Reduced Chest Computed Tomography Scan Length for Patients Positive for Coronavirus Disease 2019: Dose Reduction and Impact on Diagnostic Utility.

METHODS: This study used the Personalized Rapid Estimation of Dose in CT (PREDICT) tool to estimate patient-specific organ doses from CT image data. The PREDICT is a research tool that combines a linear Boltzmann transport equation solver for radiation dose map generation with deep learning algorithms for organ contouring. Computed tomography images from 74 subjects in the Medical Imaging Data Resource Center-RSNA International COVID-19 Open Radiology Database data set (chest CT of adult patients positive for COVID-19), which included expert annotations including "infectious opacities," were analyzed. First, the full z-scan length of the CT image data set was evaluated. Next, the z-scan length was reduced from the left hemidiaphragm to the top of the aortic arch. Generic dose reduction based on dose length product (DLP) and patient-specific organ dose reductions were calculated. The percentage of infectious opacities excluded from the reduced z-scan length was used to quantify the effect on diagnostic utility. RESULTS: Generic dose reduction, based on DLP, was 69%. The organ dose reduction ranged from approximately equal to 18% (breasts) to approximately equal to 64% (bone surface and bone marrow). On average, 12.4% of the infectious opacities were not included in the reduced z-coverage, per patient, of which 5.1% were above the top of the arch and 7.5% below the left hemidiaphragm. CONCLUSIONS: Limiting z-scan length of chest CTs reduced radiation dose without significantly compromising diagnostic utility in COVID-19 patients. The PREDICT demonstrated that patient-specific organ dose reductions varied from generic dose reduction based on DLP.

Dual energy CT reconstruction using the constrained one step spectral image reconstruction algorithm.

BACKGROUND: The constrained one-step spectral CT Image Reconstruction method (cOSSCIR) has been developed to estimate basis material maps directly from spectral CT data using a model of the polyenergetic x-ray transmissions and incorporating convex constraints into the inversion problem. This 'one-step' approach has been shown to stabilize the inversion in the case of photon-counting CT, and may provide similar benefits to dual-kV systems that utilize integrating detectors. Since the approach does not require the same rays be acquired for every spectral measurement, cOSSCIR can apply to dual energy protocols and systems used clinically, such as fast and slow kV switching systems and dual source scanning. PURPOSE: The purpose of this study is to investigate the use of cOSSCIR applied to dual-kV data, using both registered and unregistered spectral acquisitions, specifically slow and fast kV switching imaging protocols. For this application, cOSSCIR is investigated using inverse crime simulations and dual-kV experiments. This study is the first demonstration of cOSSCIR on the dual-kV reconstruction problem. METHODS: An integrating detector model was developed for the purpose of reconstructing dual-kV data, and an inverse crime study was used to validate the detector model within the cOSSCIR framework using a simulated pelvic phantom. Experiments were also used to evaluate cOSSCIR on the dual energy problem. Dual-kV data was obtained from a physical phantom containing analogs of adipose, bone, and liver tissues, with the aim of recovering the material coefficients in the bone and adipose basis material maps. cOSSCIR was applied to acquisitions where all rays performed both spectral measurements (registered) and fast and slow kV switching acquisitions (unregistered). cOSSCIR was also compared to two image-domain decomposition approaches, where image-domain methods are the conventional approach for decomposing unregistered spectral data. RESULTS: Simulations demonstrate the application of cOSSCIR to the dual-kV inversion problem by successfully recovering the material basis maps on ideal data, while further showing that unregistered data presents a more challenging inversion problem. In our experimental reconstructions, the recovered basis material coefficient errors were found to be less than 6.5% in the bone, adipose, and liver regions for both registered and unregistered protocols. Similarly, the errors were less than 4% in the 50 keV virtual mono-energetic images, and the recovered material decomposition vectors nearly overlap their corresponding ground-truth vectors. Additionally, a preliminary two material decomposition study of iodine quantification recovered an average concentration of 9.2 mg/mL from a 10 mg/mL experimental iodine analog. CONCLUSIONS: Using our integrating detector and spectral models, cOSCCIR is capable of accurately recovering material basis maps from dual-kV data for both registered and unregistered data. The material decomposition quantification compare favorably to the image domain approaches, and our results were not affected by the imaging protocol. Our results also suggest the extension of cOSSCIR to iodine quantification using two material decomposition.

Age Encoded Adversarial Learning for Pediatric CT Segmentation.

Organ segmentation from CT images is critical in the early diagnosis of diseases, progress monitoring, pre-operative planning, radiation therapy planning, and CT dose estimation. However, data limitation remains one of the main challenges in medical image segmentation tasks. This challenge is particularly huge in pediatric CT segmentation due to children's heightened sensitivity to radiation. In order to address this issue, we propose a novel segmentation framework with a built-in auxiliary classifier generative adversarial network (ACGAN) that conditions age, simultaneously generating additional features during training. The proposed conditional feature generation segmentation network (CFG-SegNet) was trained on a single loss function and used 2.5D segmentation batches. Our experiment was performed on a dataset with 359 subjects (180 male and 179 female) aged from 5 days to 16 years and a mean age of 7 years. CFG-SegNet achieved an average segmentation accuracy of 0.681 dice similarity coefficient (DSC) on the prostate, 0.619 DSC on the uterus, 0.912 DSC on the liver, and 0.832 DSC on the heart with four-fold cross-validation. We compared the segmentation accuracy of our proposed method with previously published U-Net results, and our network improved the segmentation accuracy by 2.7%, 2.6%, 2.8%, and 3.4% for the prostate, uterus, liver, and heart, respectively. The results indicate that our high-performing segmentation framework can more precisely segment organs when limited training images are available.

