Detection and size measurements of kidney stones on virtual non-contrast reconstructions derived from dual-layer computed tomography in an ex vivo phantom setup.

OBJECTIVES: To systematically investigate the usability of virtual non-contrast reconstructions (VNC) derived from dual-layer CT (DLCT) for detection and size measurements of kidney stones with regards to different degrees of surrounding iodine-induced attenuation and radiation dose. METHODS: Ninety-two kidney stones of varying size (3-14 mm) and composition were placed in a phantom filled with different contrast media/water mixtures exhibiting specific iodine-induced attenuation (0-1500 HU). DLCT-scans were acquired using CTDIvol of 2 mGy and 10 mGy. Conventional images (CI) and VNC0H-1500HU were reconstructed. Reference stone size was determined using a digital caliper (Man-M). Visibility and stone size were assessed. Statistical analysis was performed using the McNemar test, Wilcoxon test, and the coefficient of determination. RESULTS: All stones were visible on CI0HU and VNC200HU. Starting at VNC400 HU, the detection rate decreased with increasing HU and was significantly lower as compared to CI0HU on VNC≥ 600HU (100.0 vs. 94.0%, p < 0.05). The overall detection rate was higher using 10 mGy as compared to 2 mGy protocol (87.9 vs. 81.8%; p < 0.001). Stone size was significantly overestimated on all VNC compared to Man-M (7.0 ± 3.5 vs. 6.6 ± 2.8 mm, p < 0.001). Again, the 10 mGy protocol tended to show a better correlation with Man-M as compared to 2 mGy protocol (R2 = 0.39-0.68 vs. R2 = 0.31-0.57). CONCLUSIONS: Detection and size measurements of kidney stones surrounded by contrast media on VNC are feasible. The detection rate of kidney stones decreases with increasing iodine-induced attenuation and with decreasing radiation dose as well as stone size, while remaining comparable to CI0HU on VNC ≤ 400 HU. KEY POINTS: • The detection rate of kidney stones on VNC depends on the surrounding iodine-induced attenuation, the used radiation dose, and the stone size. • The detection rate of kidney stones on VNC decreases with greater iodine-induced attenuation and with lower radiation dose, particularly in small stones. • The visibility of kidney stones on VNC ≤ 400 HU remains comparable to true-non-contrast scans even when using a low-dose technique.

[Status and recommendations for interventional radiological procedures in COVID-19 patients]. / Status und Empfehlungen zur interventionell-radiologischen Versorgung bei COVID-19-PatientInnen.

BACKGROUND: Particularly at the beginning, the COVID-19 (coronavirus disease 2019) pandemic caused a reduction in the number of interventions in interventional radiology. At the same time, interventional training became more challenging. Infectious patients and disease transmission within interventional radiology departments continue to pose significant challenges. OBJECTIVES: This article describes the status and recommendations for interventional radiological procedures in COVID-19 patients. MATERIALS AND METHODS: Guidelines and recommendations from international and national societies as well as original works and reviews were evaluated. RESULTS: Interventional radiological care of COVID-19 patients with complicated courses of infection has become established during the course of the pandemic. To protect patients and staff, interventions in COVID-19 patients should be prioritized, performed in separate procedure rooms if possible, and patients should be tested before interventions. Logistics, staff planning, and hygiene measures should be continuously optimized. CONCLUSIONS: Structured workflows within interventional radiology in dealing with COVID-19 patients appear necessary to minimize infection risks and to guarantee the staff's work capability and health. In order to develop concepts for the handling of COVID-19 patients and to be prepared for potential upcoming waves of infections, recommendations of the Robert Koch Institute (RKI) and (inter-)national professional societies are helpful.

Head and neck squamous cell carcinoma: evaluation of iodine overlay maps and low-energy virtual mono-energetic images acquired with spectral detector CT.

