RESUMEN

PURPOSE: The objective was to develop a fully automated algorithm that generates confidence maps to identify regions valid for analysis of quantitative proton density fat fraction (PDFF) and R 2 * $$ {R}_2^{\ast } $$ maps of the liver, generated with chemical shift-encoded MRI (CSE-MRI). Confidence maps are urgently needed for automated quality assurance, particularly with the emergence of automated segmentation and analysis algorithms. METHODS: Confidence maps for both PDFF and R 2 * $$ {R}_2^{\ast } $$ maps are generated based on goodness of fit, measured by normalized RMS error between measured complex signals and the CSE-MRI signal model. Based on Cramér-Rao lower bound and Monte-Carlo simulations, normalized RMS error threshold criteria were developed to identify unreliable regions in quantitative maps. Simulation, phantom, and in vivo clinical studies were included. To analyze the clinical data, a board-certified radiologist delineated regions of interest (ROIs) in each of the nine liver segments for PDFF and R 2 * $$ {R}_2^{\ast } $$ analysis in consecutive clinical CSE-MRI data sets. The percent area of ROIs in areas deemed unreliable by confidence maps was calculated to assess the impact of confidence maps on real-world clinical PDFF and R 2 * $$ {R}_2^{\ast } $$ measurements. RESULTS: Simulations and phantom studies demonstrated that the proposed algorithm successfully excluded regions with unreliable PDFF and R 2 * $$ {R}_2^{\ast } $$ measurements. ROI analysis by the radiologist revealed that 2.6% and 15% of the ROIs were placed in unreliable areas of PDFF and R 2 * $$ {R}_2^{\ast } $$ maps, as identified by confidence maps. CONCLUSION: A proposed confidence map algorithm that identifies reliable areas of PDFF and R 2 * $$ {R}_2^{\ast } $$ measurements from CSE-MRI acquisitions was successfully developed. It demonstrated technical and clinical feasibility.

Asunto(s)

RESUMEN

Diffusion MRI (dMRI) is a widely used method to investigate the microstructure of the brain. Quality control (QC) of dMRI data is an important processing step that is performed prior to analysis using models such as diffusion tensor imaging (DTI) or neurite orientation dispersion and density imaging (NODDI). When processing dMRI data from infants and young children, where intra-scan motion is common, the identification and removal of motion artifacts is of the utmost importance. Manual QC of dMRI data is (1) time-consuming due to the large number of diffusion directions, (2) expensive, and (3) prone to subjective errors and observer variability. Prior techniques for automated dMRI QC have mostly been limited to adults or school-age children. Here, we propose a deep learning-based motion artifact detection tool for dMRI data acquired from infants and toddlers. The proposed framework uses a simple three-dimensional convolutional neural network (3DCNN) trained and tested on an early pediatric dataset of 2,276 dMRI volumes from 121 exams acquired at 1 month and 24 months of age. An average classification accuracy of 95% was achieved following four-fold cross-validation. A second dataset with different acquisition parameters and ages ranging from 2-36 months (consisting of 2,349 dMRI volumes from 26 exams) was used to test network generalizability, achieving 98% classification accuracy. Finally, to demonstrate the importance of motion artifact volume removal in a dMRI processing pipeline, the dMRI data were fit to the DTI and NODDI models and the parameter maps were compared with and without motion artifact removal.

RESUMEN

Background The size-specific dose estimate (SSDE) is a patient-focused CT dose metric. However, published size-dependent conversion factors (fsize) used to calculate SSDE were determined primarily by using phantoms; only eight to 15 patient data sets were used, all at 120 kV. Purpose To determine the effect of different tube potentials on the water-equivalent diameter (WED) and SSDE for patient CT scans of the head, chest, and abdomen. Materials and Methods This retrospective study used 250 noncontrast CT scans acquired between March 2013 and June 2017. Bony structures were segmented, and their CT numbers were modified to reflect bone attenuation at 70, 90, 110, 130, and 150 kV. Soft-tissue CT numbers were unchanged because of negligible energy dependence. fsize was measured in anthropomorphic phantoms for each tube potential and fit to an exponential function. WED and SSDE were determined for each patient at all tube potentials, regression analysis was performed relative to the WED and SSDE at 120 kV, and mean differences relative to 120 kV were calculated. Results In 250 patients (median age, 21.5 years; interquartile range, 44 years; 130 women), WED for all tube potentials was linearly related to the WED at 120 kV in all body regions (R2 = 0.995-1.000). The effect of tube potential on WED was negligible for torso examinations (Cohen d < 0.05). In the head, a medium effect size was observed at 70 kV; however, the mean absolute difference in WED was small (-0.49 cm ± 0.08 [standard deviation]; P < .001). For commonly used combinations of tube potential and patient size, the mean differences in SSDE at alternative tube potentials relative to SSDE at 120 kV were less than 5%. Conclusion At noncontrast CT, published size-dependent conversion factors accurately determined size-specific dose estimates on 250 patient scans at five tube potentials other than 120 kV. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Boone in this issue.