The performance of MLEM for dynamic imaging from simulated few-view, multi-pinhole SPECT.

Stationary small-animal SPECT systems are being developed for rapid dynamic imaging from limited angular views. This paper quantified, through simulations, the performance of Maximum Likelihood Expectation Maximization (MLEM) for reconstructing a time-activity curve (TAC) with uptake duration of a few seconds from a stationary, three-camera multi-pinhole SPECT system. The study also quantified the benefits of a heuristic method of initializing the reconstruction with a prior image reconstructed from a conventional number of views, for example from data acquired during the late-study portion of the dynamic TAC. We refer to MLEM reconstruction initialized by a prior-image initial guess (IG) as MLEM ig . The effect of the prior-image initial guess on the depiction of contrast between two regions of a static phantom was quantified over a range of angular sampling schemes. A TAC was modeled from the experimentally measured uptake of 99m Tc-hexamethylpropyleneamine oxime (HMPAO) in the rat lung. The resulting time series of simulated images was quantitatively analyzed with respect to the accuracy of the estimated exponential washin and washout parameters. In both static and dynamic phantom studies, the prior-image initial guess improved the spatial depiction of the phantom, for example improved definition of the cylinder boundaries and more accurate quantification of relative contrast between cylinders. For example in the dynamic study, there was ~50% error in relative contrast for MLEM reconstructions compared to ~25-30% error for MLEM ig . In the static phantom study, the benefits of the initial guess decreased as the number of views increased. The prior-image initial guess introduced an additive offset in the reconstructed dynamic images, likely due to biases introduced by the prior image. MLEM initialized with a uniform initial guess yielded images that faithfully reproduced the time dependence of the simulated TAC; there were no statistically significant differences in the mean exponential washin/washout parameters estimated from MLEM reconstructions compared to the true values. Washout parameters estimated from MLEM ig reconstructions did not differ significantly from the true values, however the estimated washin parameter differed significantly from the true value in some cases. Overall, MLEM reconstruction from few views and a uniform initial guess accurately quantified the time dependance of the TAC while introducing errors in the spatial depiction of the object. Initializing the reconstruction with a late-study initial guess improved spatial accuracy while decreasing temporal accuracy in some cases.

Constrained one-step material decomposition reconstruction of head CT data from a silicon photon-counting prototype.

BACKGROUND: Spectral CT material decomposition provides quantitative information but is challenged by the instability of the inversion into basis materials. We have previously proposed the constrained One-Step Spectral CT Image Reconstruction (cOSSCIR) algorithm to stabilize the material decomposition inversion by directly estimating basis material images from spectral CT data. cOSSCIR was previously investigated on phantom data. PURPOSE: This study investigates the performance of cOSSCIR using head CT datasets acquired on a clinical photon-counting CT (PCCT) prototype. This is the first investigation of cOSSCIR for large-scale, anatomically complex, clinical PCCT data. The cOSSCIR decomposition is preceded by a spectrum estimation and nonlinear counts correction calibration step to address nonideal detector effects. METHODS: Head CT data were acquired on an early prototype clinical PCCT system using an edge-on silicon detector with eight energy bins. Calibration data of a step wedge phantom were also acquired and used to train a spectral model to account for the source spectrum and detector spectral response, and also to train a nonlinear counts correction model to account for pulse pileup effects. The cOSSCIR algorithm optimized the bone and adipose basis images directly from the photon counts data, while placing a grouped total variation (TV) constraint on the basis images. For comparison, basis images were also reconstructed by a two-step projection-domain approach of Maximum Likelihood Estimation (MLE) for decomposing basis sinograms, followed by filtered backprojection (MLE + FBP) or a TV minimization algorithm (MLE + TVmin ) to reconstruct basis images. We hypothesize that the cOSSCIR approach will provide a more stable inversion into basis images compared to two-step approaches. To investigate this hypothesis, the noise standard deviation in bone and soft-tissue regions of interest (ROIs) in the reconstructed images were compared between cOSSCIR and the two-step methods for a range of regularization constraint settings. RESULTS: cOSSCIR reduced the noise standard deviation in the basis images by a factor of two to six compared to that of MLE + TVmin , when both algorithms were constrained to produce images with the same TV. The cOSSCIR images demonstrated qualitatively improved spatial resolution and depiction of fine anatomical detail. The MLE + TVmin algorithm resulted in lower noise standard deviation than cOSSCIR for the virtual monoenergetic images (VMIs) at higher energy levels and constraint settings, while the cOSSCIR VMIs resulted in lower noise standard deviation at lower energy levels and overall higher qualitative spatial resolution. There were no statistically significant differences in the mean values within the bone region of images reconstructed by the studied algorithms. There were statistically significant differences in the mean values within the soft-tissue region of the reconstructed images, with cOSSCIR producing mean values closer to the expected values. CONCLUSIONS: The cOSSCIR algorithm, combined with our previously proposed spectral model estimation and nonlinear counts correction method, successfully estimated bone and adipose basis images from high resolution, large-scale patient data from a clinical PCCT prototype. The cOSSCIR basis images were able to depict fine anatomical details with a factor of two to six reduction in noise standard deviation compared to that of the MLE + TVmin two-step approach.