AIM: To evaluate the diagnostic value of spectral detector computed tomography (SDCT)-derived iodine overlay maps and low-energy virtual mono-energetic images (VMI) for the initial locoregional assessment of primary, therapy-naive head and neck cancer. MATERIALS AND METHODS: Fifty-six patients with histologically confirmed untreated squamous cell carcinoma of the head and neck who underwent SDCT of the neck for staging purposes were included in this retrospective study. Attenuation, image noise as well as signal- and contrast-to-noise ratios (S-/CNR) in VMI40-70keV were obtained from region of interest (ROI)-based measurements in the tumour and important anatomical landmarks (sternocleidomastoid muscle, subcutaneous fat, thyroid gland, submandibular gland, carotid artery, and jugular vein). Tumour conspicuity and delineation, as well as subjective image quality, were rated for conventional images, VMI40-70keV, and iodine overlay maps using five-point Likert scales. RESULTS: The CNR of the tumour versus the floor of the mouth and the CNR of the tumour versus the sternocleidomastoid muscle was significantly higher in VMI40keV in comparison to conventional images (10.0 ± 7.3 versus 3.8 ± 3.3 and 11.3 ± 7.6 versus 3.6 ± 2.8; p<0.05 each). This was supported by qualitative results, as tumour conspicuity and delineation received superior ratings in iodine overlay maps and VMI40keV compared to conventional images (5 [3-5] and 5 [4-5] versus 3 [2-5]; 5 [2-5] and 5 [3-5] versus 3 [2-4], respectively, all p<0.05). VMI40keV yielded the highest score among all included image reconstructions for overall image quality (p<0.05 all). CONCLUSION: Iodine overlay maps and low-energy VMI derived from SDCT improve initial assessment of primary squamous cell carcinoma of the head and neck compared to conventional images.

Automated Color-Coding of Lesion Changes in Contrast-Enhanced 3D T1-Weighted Sequences for MRI Follow-up of Brain Metastases.

BACKGROUND AND PURPOSE: MR imaging is the technique of choice for follow-up of patients with brain metastases, yet the radiologic assessment is often tedious and error-prone, especially in examinations with multiple metastases or subtle changes. This study aimed to determine whether using automated color-coding improves the radiologic assessment of brain metastases compared with conventional reading. MATERIALS AND METHODS: One hundred twenty-one pairs of follow-up examinations of patients with brain metastases were assessed. Two radiologists determined the presence of progression, regression, mixed changes, or stable disease between the follow-up examinations and indicated subjective diagnostic certainty regarding their decisions in a conventional reading and a second reading using automated color-coding after an interval of 8 weeks. RESULTS: The rate of correctly classified diagnoses was higher (91.3%, 221/242, versus 74.0%, 179/242, P < .01) when using automated color-coding, and the median Likert score for diagnostic certainty improved from 2 (interquartile range, 2-3) to 4 (interquartile range, 3-5) (P < .05) compared with the conventional reading. Interrater agreement was excellent (κ = 0.80; 95% CI, 0.71-0.89) with automated color-coding compared with a moderate agreement (κ = 0.46; 95% CI, 0.34-0.58) with the conventional reading approach. When considering the time required for image preprocessing, the overall average time for reading an examination was longer in the automated color-coding approach (91.5 [SD, 23.1] seconds versus 79.4 [SD, 34.7 ] seconds, P < .001). CONCLUSIONS: Compared with the conventional reading, automated color-coding of lesion changes in follow-up examinations of patients with brain metastases significantly increased the rate of correct diagnoses and resulted in higher diagnostic certainty.

Automated Detection and Segmentation of Brain Metastases in Malignant Melanoma: Evaluation of a Dedicated Deep Learning Model.

BACKGROUND AND PURPOSE: Malignant melanoma is an aggressive skin cancer in which brain metastases are common. Our aim was to establish and evaluate a deep learning model for fully automated detection and segmentation of brain metastases in patients with malignant melanoma using clinical routine MR imaging. MATERIALS AND METHODS: Sixty-nine patients with melanoma with a total of 135 brain metastases at initial diagnosis and available multiparametric MR imaging datasets (T1-/T2-weighted, T1-weighted gadolinium contrast-enhanced, FLAIR) were included. A previously established deep learning model architecture (3D convolutional neural network; DeepMedic) simultaneously operating on the aforementioned MR images was trained on a cohort of 55 patients with 103 metastases using 5-fold cross-validation. The efficacy of the deep learning model was evaluated using an independent test set consisting of 14 patients with 32 metastases. Manual segmentations of metastases in a voxelwise manner (T1-weighted gadolinium contrast-enhanced imaging) performed by 2 radiologists in consensus served as the ground truth. RESULTS: After training, the deep learning model detected 28 of 32 brain metastases (mean volume, 1.0 [SD, 2.4] cm3) in the test cohort correctly (sensitivity of 88%), while false-positive findings of 0.71 per scan were observed. Compared with the ground truth, automated segmentations achieved a median Dice similarity coefficient of 0.75. CONCLUSIONS: Deep learning-based automated detection and segmentation of brain metastases in malignant melanoma yields high detection and segmentation accuracy with false-positive findings of <1 per scan.

Evaluating anemia using contrast-enhanced spectral detector CT of the chest in a large cohort of 522 patients.

