02% for all biomarkers except for v_p, which was 42.44%. Cohort-level changes were significant for K^trans and v_e (p < 0.001) and tumour volume (p = 0.002). K^trans and tumour volume consistently showed the greatest number of individual lesions showing biological response. In distinction, no individual lesions had a real change in v_e despite the cohort-level change.

Conclusion: Identifying individual early biological responders provided additional information to that derived from conventional cohort cohort-level statistics, helping to prioritise which parameters would be best taken forward into future studies.

Clinical relevance statement: Dynamic contrast-enhanced magnetic resonance imaging biomarkers K^trans and tumour volume are repeatable and detect early treatment-induced changes at both cohort and individual lesion levels, supporting their use in further evaluation of radiotherapy and targeted therapeutics.

Key points: Few literature studies report quantitative imaging biomarker precision, by measuring repeatability or reproducibility. Several DCE-MRI biomarkers of lung cancer tumour microenvironment were highly repeatable. Repeatability coefficient measurements enabled lesion-specific evaluation of early biological response to therapy, improving conventional assessment.

Objectives: 3D phase-resolved functional lung (PREFUL) MRI offers evaluation of pulmonary ventilation without inhalation of contrast agent. This study seeks to compare ventilation maps obtained from 3D PREFUL MRI with a direct ventilation measurement derived from ¹²⁹Xe MRI in both patients with chronic obstructive pulmonary disease (COPD) and healthy volunteers.

Methods: Thirty-one patients with COPD and 12 healthy controls underwent free-breathing 3D PREFUL MRI and breath-hold ¹²⁹Xe MRI at 1.5 T. For both MRI techniques, ventilation defect (VD) maps were determined and respective ventilation defect percentage (VDP) values were computed. All parameters of both techniques were compared by Spearman correlation coefficient (r) and the differences between VDP values were quantified by Bland-Altman analysis and tested for significance using Wilcoxon signed-rank test. In a regional comparison of VD maps, spatial overlap and Sørensen-Dice coefficients of healthy and defect areas were computed.

Results: On a global level, all 3D PREFUL VDP values correlated significantly to VDP measure derived by ¹²⁹Xe ventilation imaging (all r > 0.65; all p < 0.0001). ¹²⁹Xe VDP was significantly greater than 3D PREFUL derived VDP_RVent (mean bias = 10.5%, p < 0.001) and VDP_FVL-CM (mean bias = 11.3%, p < 0.0001) but not for VDP_Combined (mean bias = 1.7%, p = 0.70). The total regional agreement of ¹²⁹Xe and 3D PREFUL VD maps ranged between 60% and 63%.

Conclusions: Free-breathing 3D PREFUL MRI showed a strong correlation with breath-hold hyperpolarized ¹²⁹Xe MRI regarding the VDP values and modest differences in the detection of VDs on a regional level.

Clinical relevance statement: 3D PREFUL MRI correlated with ¹²⁹Xe MRI, unveiling regional differences in COPD defect identification. This proposes 3D PREFUL MRI as a ventilation mapping surrogate, eliminating the need for extra hardware or inhaled gases.

Key points: Current non-invasive evaluation techniques for lung diseases have drawbacks; ¹²⁹Xe MRI is limited by cost and availability. 3D PREFUL MRI correlated with ¹²⁹Xe MRI, with regional differences in identifying COPD defects. 3D PREFUL MRI can provide ventilation mapping without the need for additional hardware or inhaled gases.

Objectives: To investigate the use of the score-based diffusion model to accelerate breast MRI reconstruction.

Materials and methods: We trained a score-based model on 9549 MRI examinations of the female breast and employed it to reconstruct undersampled MRI images with undersampling factors of 2, 5, and 20. Images were evaluated by two experienced radiologists who rated the images based on their overall quality and diagnostic value on an independent test set of 100 additional MRI examinations.

Results: The score-based model produces MRI images of high quality and diagnostic value. Both T1- and T2-weighted MRI images could be reconstructed to a high degree of accuracy. Two radiologists rated the images as almost indistinguishable from the original images (rating 4 or 5 on a scale of 5) in 100% (radiologist 1) and 99% (radiologist 2) of cases when the acceleration factor was 2. This fraction dropped to 88% and 70% for an acceleration factor of 5 and to 5% and 21% with an extreme acceleration factor of 20.

Conclusion: Score-based models can reconstruct MRI images at high fidelity, even at comparatively high acceleration factors, but further work on a larger scale of images is needed to ensure that diagnostic quality holds.

Clinical relevance statement: The number of MRI examinations of the breast is expected to rise with MRI screening recommended for women with dense breasts. Accelerated image acquisition methods can help in making this examination more accessible.

Key points: Accelerating breast MRI reconstruction remains a significant challenge in clinical settings. Score-based diffusion models can achieve near-perfect reconstruction for moderate undersampling factors. Faster breast MRI scans with maintained image quality could revolutionize clinic workflows and patient experience.

Objectives: Fat-signal suppression is essential for breast diffusion magnetic resonance imaging (or diffusion-weighted MRI, DWI) as the very low diffusion coefficient of fat tends to decrease absolute diffusion coefficient (ADC) values. Among several methods, the STIR (short-tau inversion recovery) method is a popular approach, but signal suppression/attenuation is not specific to fat contrary to other methods such as SPAIR (spectral adiabatic (or attenuated) inversion recovery). This article focuses on those two techniques to illustrate the importance of appropriate fat suppression in breast DWI, briefly presenting the pros and cons of both approaches.

Methods and results: We show here through simulation and data acquired in a dedicated breast DWI phantom made of vials with water and various concentrations of polyvinylpyrrolidone (PVP) how ADC values obtained with STIR DWI may be biased toward tissue components with the longest T1 values: ADC values obtained with STIR fat suppression may be over/underestimated depending on the T1 and ADC profile within tissues. This bias is also illustrated in two clinical examples.

Conclusion: Fat-specific methods should be preferred over STIR for fat-signal suppression in breast DWI, such as SPAIR which also provides a higher sensitivity than STIR for lesion detection. One should remain aware, however, that efficient fat-signal suppression with SPAIR requires good B0 shimming to avoid ADC underestimation from residual fat contamination.

Clinical relevance statement: The spectral adiabatic (or attenuated) inversion recovery (SPAIR) method should be preferred over short-tau inversion recovery (STIR) for fat suppression in breast DWI.

Key points: Fat-signal suppression is essential for breast DWI; the SPAIR method is recommended. Short-tau inversion recovery (STIR) is not specific to fat; as a result, SNR is decreased and ADC values may be over- or underestimated. The STIR fat-suppression method must not be used after the injection of gadolinium-based contrast agents.