Deep learning classification of deep ultraviolet fluorescence images toward intra-operative margin assessment in breast cancer.

Background: Breast-conserving surgery is aimed at removing all cancerous cells while minimizing the loss of healthy tissue. To ensure a balance between complete resection of cancer and preservation of healthy tissue, it is necessary to assess themargins of the removed specimen during the operation. Deep ultraviolet (DUV) fluorescence scanning microscopy provides rapid whole-surface imaging (WSI) of resected tissues with significant contrast between malignant and normal/benign tissue. Intra-operative margin assessment with DUV images would benefit from an automated breast cancer classification method. Methods: Deep learning has shown promising results in breast cancer classification, but the limited DUV image dataset presents the challenge of overfitting to train a robust network. To overcome this challenge, the DUV-WSI images are split into small patches, and features are extracted using a pre-trained convolutional neural network-afterward, a gradient-boosting tree trains on these features for patch-level classification. An ensemble learning approach merges patch-level classification results and regional importance to determine the margin status. An explainable artificial intelligence method calculates the regional importance values. Results: The proposed method's ability to determine the DUV WSI was high with 95% accuracy. The 100% sensitivity shows that the method can detect malignant cases efficiently. The method could also accurately localize areas that contain malignant or normal/benign tissue. Conclusion: The proposed method outperforms the standard deep learning classification methods on the DUV breast surgical samples. The results suggest that it can be used to improve classification performance and identify cancerous regions more effectively.

Estimation of organ and effective dose due to Compton backscatter security scans.

PURPOSE: To estimate organ and effective radiation doses due to backscatter security scanners using Monte Carlo simulations and a voxelized phantom set. METHODS: Voxelized phantoms of male and female adults and children were used with the GEANT4 toolkit to simulate a backscatter security scan. The backscatter system was modeled based on specifications available in the literature. The simulations modeled a 50 kVp spectrum with 1.0 mm-aluminum-equivalent filtration and a previously measured exposure of approximately 4.6 µR at 30 cm from the source. Photons and secondary interactions were tracked from the source until they reached zero kinetic energy or exited from the simulation's boundaries. The energy deposited in the phantoms' respective organs was tallied and used to calculate total organ dose and total effective dose for frontal, rear, and full scans with subjects located 30 and 75 cm from the source. RESULTS: For a full screen, all phantoms' total effective doses were below the established 0.25 µSv standard, with an estimated maximum total effective dose of 0.07 µSv for full screen of a male child. The estimated maximum organ dose due to a full screen was 1.03 µGy, deposited in the adipose tissue of the male child phantom when located 30 cm from the source. All organ dose estimates had a coefficient of variation of less than 3% for a frontal scan and less than 11% for a rear scan. CONCLUSIONS: Backscatter security scanners deposit dose in organs beyond the skin. The effective dose is below recommended standards set by the Health Physics Society (HPS) and the American National Standards Institute (ANSI) assuming the system provides a maximum exposure of approximately 4.6 µR at 30 cm.

A database for estimating organ dose for coronary angiography and brain perfusion CT scans for arbitrary spectra and angular tube current modulation.