OBJECTIVES: The blood of patients with anemia demonstrates distinctly lower attenuation in unenhanced CT images. However, the frequent usage of intravenous contrast hampers evaluation of anemia. Spectral detector computed tomography (SDCT) allows for reconstruction of virtual non-contrast images (VNC) from contrast-enhanced data (CE). The purpose of this study was to evaluate whether VNC allow for prediction of anemia. METHODS: Five hundred twenty-two patients with CE-SDCT of the chest and accessible serum hemoglobin (HbS) were retrospectively included. Patients were assigned to three groups (severe anemia, moderate/mild anemia, and healthy) based on recent lab tests (≤ 7 days) for HbS following gender and the WHO definition of anemia. CT attenuation was determined using two ROI in the left ventricular lumen and one ROI in the descending thoracic aorta. ROI were placed on CE and copied to VNC. ANOVA, linear regression, and receiver operating characteristics were used for statistic evaluation. RESULTS: Average HbS was 11.6 ± 2.4 g/dl. Attenuation on VNC showed significant differences between healthy patients, patients with mild/moderate anemia, and severely anemic patients (all p ≤ 0.05). Applying cutoffs of 39.2/37.6 HU and 33.6/32.7 HU allowed to differentiate between healthy, mild/moderately, and severely anemic men/women (AUC 0.857/0.833 and 0.879/0.932). A linear relationship between HbS and attenuation on VNC was established (r2 = 0.54, HbS = - 0.875 + 0.329 × HU). CONCLUSIONS: An approximation of HbS and presence of anemia can be conducted based on simple attenuation measurements in contrast-enhanced SDCT examinations enabled by VNC imaging. KEY POINTS: • While the attenuation of blood is a previously described biomarker for anemia in non-contrast images, virtual non-contrast images from spectral detector CT circumvent this limitation and allow for diagnosis of anemia in contrast-enhanced scans. • Attenuation of blood in virtual non-contrast images derived from spectral detector CT shows a moderate correlation to serum hemoglobin levels. • Presence of anemia be estimated in virtual non-contrast images using proposed cutoffs of 39.2 HU and 37.6 HU for men and women, respectively, to differentiate between healthy and anemic patients.

Virtual monoenergetic images from spectral detector computed tomography facilitate washout assessment in arterially hyper-enhancing liver lesions.

OBJECTIVES: To investigate whether the increased soft tissue contrast of virtual monoenergetic images (VMIs) obtained from a spectral detector computed tomography (SDCT) system improves washout assessment of arterially hyper-enhancing liver lesions. METHODS: Fifty-nine arterially hyper-enhancing lesions in 31 patients (age 65 ± 9 years, M/W 20/11) were included in this IRB-approved study. All patients underwent multi-phase SDCT for HCC screening. MRI, CEUS or biopsy within 3 months served as standard of reference to classify lesions as LiRADS 3 or 4/5. VMIs and conventional images (CIs) were reconstructed. Visual analysis was performed on 40, 60, and 80 kiloelectronvolt (keV) and CIs by 3 radiologists. Presence and visibility of washout were assessed; image quality and confidence of washout evaluation were evaluated on 5-point Likert scales. Signal-to-noise ratio (SNR), lesion-to-liver contrast-to-noise ratio (CNR) (|HUlesion-HUliver|/SDliver) and washout (|HUlesion-HUliver|) were calculated. Statistical assessment was performed using ANOVA and Wilcoxon test. RESULTS: On subjective lesion analysis, the highest level of diagnostic confidence and highest sensitivity for the detection of lesion washout were found for 40-keV VMIs (40 keV vs. CI, 81.3 vs. 71.3%). Image quality parameters were significantly better in low-kiloelectronvolt VMIs than in CIs (p < 0.05; e.g. SNRliver: 40 keV vs. CIs, 12.5 ± 4.1 vs. 5.6 ± 1.6). In LiRADS 4/5 lesions, CNR and quantitative washout values were significantly higher in 40-keV VMIs compared to CIs (p < 0.05; e.g. CNR and washout in 40 keV vs. CIs, 2.3 ± 1.6 vs. 0.8 ± 0.5 and 29.0 ± 19.1 vs. 12.9 ± 6.9 HU, respectively). CONCLUSION: By increasing lesion contrast, low-kiloelectronvolt VMIs obtained from SDCT improve washout assessment of hyper-enhancing liver lesions with respect to washout visibility and diagnostic confidence. KEY POINTS: • Low-kiloelectronvolt virtual monoenergetic images from spectral detector CT facilitate washout assessment in arterially hyper-enhancing liver lesions. • Image quality and quantitative washout parameters as well as subjective washout visibility and diagnostic confidence benefit from low-kiloelectronvolt virtual monoenergetic images.