Asunto(s)

RESUMEN

OBJECTIVES: The objectives of the study were to estimate the impact of high matrix image reconstruction on chest computed tomography (CT) compared to standard image reconstruction. MATERIAL AND METHODS: This retrospective study included patients with interstitial or parenchymal lung disease, airway disease, and pulmonary nodules who underwent chest CT. Chest CT images were reconstructed using high matrix (1024 × 1024) or standard matrix (512 × 512), with all other parameters matched. Two radiologists, blinded to reconstruction technique, independently examined each lung, viewing image sets side by side and rating the conspicuity of imaging findings using a 5-point relative conspicuity scale. The presence of pulmonary nodules and confidence in classification of internal attenuation was also graded. Overall image quality and subjective noise/artifacts were assessed. RESULTS: Thirty-four patients with 68 lungs were evaluated. Relative conspicuity scores were significantly higher using high matrix image reconstruction for all imaging findings indicative of idiopathic lung fibrosis (peripheral airway visualization, interlobular septal thickening, intralobular reticular opacity, and end-stage fibrotic change; P ≤ 0.001) along with emphysema, mosaic attenuation, and fourth order bronchi for both readers (P ≤ 0.001). High matrix reconstruction did not improve confidence in the presence or classification of internal nodule attenuation for either reader. Overall image quality was increased but not subjective noise/artifacts with high matrix image reconstruction for both readers (P < 0.001). CONCLUSION: High matrix image reconstruction significantly improves the conspicuity of imaging findings reflecting interstitial lung disease and may be useful for diagnosis or treatment response assessment.

RESUMEN

OBJECTIVE: The aims of this study were to investigate the feasibility of using a universal abdominal acquisition protocol on a photon-counting detector computed tomography (PCD-CT) system and to compare its performance to that of single-energy (SE) and dual-energy (DE) CT using energy-integrating detectors (EIDs). METHODS: Iodine inserts of various concentrations and sizes were embedded into different sizes of adult abdominal phantoms. Phantoms were scanned on a research PCD-CT and a clinical EID-CT with SE and DE modes. Virtual monoenergetic images (VMIs) were generated from PCD-CT and DE mode of EID-CT. For each image type and phantom size, contrast-to-noise ratio (CNR) was measured for each iodine insert and the area under the receiver operating characteristic curve (AUC) for iodine detectability was calculated using a channelized Hotelling observer. The optimal energy (in kiloelectrovolt) of VMIs was determined separately as the one with highest CNR and the one with the highest AUC. The PCD-CT VMIs at the optimal energy were then compared with DE VMIs and SE images in terms of CNR and AUC. RESULTS: Virtual monoenergetic image at 50 keV had both the highest CNR and highest AUC for PCD-CT and DECT. For 1.0 mg I/mL iodine and 35 cm phantom, the CNRs of 50 keV VMIs from PCD-CT (2.01 ± 0.67) and DE (1.96 ± 0.52) were significantly higher (P < 0.001, Wilcoxon signed-rank test) than SE images (1.11 ± 0.35). The AUC of PCD-CT (0.98 ± 0.01) was comparable to SE (0.98 ± 0.01), and both were slightly lower than DE (0.99 ± 0.01, P < 0.01, Wilcoxon signed-rank test). A similar trend was observed for other phantom sizes and iodine concentrations. CONCLUSIONS: Virtual monoenergetic images at a fixed energy from a universal acquisition protocol on PCD-CT demonstrated higher iodine CNR and comparable iodine detectability than SECT images, and similar performance compared with DE VMIs.

Asunto(s)

