CT-Guided Liver Biopsy: Evaluation of Spectral Data From Dual-Layer Detector CT for Improved Lesion Detection.

PURPOSE: Evaluation of dual-layer spectral computed tomography (CT) for contrast enhancement during image-guided biopsy of liver lesions using virtual monoenergetic images (VMI) and virtual non-contrast (VNC) images. METHODS: Spectral CT data of 20 patients receiving CT-guided needle biopsy of focal liver lesions were used to generate VMI at energy levels from 40 to 200 keV and VNC images. Images were analyzed objectively regarding contrast-to-noise ratio between lesion center (CNRcent) or periphery (CNRperi) and normal liver parenchyma. Lesion visibility and image quality were evaluated on a 4-point Likert scale by two radiologists. RESULTS: Using VMI/VNC images, readers reported an increased visibility of the lesion compared to the conventional CT images in 18/20 cases. In 75% of cases, the highest visibility was derived by VMI-40. Showing all reconstructions simultaneously, VMI-40 offered the highest visibility in 75% of cases, followed by VNC in 12.5% of cases. Either CNRcent (17/20) or/and CNRperi (17/20) was higher (CNR increase > 50%) in 19/20 cases for VMI-40 or VNC images compared to conventional CT images. VMI-40 showed the highest CNRcent in 14 cases and the highest CNRperi in 12 cases. High image quality was present for all reconstructions with a minimum median of 3.5 for VMI-40 and VMI-50. CONCLUSIONS: When implemented in the CT scanner software, automated contrast enhancement of liver lesions during image-guided biopsy may facilitate the procedure.

Prospectively Accelerated T2-Weighted Imaging of the Prostate by Combining Compressed SENSE and Deep Learning in Patients with Histologically Proven Prostate Cancer.

BACKGROUND: To assess the performance of prospectively accelerated and deep learning (DL) reconstructed T2-weighted (T2w) imaging in volunteers and patients with histologically proven prostate cancer (PCa). METHODS: Prospectively undersampled T2w datasets were acquired with acceleration factors of 1.7 (reference), 3.4 and 4.8 in 10 healthy volunteers and 23 patients with histologically proven PCa. Image reconstructions using compressed SENSE (C-SENSE) and a combination of C-SENSE and DL-based artificial intelligence (C-SENSE AI) were analyzed. Qualitative image comparison was performed using a 6-point Likert scale (overall image quality, noise, motion artifacts, lesion detection, diagnostic certainty); the T2 and PI-RADS scores were compared between the two reconstructions. Additionally, quantitative image parameters were assessed (apparent SNR, apparent CNR, lesion size, line profiles). RESULTS: All C-SENSE AI-reconstructed images received a significantly higher qualitative rating compared to the C-SENSE standard images. Analysis of the quantitative parameters supported this finding, with significantly higher aSNR and aCNR. The line profiles demonstrated a significantly steeper signal change at the border of the prostatic lesion and the adjacent normal tissue in the C-SENSE AI-reconstructed images, whereas the T2 and PI-RADS scores as well as the lesion size did not differ. CONCLUSION: In this prospective study, we demonstrated the clinical feasibility of a novel C-SENSE AI reconstruction enabling a 58% acceleration in T2w imaging of the prostate while obtaining significantly better image quality.

X-ray Dark-Field CT for Early Detection of Radiation-induced Lung Injury in a Murine Model.

Online supplemental material is available for this article.

X-ray dark-field chest imaging for detection and quantification of emphysema in patients with chronic obstructive pulmonary disease: a diagnostic accuracy study.