Virtual Monoenergetic Images from Spectral Detector CT Enable Radiation Dose Reduction in Unenhanced Cranial CT.

BACKGROUND AND PURPOSE: Our aim was to evaluate whether improved gray-white matter differentiation in cranial CT by means of 65- keV virtual monoenergetic images enables a radiation dose reduction compared to conventional images. MATERIALS AND METHODS: One hundred forty consecutive patients undergoing 171 spectral detector CTs of the head between February and November 2017 (56 ± 19 years of age; male/female ratio, 56%/44%) were retrospectively included. The tube current-time product was reduced during the study period, resulting in 61, 55, and 55 patients being examined with 320, 290, and 260 mAs, respectively. All other scanning parameters were kept identical. The volume CT dose index was recorded. ROIs were placed in gray and white matter on conventional images and copied to identical positions in 65- keV virtual monoenergetic images. The contrast-to-noise ratio was calculated. Two radiologists blinded to the reconstruction technique evaluated image quality on a 5-point Likert-scale. Statistical assessment was performed using ANOVA and Wilcoxon test adjusted for multiple comparisons. RESULTS: The mean volume CT dose index was 55, 49.8, and 44.7 mGy using 320, 290, and 260 mAs, respectively. Irrespective of the volume CT dose index, noise was significantly lower in 65- keV virtual monoenergetic images compared with conventional images (65- keV virtual monoenergetic images/conventional images: extraocular muscle with 49.8 mGy, 3.7 ± 1.3/5.6 ± 1.6 HU, P < .001). Noise slightly increased with a reduced radiation dose (eg, extraocular muscle in conventional images: 5.3 ± 1.4/5.6 ± 1.6/6.1 ± 2.1 HU). Overall, the contrast-to-noise ratio in 65- keV virtual monoenergetic images was superior to that in conventional images irrespective of the volume CT dose index (P < .001). Particularly, 65-keV virtual monoenergetic images with 44.7 mGy showed significantly lower noise and a higher contrast-to-noise ratio than conventional images with 55 mGy (P < .001). Subjective analysis confirmed better image quality in 65- keV virtual monoenergetic images, even using 44.7 mGy. CONCLUSIONS: The 65-keV virtual monoenergetic images from spectral detector CT allow radiation dose reduction in cranial CT. While this proof of concept included a radiation dose reduction of 19%, our data suggest that even greater reduction appears achievable.

Artifact reduction from dental implants using virtual monoenergetic reconstructions from novel spectral detector CT.

OBJECTIVES: Image quality in head and neck imaging is often severely hampered by artifacts arising from dental implants. This study evaluates metal artifact (MA) reduction using virtual monoenergetic images (VMI) compared to conventional CT images (CI) from spectral-detector computed tomography (SDCT). METHODS: 38 consecutive patients with dental implants were included in this retrospective study. All examinations were performed using a SDCT (IQon, Philips, Best, The Netherlands). Images were reconstructed as conventional images (CI) and as VMI in a range of 40-200 keV (10 keV increment). Quantitative image analysis was performed ROI-based by measurement of attenuation (HU) and standard deviation in most pronounced hypo- and hyperdense artifact, fat and soft tissue with presence of artifacts. Qualitatively, extent of artifact reduction, assessment of soft palate and cheeks were rated on 5-point Likert-scales by two radiologists. Statistical data evaluation included ANOVA and Wilcoxon-test with correction for multiple comparisons; interrater-agreement was determined by intraclass-correlation coefficient (ICC). RESULTS: The hypo- and hyperattenuating artifacts showed an increase and decrease of HU-values in VMIhigh (CI/VMI200 keV: -218.7/-174.4 HU, p = 0.1; and 309.8/119.2, p ≤ 0.05, respectively). Artifacts in the fat, as depicted by image noise did also decrease in VMIhigh (CI/VMI200 keV: 23.9/16.4, p ≤ 0.05). Qualitatively, hyperdense artifacts were decreased significantly in VMI ≥100 keV (e.g. CI/VMI200 keV: 2(1-3)/3(1-5), p ≤ 0.05). Artifact reduction resulted in improved assessment of the soft palate and cheeks (e.g. CI/VMI200 keV: 2(1-4)/3(1-5) and 2(1-5)/3(1-5), p ≤ 0.05). Overall interrater agreement was good (ICC = 0.77). CONCLUSIONS: Virtual monoenergetic images from SDCT reduce metal artifacts from dental implants and improve diagnostic assessment of surrounding soft tissue.