PURPOSE: The purpose of this study was to develop a database for estimating organ dose in a voxelized patient model for coronary angiography and brain perfusion CT acquisitions with any spectra and angular tube current modulation setting. The database enables organ dose estimation for existing and novel acquisition techniques without requiring Monte Carlo simulations. METHODS: The study simulated transport of monoenergetic photons between 5 and 150 keV for 1000 projections over 360° through anthropomorphic voxelized female chest and head (0° and 30° tilt) phantoms and standard head and body CTDI dosimetry cylinders. The simulations resulted in tables of normalized dose deposition for several radiosensitive organs quantifying the organ dose per emitted photon for each incident photon energy and projection angle for coronary angiography and brain perfusion acquisitions. The values in a table can be multiplied by an incident spectrum and number of photons at each projection angle and then summed across all energies and angles to estimate total organ dose. Scanner-specific organ dose may be approximated by normalizing the database-estimated organ dose by the database-estimated CTDI(vol) and multiplying by a physical CTDI(vol) measurement. Two examples are provided demonstrating how to use the tables to estimate relative organ dose. In the first, the change in breast and lung dose during coronary angiography CT scans is calculated for reduced kVp, angular tube current modulation, and partial angle scanning protocols relative to a reference protocol. In the second example, the change in dose to the eye lens is calculated for a brain perfusion CT acquisition in which the gantry is tilted 30° relative to a nontilted scan. RESULTS: Our database provides tables of normalized dose deposition for several radiosensitive organs irradiated during coronary angiography and brain perfusion CT scans. Validation results indicate total organ doses calculated using our database are within 1% of those calculated using Monte Carlo simulations with the same geometry and scan parameters for all organs except red bone marrow (within 6%), and within 23% of published estimates for different voxelized phantoms. Results from the example of using the database to estimate organ dose for coronary angiography CT acquisitions show 2.1%, 1.1%, and -32% change in breast dose and 2.1%, -0.74%, and 4.7% change in lung dose for reduced kVp, tube current modulated, and partial angle protocols, respectively, relative to the reference protocol. Results show -19.2% difference in dose to eye lens for a tilted scan relative to a nontilted scan. The reported relative changes in organ doses are presented without quantification of image quality and are for the sole purpose of demonstrating the use of the proposed database. CONCLUSIONS: The proposed database and calculation method enable the estimation of organ dose for coronary angiography and brain perfusion CT scans utilizing any spectral shape and angular tube current modulation scheme by taking advantage of the precalculated Monte Carlo simulation results. The database can be used in conjunction with image quality studies to develop optimized acquisition techniques and may be particularly beneficial for optimizing dual kVp acquisitions for which numerous kV, mA, and filtration combinations may be investigated.

Addressing CT metal artifacts using photon-counting detectors and one-step spectral CT image reconstruction.

PURPOSE: The constrained one-step spectral CT image reconstruction (cOSSCIR) algorithm with a nonconvex alternating direction method of multipliers optimizer is proposed for addressing computed tomography (CT) metal artifacts caused by beam hardening, noise, and photon starvation. The quantitative performance of cOSSCIR is investigated through a series of photon-counting CT simulations. METHODS: cOSSCIR directly estimates basis material maps from photon-counting data using a physics-based forward model that accounts for beam hardening. The cOSSCIR optimization framework places constraints on the basis maps, which we hypothesize will stabilize the decomposition and reduce streaks caused by noise and photon starvation. Another advantage of cOSSCIR is that the spectral data need not be registered, so that a ray can be used even if some energy window measurements are unavailable. Photon-counting CT acquisitions of a virtual pelvic phantom with low-contrast soft tissue texture and bilateral hip prostheses were simulated. Bone and water basis maps were estimated using the cOSSCIR algorithm and combined to form a virtual monoenergetic image for the evaluation of metal artifacts. The cOSSCIR images were compared to a "two-step" decomposition approach that first estimated basis sinograms using a maximum likelihood algorithm and then reconstructed basis maps using an iterative total variation constrained least-squares optimization (MLE+TV min $_{\text{min}}$ ). Images were also compared to a nonspectral TV min $_{\text{min}}$ reconstruction of the total number of counts detected for each ray with and without normalized metal artifact reduction (NMAR) applied. The simulated metal density was increased to investigate the effects of increasing photon starvation. The quantitative error and standard deviation in regions of the phantom were compared across the investigated algorithms. The ability of cOSSCIR to reproduce the soft-tissue texture, while reducing metal artifacts, was quantitatively evaluated. RESULTS: Noiseless simulations demonstrated the convergence of the cOSSCIR and MLE+TV min $_{\text{min}}$ algorithms to the correct basis maps in the presence of beam-hardening effects. When noise was simulated, cOSSCIR demonstrated a quantitative error of -1 HU, compared to 2 HU error for the MLE+TV min $_{\text{min}}$ algorithm and -154 HU error for the nonspectral TV min $_{\text{min}}$ +NMAR algorithm. For the cOSSCIR algorithm, the standard deviation in the central iodine region of interest was 20 HU, compared to 299 HU for the MLE+TV min $_{\text{min}}$ algorithm, 41 HU for the MLE+TV min $_{\text{min}}$ +Mask algorithm that excluded rays through metal, and 55 HU for the nonspectral TV min $_{\text{min}}$ +NMAR algorithm. Increasing levels of photon starvation did not impact the bias or standard deviation of the cOSSCIR images. cOSSCIR was able to reproduce the soft-tissue texture when an appropriate regularization constraint value was selected. CONCLUSIONS: By directly inverting photon-counting CT data into basis maps using an accurate physics-based forward model and a constrained optimization algorithm, cOSSCIR avoids metal artifacts due to beam hardening, noise, and photon starvation. The cOSSCIR algorithm demonstrated improved stability and accuracy compared to a two-step method of decomposition followed by reconstruction.

Automated assessment of breast margins in deep ultraviolet fluorescence images using texture analysis.

Microscopy with ultraviolet surface excitation (MUSE) is increasingly studied for intraoperative assessment of tumor margins during breast-conserving surgery to reduce the re-excision rate. Here we report a two-step classification approach using texture analysis of MUSE images to automate the margin detection. A study dataset consisting of MUSE images from 66 human breast tissues was constructed for model training and validation. Features extracted using six texture analysis methods were investigated for tissue characterization, and a support vector machine was trained for binary classification of image patches within a full image based on selected feature subsets. A weighted majority voting strategy classified a sample as tumor or normal. Using the eight most predictive features ranked by the maximum relevance minimum redundancy and Laplacian scores methods has achieved a sample classification accuracy of 92.4% and 93.0%, respectively. Local binary pattern alone has achieved an accuracy of 90.3%.