BACKGROUND: Although advanced medical imaging technologies give detailed diagnostic information, a low-dose, fast, and inexpensive option for early detection of respiratory diseases and follow-ups is still lacking. The novel method of x-ray dark-field chest imaging might fill this gap but has not yet been studied in living humans. Enabling the assessment of microstructural changes in lung parenchyma, this technique presents a more sensitive alternative to conventional chest x-rays, and yet requires only a fraction of the dose applied in CT. We studied the application of this technique to assess pulmonary emphysema in patients with chronic obstructive pulmonary disease (COPD). METHODS: In this diagnostic accuracy study, we designed and built a novel dark-field chest x-ray system (Technical University of Munich, Munich, Germany)-which is also capable of simultaneously acquiring a conventional thorax radiograph (7 s, 0·035 mSv effective dose). Patients who had undergone a medically indicated chest CT were recruited from the department of Radiology and Pneumology of our site (Klinikum rechts der Isar, Technical University of Munich, Munich, Germany). Patients with pulmonary pathologies, or conditions other than COPD, that might influence lung parenchyma were excluded. For patients with different disease stages of pulmonary emphysema, x-ray dark-field images and CT images were acquired and visually assessed by five readers. Pulmonary function tests (spirometry and body plethysmography) were performed for every patient and for a subgroup of patients the measurement of diffusion capacity was performed. Individual patient datasets were statistically evaluated using correlation testing, rank-based analysis of variance, and pair-wise post-hoc comparison. FINDINGS: Between October, 2018 and December, 2019 we enrolled 77 patients. Compared with CT-based parameters (quantitative emphysema ρ=-0·27, p=0·089 and visual emphysema ρ=-0·45, p=0·0028), the dark-field signal (ρ=0·62, p<0·0001) yields a stronger correlation with lung diffusion capacity in the evaluated cohort. Emphysema assessment based on dark-field chest x-ray features yields consistent conclusions with findings from visual CT image interpretation and shows improved diagnostic performance than conventional clinical tests characterising emphysema. Pair-wise comparison of corresponding test parameters between adjacent visual emphysema severity groups (CT-based, reference standard) showed higher effect sizes. The mean effect size over the group comparisons (absent-trace, trace-mild, mild-moderate, and moderate-confluent or advanced destructive visual emphysema grades) for the COPD assessment test score is 0·21, for forced expiratory volume in 1 s (FEV1)/functional vital capacity is 0·25, for FEV1% of predicted is 0·23, for residual volume % of predicted is 0·24, for CT emphysema index is 0·35, for dark-field signal homogeneity within lungs is 0·38, for dark-field signal texture within lungs is 0·38, and for dark-field-based emphysema severity is 0·42. INTERPRETATION: X-ray dark-field chest imaging allows the diagnosis of pulmonary emphysema in patients with COPD because this technique provides relevant information representing the structural condition of lung parenchyma. This technique might offer a low radiation dose alternative to CT in COPD and potentially other lung disorders. FUNDING: European Research Council, Deutsche Forschungsgemeinschaft, Royal Philips, and Karlsruhe Nano Micro Facility.

In-vivo X-ray dark-field computed tomography for the detection of radiation-induced lung damage in mice.

BACKGROUND AND PURPOSE: Radiotherapy of thoracic tumours can lead to side effects in the lung, which may benefit from early diagnosis. We investigated the potential of X-ray dark-field computed tomography by a proof-of-principle murine study in a clinically relevant radiotherapeutic setting aiming at the detection of radiation-induced lung damage. MATERIAL AND METHODS: Six mice were irradiated with 20 Gy to the entire right lung. Together with five unirradiated control mice, they were imaged using computed tomography with absorption and dark-field contrast before and 16 weeks post irradiation. Mean pixel values for the right and left lung were calculated for both contrasts, and the right-to-left-ratio R of these means was compared. Radiologists also assessed the tomograms acquired 16 weeks post irradiation. Sensitivity, specificity, inter- and intra-reader accuracy were evaluated. RESULTS: In absorption contrast the group-average of R showed no increase in the control group and increased by 7% (p = 0.005) in the irradiated group. In dark-field contrast, it increased by 2% in the control group and by 14% (p = 0.005) in the irradiated group. Specificity was 100% for both contrasts but sensitivity was almost four times higher using dark-field tomography. Two cases were missed by absorption tomography but were detected by dark-field tomography. CONCLUSIONS: The applicability of X-ray dark-field computed tomography for the detection of radiation-induced lung damage was demonstrated in a pre-clinical mouse model. The presented results illustrate the differences between dark-field and absorption contrast and show that dark-field tomography could be advantageous in future clinical settings.

