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.

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.

Improvements of diagnostic accuracy and visualization of vertebral metastasis using multi-level virtual non-calcium reconstructions from dual-layer spectral detector computed tomography.

OBJECTIVE: To evaluate feasibility and diagnostic performance of multi-level calcium suppression in spectral detector computed tomography (SDCT) for assessment of bone metastasis. MATERIALS AND METHODS: Retrospective IRB-approved study on 21 patients who underwent SDCT (120 kV, reference mAs 116) and MRI. Thoracic and lumbar vertebrae (n = 357) were included and categorized as normal (n = 133) or metastatic (n = 203) based on MRI (STIR, T1w, ±contrast). The multi-level virtual non-calcium (VNCa) algorithm computes dynamic soft tissue/calcium pairs allowing for computation of different suppression index levels to address inter-individual variance of prevalent calcium composition weights. We computed images with low, medium, and high calcium suppression indices and compared them with conventional images (VNCa_low/med/high and conventional images (CI)). For quantitative image analysis, regions of interest were placed in normal and metastatic bone. Two readers reviewed the datasets independently in multiple sessions. They determined the presence of vertebral metastases on a per vertebra basis using a binary scale. Statistic assessment was performed using ANOVA with Tukey HSD, Student's T test, and ROC analysis. RESULTS: Attenuation of both normal and metastatic bone was lower in VNCa images than that in conventional images (e.g., CI/VNCa_low, - 46.3 to 238.8 HU/343.3-60.2 HU; p ≤ 0.05). VNCa_low+med improved separation of normal and metastatic bone in ROC analysis (AUC, CI/VNCa_low/VNCa_med = 0.74/0.95/0.98; p ≤ 0.05). In subjective analysis, both sensitivity and specificity were clearly improved in VNCa_low as compared with CI (0.85/0.84 versus 0.78/0.82). Readers showed a good inter-rater reliability (kappa = 0.65). CONCLUSIONS: Multi-level VNCa reconstructed from SDCT improve quantitative separation of normal and metastatic bone and subjective determination of bone metastases when using low to intermediate calcium suppression indices. KEY POINTS: • Spectral detector CT allows for multi-level calcium suppression in CT images and low and medium calcium suppression indices improved separation of normal and metastatic bone. • Thus, multi-level calcium suppression allows to optimize image contrast in regard to dedicated pathologies. • Low-level virtual non-calcium images (index 25-50) improved diagnostic performance regarding detection of metastasis.

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.

Reduction of artifacts caused by orthopedic hardware in the spine in spectral detector CT examinations using virtual monoenergetic image reconstructions and metal-artifact-reduction algorithms.

OBJECTIVE: Aim of this study was to assess the artifact reduction in patients with orthopedic hardware in the spine as provided by (1) metal-artifact-reduction algorithms (O-MAR) and (2) virtual monoenergetic images (MonoE) as provided by spectral detector CT (SDCT) compared to conventional iterative reconstruction (CI). METHODS: In all, 28 consecutive patients with orthopedic hardware in the spine who underwent SDCT-examinations were included. CI, O-MAR and MonoE (40-200 keV) images were reconstructed. Attenuation (HU) and noise (SD) were measured in order to calculate signal-to-noise ratio (SNR) of paravertebral muscle and spinal canal. Subjective image quality was assessed by two radiologists in terms of image quality and extent of artifact reduction. RESULTS: O-MAR and high-keV MonoE showed significant decrease of hypodense artifacts in terms of higher attenuation as compared to CI (CI vs O-MAR, 200 keV MonoE: -396.5HU vs. -115.2HU, -48.1HU; both p ≤ 0.001). Further, artifacts as depicted by noise were reduced in O-MAR and high-keV MonoE as compared to CI in (1) paravertebral muscle and (2) spinal canal-CI vs. O-MAR/200 keV: (1) 34.7 ± 19.0 HU vs. 26.4 ± 14.4 HU, p ≤ 0.05/27.4 ± 16.1, n.s.; (2) 103.4 ± 61.3 HU vs. 72.6 ± 62.6 HU/60.9 ± 40.1 HU, both p ≤ 0.001. Subjectively both O-MAR and high-keV images yielded an artifact reduction in up to 24/28 patients. CONCLUSION: Both, O-MAR and high-keV MonoE reconstructions as provided by SDCT lead to objective and subjective artifact reduction, thus the combination of O-MAR and MonoE seems promising for further reduction.