RESUMEN

OBJECTIVE: The aim of this study was to quantitatively demonstrate radiation dose reduction for sinus and temporal bone examinations using high-resolution photon-counting detector (PCD) computed tomography (CT) with an additional tin (Sn) filter. MATERIALS AND METHODS: A multienergy CT phantom, an anthropomorphic head phantom, and a cadaver head were scanned on a research PCD-CT scanner using ultra-high-resolution mode at 100-kV tube potential with an additional tin filter (Sn-100 kV) and volume CT dose index of 10 mGy. They were also scanned on a commercial CT scanner with an energy-integrating detector (EID) following standard clinical protocols. Thirty patients referred to clinically indicated sinus examinations, and two patients referred to temporal bone examinations were scanned on the PCD-CT system after their clinical scans on an EID-CT. For the sinus cohort, PCD-CT scans were performed using Sn-100 kV at 4 dose levels at 10 mGy (n = 9), 8 mGy (n = 7), 7 mGy (n = 7), and 6 mGy (n = 7), and the clinical EID-CT was performed at 120 kV and 13.7 mGy (mean CT volume dose index). For the temporal bone scans, PCD-CT was performed using Sn-100 kV (10.1 mGy), and EID-CT was performed at 120 kV and routine clinical dose (52.6 and 66 mGy). For both PCD-CT and EID-CT, sinus images were reconstructed using H70 kernel at 0.75-mm slice thickness, and temporal bone images were reconstructed using a U70 kernel at 0.6-mm slice thickness. In addition, iterative reconstruction with a dedicated sharp kernel (V80) was used to obtain high-resolution PCD-CT images from a sinus patient scan to demonstrate improved anatomic delineation. Improvements in spatial resolution from the dedicated sharp kernel was quantified using modulation transfer function measured with a wire phantom. A neuroradiologist assessed the H70 sinus images for visualization of critical anatomical structures in low-dose PCD-CT images and routine-dose EID-CT images using a 5-point Likert scale (structural detection obscured and poor diagnostic confidence, score = 1; improved anatomic delineation and diagnostic confidence, score = 5). Image contrast and noise were measured in representative regions of interest and compared between PCD-CT and EID-CT, and the noise difference between the 2 acquisitions was used to estimate the dose reduction in the sinus and temporal bone patient cohorts. RESULTS: The multienergy phantom experiment showed a noise reduction of 26% in the Sn-100 kV PCD-CT image, corresponding to a total dose reduction of 56% compared with EID-CT (clinical dose) without compromising image contrast. The PCD-CT images from the head phantom and the cadaver scans demonstrated a dose reduction of 67% and 83%, for sinus and temporal bone examinations, respectively, compared with EID-CT. In the sinus cohort, PCD-CT demonstrated a mean dose reduction of 67%. The 10- and 8-mGy sinus patient images from PCD-CT were significantly superior to clinical EID-CT for visualization of critical sinus structures (median score = 5 ± 0.82 and P = 0.01 for lesser palatine foramina, median score = 4 ± 0.68 and P = 0.039 for nasomaxillary sutures, and median score = 4 ± 0.96 and P = 0.01 for anterior ethmoid artery canal). The 6- and 7-mGy sinus patient images did not show any significant difference between PCD-CT and EID-CT. In addition, V80 (sharp kernel, 10% modulation transfer function = 18.6 cm) PCD-CT images from a sinus patient scan increased the conspicuity of nasomaxillary sutures compared with the clinical EID-CT images. The temporal bone patient images demonstrated a dose reduction of up to 85% compared with clinical EID-CT images, whereas visualization of inner ear structures such as the incudomalleolar joint were similar between EID-CT and PCD-CT. CONCLUSIONS: Phantom and cadaver studies demonstrated dose reduction using Sn-100 kV PCD-CT compared with current clinical EID-CT while maintaining the desired image contrast. Dose reduction was further validated in sinus and temporal bone patient studies. The ultra-high resolution capability from PCD-CT allowed improved anatomical delineation for sinus imaging compared with current clinical standard.

Asunto(s)

RESUMEN

In addition to low-energy-threshold images (TLIs), photon-counting detector (PCD) computed tomography (CT) can generate virtual monoenergetic images (VMIs) and iodine maps. Our study sought to determine the image type that maximizes iodine detectability. Adult abdominal phantoms with iodine inserts of various concentrations and lesion sizes were scanned on a PCD-CT system. TLIs, VMIs at 50 keV, and iodine maps were generated, and iodine contrast-to-noise ratio (CNR) was measured. A channelized Hotelling observer was used to determine the area under the receiver-operating-characteristic curve (AUC) for iodine detectability. Iodine map CNR ( 0.57 ± 0.42 ) was significantly higher ( P < 0.05 ) than for TLIs ( 0.46 ± 0.26 ) and lower ( P < 0.001 ) than for VMIs at 50 keV ( 0.74 ± 0.33 ) for 0.5 mgI/cc and a 35-cm phantom. For the same condition and an 8-mm lesion, iodine detectability from iodine maps ( AUC = 0.95 ± 0.01 ) was significantly lower ( P < 0.001 ) than both TLIs ( AUC = 0.99 ± 0.00 ) and VMIs ( AUC = 0.99 ± 0.01 ). VMIs at 50 keV had similar detectability to TLIs and both outperformed iodine maps. The lowest detectable iodine concentration was 0.5 mgI/cc for an 8-mm lesion and 1.0 mgI/cc for a 4-mm lesion.