X-ray Dark-Field Chest Imaging: Qualitative and Quantitative Results in Healthy Humans.

Background X-ray dark-field radiography takes advantage of the wave properties of x-rays, with a relatively high signal in the lungs due to the many air-tissue interfaces in the alveoli. Purpose To describe the qualitative and quantitative characteristics of x-ray dark-field images in healthy human subjects. Materials and Methods Between October 2018 and January 2020, patients of legal age who underwent chest CT as part of their diagnostic work-up were screened for study participation. Inclusion criteria were a normal chest CT scan, the ability to consent, and the ability to stand upright without help. Exclusion criteria were pregnancy, serious medical conditions, and changes in the lung tissue, such as those due to cancer, pleural effusion, atelectasis, emphysema, infiltrates, ground-glass opacities, or pneumothorax. Images of study participants were obtained by using a clinical x-ray dark-field prototype, recently constructed and commissioned at the authors' institution, to simultaneously acquire both attenuation-based and dark-field thorax radiographs. Each subject's total dark-field signal was correlated with his or her lung volume, and the dark-field coefficient was correlated with age, sex, weight, and height. Results Overall, 40 subjects were included in this study (average age, 62 years ± 13 [standard deviation]; 26 men, 14 women). Normal human lungs have high signal, while the surrounding osseous structures and soft tissue have very low and no signal, respectively. The average dark-field signal was 2.5 m-1 ± 0.4 of examined lung tissue. There was a correlation between the total dark-field signal and the lung volume (r = 0.61, P < .001). No difference was found between men and women (P = .78). Also, age (r = -0.18, P = .26), weight (r = 0.24, P = .13), and height (r = 0.01, P = .96) did not influence dark-field signal. Conclusion This study introduces qualitative and quantitative values for x-ray dark-field imaging in healthy human subjects. The quantitative x-ray dark-field coefficient is independent from demographic subject parameters, emphasizing its potential in diagnostic assessment of the lung. ©RSNA, 2021 See also the editorial by Hatabu and Madore in this issue.

Sparse-sampling computed tomography for detection of endoleak after endovascular aortic repair (EVAR).

OBJECTIVES: To evaluate sparse sampling computed tomography (SpSCT) for detection of endoleak after endovascular aortic repair (EVAR) at different dose levels in terms of subjective image criteria and diagnostic accuracy. METHODS: Twenty clinically indicated computed tomography aortic angiography (CTA) scans were used to obtain simulated low-dose scans with 100%, 50%, 25%, 12.5% and 6.25% of the applicated clinical dose, resulting in five dose levels (DL). From full sampling (FS) data sets, every second (2-SpSCT) or fourth (4-SpSCT) projection was used to generate simulated sparse sampling scans. All examinations were evaluated by four blinded radiologists regarding subjective image criteria and diagnostic performance. RESULTS: Sensitivity was higher than 93% in 4-SpSCT at the 25% DL which is the same as with FS at full dose (100% DL). High accuracies and relative high AUC-values were obtained for 2- and 4-SpSCT down to the 12.5% DL, while for FS similar values were shown down to 25% DL only. Subjective image quality was significantly higher for 4-SpSCT compared to FS at each dose level. More than 90% of all cases were rated with a high or medium confidence for FS and 2-SpSCT at the 50% DL and for 4-SpSCT at the 25% DL. At DL 25% and 12.5%, more cases showed a high confidence using 2- and 4-SpSCT compared with FS. CONCLUSIONS: Via SpSCT, a dose reduction down to a 25% dose level (mean effective dose of 1.49 mSv in the current study) for CTA is possible while maintaining high image quality and full diagnostic confidence.

Early detection of radiation-induced lung damage with X-ray dark-field radiography in mice.