Pediatric chest-abdomen-pelvis and abdomen-pelvis CT images with expert organ contours.

PURPOSE: Organ autosegmentation efforts to date have largely been focused on adult populations, due to limited availability of pediatric training data. Pediatric patients may present additional challenges for organ segmentation. This paper describes a dataset of 359 pediatric chest-abdomen-pelvis and abdomen-pelvis Computed Tomography (CT) images with expert contours of up to 29 anatomical organ structures to aid in the evaluation and development of autosegmentation algorithms for pediatric CT imaging. ACQUISITION AND VALIDATION METHODS: The dataset collection consists of axial CT images in Digital Imaging and Communications in Medicine (DICOM) format of 180 male and 179 female pediatric chest-abdomen-pelvis or abdomen-pelvis exams acquired from one of three CT scanners at Children's Wisconsin. The datasets represent random pediatric cases based upon routine clinical indications. Subjects ranged in age from 5 days to 16 years, with a mean age of 7 years. The CT acquisition, contrast, and reconstruction protocols varied across the scanner models and patients, with specifications available in the DICOM headers. Expert contours were manually labeled for up to 29 organ structures per subject. Not all contours are available for all subjects, due to limited field of view or unreliable contouring due to high noise. DATA FORMAT AND USAGE NOTES: The data are available on The Cancer Imaging Archive (TCIA_ (https://www.cancerimagingarchive.net/) under the collection Pediatric-CT-SEG. The axial CT image slices for each subject are available in DICOM format. The expert contours are stored in a single DICOM RTSTRUCT file for each subject. The contour names are listed in Table 2. POTENTIAL APPLICATIONS: This dataset will enable the evaluation and development of organ autosegmentation algorithms for pediatric populations, which exhibit variations in organ shape and size across age. Automated organ segmentation from CT images has numerous applications including radiation therapy, diagnostic tasks, surgical planning, and patient-specific organ dose estimation.

Region-of-interest material decomposition from truncated energy-resolved CT.

PURPOSE: Energy-resolved CT using photon-counting detectors has the potential to provide improved material decomposition compared to dual-kVp approaches. However, available photon-counting detectors are susceptible to pulse-pileup artifacts, especially at the periphery of the field of view (FOV) where the object attenuation is low compared to the center of the FOV. Pulse pileup may be avoided by imaging a region-of-interest (ROI) where the dynamic range is expected to be limited. This work investigated performing material decomposition and reconstructing ROI basis images from truncated energy-resolved data. METHODS: A method is proposed to reconstruct images of basis functions primarily contained within the ROI, such as targeted or localized K-edge contrast agents. Material decomposition is performed independently for each ray in the sinogram, followed by filtered backprojection from the truncated data encompassing the ROI. A second method is proposed that uses a prior conventional energy-integrating image to estimate energy-resolved data outside the ROI. The measured and estimated energy-resolved data are decomposed into basis projections and merged into basis sinograms of the full FOV. Basis images of the ROI are then reconstructed through filtered backprojection. This method is most easily applied to objects that do not contain K-edge contrast agents outside the ROI. Simulations of a voxelized thorax phantom with iodine in the blood pool and a detector with five energy bins were performed. Full FOV, truncated, and truncated data merged with data estimated from the prior energy-integrating image were decomposed into Compton, photoelectric, and iodine basis functions. An empirical weighting factor was determined to blend the merged sinogram at the boundary of the truncated data. The effects of noise and misalignment in the prior image were also quantified. Basis images of the central 15 cm × 15 cm ROI containing the heart were reconstructed via filtered backprojection. Basis image accuracy was quantified relative to gold-standard basis images reconstructed from full FOV energy-resolved data. RESULTS: The error in the iodine basis image reconstructed from truncated energy-resolved data without prior information was less than 1% for the central 7 cm of the 7.5-cm-radius ROI and 3% at the edge of the ROI. When the truncated and estimated basis sinograms were blended, the error was below 1% throughout the ROI for photoelectric basis images and ranged from 1% at the center of the ROI to 4% at the edge for the Compton basis image. CONCLUSIONS: The density of localized K-edge contrast agents can be estimated to within 1% error using filtered back projection without prior information. For noncontrast and localized-contrast scans, ROI images of general basis functions can be reconstructed to within a few percent error using a prior energy-integrating image. The ability to perform material decomposition for a limited ROI may facilitate energy-resolved CT with available photon-counting detectors.

Experimental study of photon-counting CT neural network material decomposition under conditions of pulse pileup.