RESUMEN

OBJECTIVES: The aim of this study was to evaluate if a high-resolution photon-counting detector computed tomography (PCD-CT) system with a 1024×1024 matrix reconstruction can improve the visualization of fine structures in the lungs compared with conventional high-resolution CT (HRCT). MATERIALS AND METHODS: Twenty-two adult patients referred for clinical chest HRCT (mean CTDI vol, 13.58 mGy) underwent additional dose-matched PCD-CT (mean volume CT dose index, 13.37 mGy) after written informed consent. Computed tomography images were reconstructed at a slice thickness of 1.5 mm and an image increment of 1 mm with our routine HRCT reconstruction kernels (B46 and Bv49) at 512 and 1024 matrix sizes for conventional energy-integrating detector (EID) CT scans. For PCD-CT, routine B46 kernel and an additional sharp kernel (Q65, unavailable for EID) images were reconstructed at 1024 matrix size. Two thoracic radiologists compared images from EID and PCD-CT noting the highest level bronchus clearly identified in each lobe of the right lung, and rating bronchial wall conspicuity of third- and fourth-order bronchi. Lung nodules were also compared with the B46/EID/512 images using a 5-point Likert scale. Statistical analysis was performed using a Wilcoxon signed rank test with a P < 0.05 considered significant. RESULTS: Compared with B46/EID/512, readers detected higher-order bronchi using B46/PCD/1024 and Q65/PCD/1024 images for every lung lobe (P < 0.0015), but in only the right middle lobe for B46/EID/1024 (P = 0.007). Readers were able to better identify bronchial walls of the third- and fourth-order bronchi better using the Q65/PCD/1024 images (mean Likert scores of 1.1 and 1.5), which was significantly higher compared with B46/EID/1024 or B46/PCD/1024 images (mean difference, 0.8; P < 0.0001). The Q65/PCD/1024 images had a mean nodule score of 1 ± 1.3 for reader 1, and -0.1 (0.9) for reader 2, with one reader having improved nodule evaluation scores for both PCD kernels (P < 0.001), and the other reader not identifying any increased advantage over B46/EID/1024 (P = 1.0). CONCLUSIONS: High-resolution lung PCD-CT with 1024 image matrix reconstruction increased radiologists' ability to visualize higher-order bronchi and bronchial walls without compromising nodule evaluation compared with current chest CT, creating an opportunity for radiologists to better evaluate airway pathology.

Asunto(s)

RESUMEN

Sliding wear is particularly problematic for micro- and nano-scale devices and applications, and is often studied at the small scale to develop practical and fundamental insights. While many methods exist to measure and quantify the wear of a sliding atomic force microscope (AFM) probe, many of these rely on specialized equipment and/or assumptions from continuum mechanics. Here we present a methodology that enables simple, purely AFM-based measurement of wear, in cases where the AFM probe wears to a flat plateau. The rate of volume removal is recast into a form that depends primarily on the time-varying contact area. This contact area is determined using images of sharp spikes, which are analyzed with a simple thresholding technique, rather than requiring sophisticated computer algorithms or continuum mechanics assumptions. This approach enables the rapid determination of volume lost, rate of material removal, normal stress, and interfacial shear stress at various points throughout the wear experiment. The method is demonstrated using silicon probes sliding on an aluminum oxide substrate. As a validation for the present method, direct imaging in the transmission electron microscope is used to verify the method's parameters and results. Overall, it is envisioned that this purely AFM-based methodology will enable higher-throughput wear experiments and direct hypothesis-based investigation into the science of wear and its dependence on different variables.

RESUMEN

Photon counting detector (PCD) based multi-energy CT is able to generate different types of images such as virtual monoenergetic images (VMIs) and material specific images (e.g., iodine maps) in addition to the conventional single energy images. The purpose of this study is to determine the image type that has optimal iodine detection and to determine the lowest detectable iodine concentration using a PCD-CT system. A 35 cm body phantom with iodine inserts of 4 concentrations and 2 sizes was scanned on a research PCD-CT system. For each iodine concentration, 80 repeated scans were performed and images were reconstructed for each energy threshold. In addition, VMIs at different keVs and iodine maps were also generated. CNR was measured for each type of images. A channelized Hotelling observer was used to assess iodine detectability after being validated with human observer studies, with area under the ROC curve (AUC) as a figure of merit. The agreement between model and human observer performance indicated that model observer could serve as an effective approach to determine optimal image type for the clinical practice and to determine the lowest detectable iodine concentration. Results demonstrated that for all size and concentration combinations, VMI at 70 keV had similar performance as that of threshold low images, both of which outperformed the iodine map images. At the AUC value of 0.8, iodine concentration as low as 0.2 mgI/cc could be detected for an 8 mm object and 0.5 mgI/cc for a 4 mm object with a 5 mm slice thickness.