OBJECTIVE: Assessing the advantage of x-ray dark-field contrast over x-ray transmission contrast in radiography for the detection of developing radiation-induced lung damage in mice. METHODS: Two groups of female C57BL/6 mice (irradiated and control) were imaged obtaining both contrasts monthly for 28 weeks post irradiation. Six mice received 20 Gy of irradiation to the entire right lung sparing the left lung. The control group of six mice was not irradiated. A total of 88 radiographs of both contrasts were evaluated for both groups based on average values for two regions of interest, covering (irradiated) right lung and healthy left lung. The ratio of these average values, R, was distinguished between healthy and damaged lungs for both contrasts. The time-point when deviations of R from healthy lung exceeded 3σ was determined and compared among contrasts. The Wilcoxon-Mann-Whitney test was used to test against the null hypothesis that there is no difference between both groups. A selection of 32 radiographs was assessed by radiologists. Sensitivity and specificity were determined in order to compare the diagnostic potential of both contrasts. Inter-reader and intra-reader accuracy were rated with Cohen's kappa. RESULTS: Radiation-induced morphological changes of lung tissue caused deviations from the control group that were measured on average 10 weeks earlier with x-ray dark-field contrast than with x-ray transmission contrast. Sensitivity, specificity, and accuracy doubled using dark-field radiography. CONCLUSION: X-ray dark-field radiography detects morphological changes of lung tissue associated with radiation-induced damage earlier than transmission radiography in a pre-clinical mouse model. KEY POINTS: • Significant deviations from healthy lung due to irradiation were measured after 16 weeks with x-ray dark-field radiography (p = 0.004). • Significant deviations occur on average 10 weeks earlier for x-ray dark-field radiography in comparison to x-ray transmission radiography. • Sensitivity and specificity doubled when using x-ray dark-field radiography instead of x-ray transmission radiography.

Dual-energy CT parameters in correlation to MRI-based apparent diffusion coefficient: evaluation in rectal cancer after radiochemotherapy.

BACKGROUND: Rectal cancer (RC) is a frequent malignancy for which magnetic resonance imaging (MRI) is the most common and accurate imaging. Iodine concentration (IC) can be quantified with spectral dual-layer computed tomography CT (DL-CT), which could improve imaging of RC, especially for evaluation of response to radiochemotherapy (RCT). PURPOSE: To compare a DL-CT system to MRI as the non-invasive imaging gold standard for imaging of RC to evaluate the possibility of a response evaluation with DL-CT. MATERIAL AND METHODS: Eleven patients who received DL-CT as well as MRI before and after RCT of RC were retrospectively included into this study. For each examination, a region of interest (ROI) was placed within the tumor. For MRI, the mean apparent diffusion coefficient (ADC) was assessed. For DL-CT, IC, z-effective, and Hounsfield Units (HU) were measured. IC, z-effective, and HU were normalized to the aorta. ADC was correlated to absolute and relative normalized IC, z-effective, and HU with Spearman's ρ. Differences before and after treatment were tested with Wilcoxon signed-rank test. RESULTS: HU, IC, and Z-effective values in DL-CT images decreased significantly after RCT (P<0.01 for each comparison). The mean ADC increased significantly after RCT. Spearman's ρ of the absolute IC difference and the absolute ADC (both before and after RCT) is high and significant (ρ = 0.73; P = 0.01), whereas the ρ-value for z-effective (ρ = 0.56) or HU (ρ = 0.45) to ADC was lower and non-significant. CONCLUSION: Response evaluation of RC after RCT could be possible with DL-CT via the measurement of IC.

CTPA with a conventional CT at 100 kVp vs. a spectral-detector CT at 120 kVp: Comparison of radiation exposure, diagnostic performance and image quality.

PURPOSE: To compare CT pulmonary angiographies (CTPAs) as well as phantom scans obtained at 100 kVp with a conventional CT (C-CT) to virtual monochromatic images (VMI) obtained with a spectral detector CT (SD-CT) at equivalent dose levels as well as to compare the radiation exposure of both systems. MATERIAL AND METHODS: In total, 2110 patients with suspected pulmonary embolism (PE) were examined with both systems. For each system (C-CT and SD-CT), imaging data of 30 patients with the same mean CT dose index (4.85 mGy) was used for the reader study. C-CT was performed with 100 kVp and SD-CT was performed with 120 kVp; for SD-CT, virtual monochromatic images (VMI) with 40, 60 and 70 keV were calculated. All datasets were evaluated by three blinded radiologists regarding image quality, diagnostic confidence and diagnostic performance (sensitivity, specificity). Contrast-to-noise ratio (CNR) for different iodine concentrations was evaluated in a phantom study. RESULTS: CNR was significantly higher with VMI at 40 keV compared to all other datasets. Subjective image quality as well as sensitivity and specificity showed the highest values with VMI at 60 keV and 70 keV. Hereby, a significant difference to 100 kVp (C-CT) was found for image quality. The highest sensitivity was found using VMI at 60 keV with a sensitivity of more than 97 % for all localizations of PE. For diagnostic confidence and subjective contrast, highest values were found with VMI at 40 keV. CONCLUSION: Higher levels of diagnostic performance and image quality were achieved for CPTAs with SD-CT compared to C-CT given similar dose levels. In the clinical setting SD-CT may be the modality of choice as additional spectral information can be obtained.