Purpose: We investigated the performance of a neural network (NN) material decomposition method under varying pileup conditions. Approach: Experiments were performed at tube current settings that provided count rates incident on the detector through air equal to 9%, 14%, 27%, 40%, and 54% of the maximum detector count rate. An NN was trained for each count-rate level using transmission measurements through known thicknesses of basis materials (PMMA and aluminum). The NN trained for each count-rate level was applied to x-ray transmission measurements through test materials and to CT data of a rod phantom. Material decomposition error was evaluated as the distance in basis material space between the estimated thicknesses and ground truth. Results: There was no clear trend between count-rate level and material decomposition error for all test materials except neoprene. As an example result, Teflon error was 0.33 cm at the 9% count-rate level and 0.12 cm at the 54% count-rate level for the x-ray transmission experiments. Decomposition error increased with count-rate level for the neoprene test case, with 0.65-cm error at 9% count-rate level and 1.14-cm error at the 54% count-rate level. In the CT study, material decomposition error decreased with increasing incident count rate. For example, the material decomposition error for Teflon was 0.089, 0.066, 0.054 at count-rate levels of 14%, 27%, and 40%, respectively. Conclusions: Results demonstrate over a range of incident count-rate levels that an NN trained at a specific count-rate level can learn the relationship between photon-counting spectral measurements and basis material thicknesses.

Validation of a deterministic linear Boltzmann transport equation solver for rapid CT dose computation using physical dose measurements in pediatric phantoms.

PURPOSE: The risk of inducing cancer to patients undergoing CT examinations has motivated efforts for CT dose estimation, monitoring, and reduction, especially among pediatric population. The method investigated in this study is Acuros CTD (Varian Medical Systems, Palo Alto, CA), a deterministic linear Boltzmann transport equation (LBTE) solver aimed at generating rapid and reliable dose maps of CT exams. By applying organ contours, organ doses can also be obtained, thus patient-specific organ dose estimates can be provided. This study experimentally validated Acuros against measurements performed on a clinical CT system using a range of physical pediatric anthropomorphic phantoms and acquisition protocols. METHODS: The study consisted of (1) the acquisition of dose measurements on a clinical CT scanner through thermoluminescent dosimeters (TLDs), and (2) the modeling in the Acuros platform of the measurement set up, which includes the modeling of the CT scanner and of the anthropomorphic phantoms. For the measurements, 1-year-old, 5-year-old, and 10-year-old anthropomorphic phantoms of the CIRS ATOM family were used. TLDs were placed in selected organ locations such as stomach, liver, lungs, and heart. The pediatric phantoms were scanned helically with the GE Discovery 750 HD clinical scanner for several examination protocols. For the simulations in Acuros, scanner-specific input, such as bowtie filters, overrange collimation, and tube current modulation schemes, were modeled. These scanner complexities were implemented by defining discretized X-ray beams whose spectral distribution, defined in Acuros by only six energy bins, varied across fan angle, cone angle, and slice position. The images generated during the CT acquisitions were used to create the geometrical models, by applying thresholding algorithms and assigning materials to the HU values. The TLDs were contoured in the phantom models as sensitive cylindrical volumes at the locations selected for dosimeters placement, to provide dose estimates, in terms of dose per unit photon. To compare measured doses with dose estimates, a calibration factor was derived from the CTDIvol displayed by the scanner, to account for the number of photons emitted by the X-ray tube during the procedure. RESULTS: The differences of the measured and estimated doses, in terms of absolute % errors, were within 13% for 153 TLD locations, with an error of 17% at the stomach for one study with the 10-year-old phantom. Root-mean-squared-errors (RMSE) across all TLD locations for all configurations were in the range of 3%-8%, with Acuros providing dose estimates in a time range of a few seconds up to 2 min. CONCLUSIONS: An overall good agreement between measurements and simulations was achieved, with average RMSE of 6% across all cases. The results demonstrate that Acuros can model a specific clinical scanner despite the required discretization in spatial and energy domains. The proposed deterministic tool has the potential to be part of a near real-time individualized dosimetry monitoring system for CT applications, providing patient-specific organ dose estimates.

Automated image processing pipeline for adaptive optics scanning light ophthalmoscopy.

To mitigate the substantial post-processing burden associated with adaptive optics scanning light ophthalmoscopy (AOSLO), we have developed an open-source, automated AOSLO image processing pipeline with both "live" and "full" modes. The live mode provides feedback during acquisition, while the full mode is intended to automatically integrate the copious disparate modules currently used in generating analyzable montages. The mean (±SD) lag between initiation and montage placement for the live pipeline was 54.6 ± 32.7s. The full pipeline reduced overall human operator time by 54.9 ± 28.4%, with no significant difference in resultant cone density metrics. The reduced overhead decreases both the technical burden and operating cost of AOSLO imaging, increasing overall clinical accessibility.

CT energy weighting in the presence of scatter and limited energy resolution.