Iodine concentration of healthy lymph nodes of neck, axilla, and groin in dual-energy computed tomography.

BACKGROUND: Lymph nodes (LN) are examined in every computed tomography (CT) scan. Until now, an evaluation is only possible based on morphological criteria. With dual-energy CT (DECT) systems, iodine concentration (IC) can be measured which could conduct in an improved diagnostic evaluation of LNs. PURPOSE: To define standard values for IC of cervical, axillary, and inguinal LNs in DECT. MATERIAL AND METHODS: Imaging data of 297 patients who received a DECT scan of the neck, thorax, abdomen-pelvis, or a combination of those in a portal-venous phase were retrospectively collected from the institutional PACS. No present history of malignancy, inflammation, or trauma in the examined region was present. For each examined region, the data of 99 patients were used. The IC of the three largest LNs, the main artery, the main vein, and a local muscle of the examined area was measured, respectively. RESULTS: Normalization of the IC of LNs to the artery, vein, muscle, or a combination of those did not lead to a decreased value-range. The smallest range and confidence interval (CI) of IC was found when using absolute values of IC for each region. Hereby, mean values (95% CI) for IC of LN were found: 2.09 mg/mL (2.00-2.18 mg/mL) for neck, 1.24 mg/mL (1.16-1.33 mg/mL) for axilla, and 1.11 mg/mL (1.04-1.17 mg/mL) for groin. CONCLUSION: The present study suggests standard values for IC of LNs in dual-layer CT could be used to differentiate between healthy and pathological lymph nodes, considering the used contrast injection protocol.

Liver lesion localisation and classification with convolutional neural networks: a comparison between conventional and spectral computed tomography.

PURPOSE: To evaluate the benefit of the additional available information present in spectral CT datasets, as compared to conventional CT datasets, when utilizing convolutional neural networks for fully automatic localisation and classification of liver lesions in CT images. MATERIALS AND METHODS: Conventional and spectral CT images (iodine maps, virtual monochromatic images (VMI)) were obtained from a spectral dual-layer CT system. Patient diagnosis were known from the clinical reports and classified into healthy, cyst and hypodense metastasis. In order to compare the value of spectral versus conventional datasets when being passed as input to machine learning algorithms, we implemented a weakly-supervised convolutional neural network (CNN) that learns liver lesion localisation without pixel-level ground truth annotations. Regions-of-interest are selected automatically based on the localisation results and are used to train a second CNN for liver lesion classification (healthy, cyst, hypodense metastasis). The accuracy of lesion localisation was evaluated using the Euclidian distances between the ground truth centres of mass and the predicted centres of mass. Lesion classification was evaluated by precision, recall, accuracy and F1-Score. RESULTS: Lesion localisation showed the best results for spectral information with distances of 8.22 ± 10.72 mm, 8.78 ± 15.21 mm and 8.29 ± 12.97 mm for iodine maps, 40 keV and 70 keV VMIs, respectively. With conventional data distances of 10.58 ± 17.65 mm were measured. For lesion classification, the 40 keV VMIs achieved the highest overall accuracy of 0.899 compared to 0.854 for conventional data. CONCLUSION: An enhanced localisation and classification is reported for spectral CT data, which demonstrates that combining machine-learning technology with spectral CT information may in the future improve the clinical workflow as well as the diagnostic accuracy.