PURPOSE: Energy-resolved CT has the potential to improve the contrast-to-noise ratio (CNR) through optimal weighting of photons detected in energy bins. In general, optimal weighting gives higher weight to the lower energy photons that contain the most contrast information. However, low-energy photons are generally most corrupted by scatter and spectrum tailing, an effect caused by the limited energy resolution of the detector. This article first quantifies the effects of spectrum tailing on energy-resolved data, which may also be beneficial for material decomposition applications. Subsequently, the combined effects of energy weighting, spectrum tailing, and scatter are investigated through simulations. METHODS: The study first investigated the effects of spectrum tailing on the estimated attenuation coefficients of homogeneous slab objects. Next, the study compared the CNR and artifact performance of images simulated with varying levels of scatter and spectrum tailing effects, and reconstructed with energy integrating, photon-counting, and two optimal linear weighting methods: Projection-based and image-based weighting. Realistic detector energy-response functions were simulated based on a previously proposed model. The energy-response functions represent the probability that a photon incident on the detector at a particular energy will be detected at a different energy. Realistic scatter was simulated with Monte Carlo methods. RESULTS: Spectrum tailing resulted in a negative shift in the estimated attenuation coefficient of slab objects compared to an ideal detector. The magnitude of the shift varied with material composition, increased with material thickness, and decreased with photon energy. Spectrum tailing caused cupping artifacts and CT number inaccuracies in images reconstructed with optimal energy weighting, and did not impact images reconstructed with photon counting weighting. Spectrum tailing did not significantly impact the CNR in reconstructed images. Scatter reduced the CNR for all energy-weighting methods; however, the effect was greater for optimal energy weighting. For example, optimal energy weighting improved the CNR of iodine and water compared to energy-integrating weighting by a factor of approximately 1.45 in the absence of scatter and by a factor of approximately 1.1 in the presence of scatter (8.9 degrees cone angle, SPR 0.5). Without scatter correction, the difference in CNR resulting from photon-counting and optimal energy weighting was negligible (< 15%) for cone angles greater than 4.4 degrees (SPR > 0.3). Optimal weights combined with deterministic scatter correction provided a 1.3 and 1.1 improvement in CNR compared to energy-integrating and photon-counting weighting, respectively, for the 8.9 degrees cone angle simulation. In the absence of spectrum tailing, image-based weighting demonstrated reduced cupping artifact compared to projection-based weighting; however, both weighting methods exhibited similar cupping artifacts when spectrum tailing was simulated. There were no statistically significant differences in the CNR resulting from projection an image-based weighting for any of the simulated conditions. CONCLUSIONS: Optimal linear energy weighting introduces artifacts and CT number inaccuracies due to spectrum tailing. While optimal energy weighting has the potential to improve CNR compared to conventional weighting methods, the benefits are reduced as scatter increases. Efficient methods for reducing scatter and correcting spectrum tailing effects are required to obtain the highest benefit from optimal energy weighting.

Experimental investigation of neural network estimator and transfer learning techniques for K-edge spectral CT imaging.

PURPOSE: Spectral computed tomography (CT) material decomposition algorithms require accurate physics-based models or empirically derived models. This study investigates a machine learning algorithm and transfer learning techniques for Spectral CT imaging of K-edge contrast agents using simulated and experimental measurements. METHODS: A feed forward multilayer perceptron was implemented and trained on data acquired using a step wedge phantom containing acrylic, aluminum, and gadolinium materials. The neural network estimator was evaluated by scanning a rod phantom with varying dilutions of gadolinium oxide nanoparticles and by scanning a rat leg specimen with injected nanoparticles on a bench-top photon-counting computed tomography system. The algorithm decomposed each spectral projection measurement into path lengths of acrylic and aluminum and mass lengths of gadolinium. Each basis material sinogram was reconstructed into basis material images using filtered backprojection. Machine learning techniques of data standardization, transfer learning from aggregated pixel data, and transfer learning from simulations were investigated to improve image quality. The algorithm was compared to a previously published empirical method based on a linear approximation and calibration error look-up tables. RESULTS: The combined transfer learning techniques did not improve quantification in the rod phantom and provided only a small qualitative improvement in ring artifacts. Transfer learning from aggregated pixel data and from simulations improved the qualitative image quality of the rat specimen, for which the calibration data were limited. Transfer learning from aggregated pixel data and simulations estimated 3.26, 6.26, and 12.45 mg/mL Gd concentrations compared to true 2.72, 5.44, and 10.88 mg/mL concentrations in the rod phantom. Additionally, the neural networks were able to separate the soft tissue, bone, and gadolinium nanoparticles of the ex vivo rat leg specimen into the different basis images. CONCLUSIONS: The results demonstrate that empirical K-edge imaging from calibration measurements using machine learning and transfer learning is possible without explicit models of material attenuations, incident spectra, or the detector response.

Deterministic linear Boltzmann transport equation solver for patient-specific CT dose estimation: Comparison against a Monte Carlo benchmark for realistic scanner configurations and patient models.

PURPOSE: Epidemiological evidence suggests an increased risk of cancer related to computed tomography (CT) scans, with children exposed to greater risk. The purpose of this work is to test the reliability of a linear Boltzmann transport equation (LBTE) solver for rapid and patient-specific CT dose estimation. This includes building a flexible LBTE framework for modeling modern clinical CT scanners and to validate the resulting dose maps across a range of realistic scanner configurations and patient models. METHODS: In this study, computational tools were developed for modeling CT scanners, including a bowtie filter, overrange collimation, and tube current modulation. The LBTE solver requires discretization in the spatial, angular, and spectral dimensions, which may affect the accuracy of scanner modeling. To investigate these effects, this study evaluated the LBTE dose accuracy for different discretization parameters, scanner configurations, and patient models (male, female, adults, pediatric). The method used to validate the LBTE dose maps was the Monte Carlo code Geant4, which provided ground truth dose maps. LBTE simulations were implemented on a GeForce GTX 1080 graphic unit, while Geant4 was implemented on a distributed cluster of CPUs. RESULTS: The agreement between Geant4 and the LBTE solver quantifies the accuracy of the LBTE, which was similar across the different protocols and phantoms. The results suggest that 18 views per rotation provides sufficient accuracy, as no significant improvement in the accuracy was observed by increasing the number of projection views. Considering this discretization, the LBTE solver average simulation time was approximately 30 s. However, in the LBTE solver the phantom model was implemented with a lower voxel resolution with respect to Geant4, as it is limited by the memory of the GPU. Despite this discretization, the results showed a good agreement between the LBTE and Geant4, with root mean square error of the dose in organs of approximately 3.5% for most of the studied configurations. CONCLUSIONS: The LBTE solver is proposed as an alternative to Monte Carlo for patient-specific organ dose estimation. This study demonstrated accurate organ dose estimates for the rapid LBTE solver when considering realistic aspects of CT scanners and a range of phantom models. Future plans will combine the LBTE framework with deep learning autosegmentation algorithms to provide near real-time patient-specific organ dose estimation.

Rapid assessment of breast tumor margins using deep ultraviolet fluorescence scanning microscopy.

SIGNIFICANCE: Re-excision rates for women with invasive breast cancer undergoing breast conserving surgery (or lumpectomy) have decreased in the past decade but remain substantial. This is mainly due to the inability to assess the entire surface of an excised lumpectomy specimen efficiently and accurately during surgery. AIM: The goal of this study was to develop a deep-ultraviolet scanning fluorescence microscope (DUV-FSM) that can be used to accurately and rapidly detect cancer cells on the surface of excised breast tissue. APPROACH: A DUV-FSM was used to image the surfaces of 47 (31 malignant and 16 normal/benign) fresh breast tissue samples stained in propidium iodide and eosin Y solutions. A set of fluorescence images were obtained from each sample using low magnification (4 × ) and fully automated scanning. The images were stitched to form a color image. Three nonmedical evaluators were trained to interpret and assess the fluorescence images. Nuclear-cytoplasm ratio (N/C) was calculated and used for tissue classification. RESULTS: DUV-FSM images a breast sample with subcellular resolution at a speed of 1.0 min / cm2. Fluorescence images show excellent visual contrast in color, tissue texture, cell density, and shape between invasive carcinomas and their normal counterparts. Visual interpretation of fluorescence images by nonmedical evaluators was able to distinguish invasive carcinoma from normal samples with high sensitivity (97.62%) and specificity (92.86%). Using N/C alone was able to differentiate patch-level invasive carcinoma from normal breast tissues with reasonable sensitivity (81.5%) and specificity (78.5%). CONCLUSIONS: DUV-FSM achieved a good balance between imaging speed and spatial resolution with excellent contrast, which allows either visual or quantitative detection of invasive cancer cells on the surfaces of a breast surgical specimen.

Optimal "image-based" weighting for energy-resolved CT.

This paper investigates a method of reconstructing images from energy-resolved CT data with negligible beam-hardening artifacts and improved contrast-to-nosie ratio (CNR) compared to conventional energy-weighting methods. Conceptually, the investigated method first reconstructs separate images from each energy bin. The final image is a linear combination of the energy-bin images, with the weights chosen to maximize the CNR in the final image. The optimal weight of a particular energy-bin image is derived to be proportional to the contrast-to-noise-variance ratio in that image. The investigated weighting method is referred to as "image-based" weighting, although, as will be described, the weights can be calculated and the energy-bin data combined prior to reconstruction. The performance of optimal image-based energy weighting with respect to CNR and beam-hardening artifacts was investigated through simulations and compared to that of energy integrating, photon counting, and previously studied optimal "projection-based" energy weighting. Two acquisitions were simulated: dedicated breast CT and a conventional thorax scan. The energy-resolving detector was simulated with five energy bins. Four methods of estimating the optimal weights were investigated, including task-specific and task-independent methods and methods that require a single reconstruction versus multiple reconstructions. Results demonstrated that optimal image-based weighting improved the CNR compared to energy-integrating weighting by factors of 1.15-1.6 depending on the task. Compared to photon-counting weighting, the CNR improvement ranged from 1.0 to 1.3. The CNR improvement factors were comparable to those of projection-based optimal energy weighting. The beam-hardening cupping artifact increased from 5.2% for energy-integrating weighting to 12.8% for optimal projection-based weighting, while optimal image-based weighting reduced the cupping to 0.6%. Overall, optimal image-based energy weighting provides images with negligible beam-hardening artifacts and improved CNR compared to energy-integrating and photon-counting methods.