Large Slice Thickness Research Articles

Reconstruction and completion of high-resolution 3D cardiac shapes using anisotropic CMRI segmentations and continuous implicit neural representations

Since the onset of computer-aided diagnosis in medical imaging, voxel-based segmentation has emerged as the primary methodology for automatic analysis of left ventricle (LV) function and morphology in cardiac magnetic resonance images (CMRI). In standard clinical practice, simultaneous multi-slice 2D cine short-axis MR imaging is performed under multiple breath-holds resulting in highly anisotropic 3D images. Furthermore, sparse-view CMRI often lacks whole heart coverage caused by large slice thickness and often suffers from inter-slice misalignment induced by respiratory motion. Therefore, these volumes only provide limited information about the true 3D cardiac anatomy which may hamper highly accurate assessment of functional and anatomical abnormalities. To address this, we propose a method that learns a continuous implicit function representing 3D LV shapes by training an auto-decoder. For training, high-resolution segmentations from cardiac CT angiography are used. The ability of our approach to reconstruct and complete high-resolution shapes from manually or automatically obtained sparse-view cardiac shape information is evaluated by using paired high- and low-resolution CMRI LV segmentations. The results show that the reconstructed LV shapes have an unconstrained subvoxel resolution and appear smooth and plausible in through-plane direction. Furthermore, Bland-Altman analysis reveals that reconstructed high-resolution ventricle volumes are closer to the corresponding reference volumes than reference low-resolution volumes with bias of [limits of agreement] −3.51 [−18.87, 11.85] mL, and 12.96 [−10.01, 35.92] mL respectively. Finally, the results demonstrate that the proposed approach allows recovering missing shape information and can indirectly correct for limited motion-induced artifacts.

Optimal slice thickness for improved accuracy of quantitative analysis of fluorescent cell and microsphere distribution in cryo-images

Cryo-imaging has been effectively used to study the biodistribution of fluorescent cells or microspheres in animal models. Sequential slice-by-slice fluorescent imaging enables detection of fluorescent cells or microspheres for corresponding quantification of their distribution in tissue. However, if slices are too thin, there will be data overload and excessive scan times. If slices are too thick, then cells can be missed. In this study, we developed a model for detection of fluorescent cells or microspheres to aid optimal slice thickness determination. Key factors include: section thickness (X), fluorescent cell intensity (Ifluo), effective tissue attenuation coefficient (μT), and a detection threshold (T). The model suggests an optimal slice thickness value that provides near-ideal sensitivity while minimizing scan time. The model also suggests a correction method to compensate for missed cells in the case that image data were acquired with overly large slice thickness. This approach allows cryo-imaging operators to use larger slice thickness to expedite the scan time without significant loss of cell count. We validated the model using real data from two independent studies: fluorescent microspheres in a pig heart and fluorescently labeled stem cells in a mouse model. Results show that slice thickness and detection sensitivity relationships from simulations and real data were well-matched with 99% correlation and 2% root-mean-square (RMS) error. We also discussed the detection characteristics in situations where key assumptions of the model were not met such as fluorescence intensity variation and spatial distribution. Finally, we show that with proper settings, cryo-imaging can provide accurate quantification of the fluorescent cell biodistribution with remarkably high recovery ratios (number of detections/delivery). As cryo-imaging technology has been used in many biological applications, our optimal slice thickness determination and data correction methods can play a crucial role in further advancing its usability and reliability.

Computed Tomography slice interpolation in the longitudinal direction based on deep learning techniques: To reduce slice thickness or slice increment without dose increase.

Large slice thickness or slice increment causes information insufficiency of Computed Tomography (CT) data in the longitudinal direction, which degrades the quality of CT-based diagnosis. Traditional approaches such as high-resolution computed tomography (HRCT) and linear interpolation can solve this problem. However, HRCT suffers from dose increase, and linear interpolation causes artifacts. In this study, we propose a deep-learning-based approach to reconstruct densely sliced CT from sparsely sliced CT data without any dose increase. The proposed method reconstructs CT images from neighboring slices using a U-net architecture. To prevent multiple reconstructed slices from influencing one another, we propose a parallel architecture in which multiple U-net architectures work independently. Moreover, for a specific organ (i.e., the liver), we propose a range-clip technique to improve reconstruction quality, which enhances the learning of CT values within this organ by enlarging the range of the training data. CT data from 130 patients were collected, with 80% used for training and the remaining 20% used for testing. Experiments showed that our parallel U-net architecture reduced the mean absolute error of CT values in the reconstructed slices by 22.05%, and also reduced the incidence of artifacts around the boundaries of target organs, compared with linear interpolation. Further improvements of 15.12%, 11.04%, 10.94%, and 10.63% were achieved for the liver, left kidney, right kidney, and stomach, respectively, using the proposed range-clip algorithm. Also, we compared the proposed architecture with original U-net method, and the experimental results demonstrated the superiority of our approach.

Verification of dose distribution in high dose-rate brachytherapy for cervical cancer using a normoxic N-vinylpyrrolidone polymer gel dosimeter.

The polymer gel dosimeter has been proposed for use as a 3D dosimeter for complex dose distribution measurement of high dose-rate (HDR) brachytherapy. However, various shapes of catheter/applicator for sealed radioactive source transport used in clinical cases must be placed in the gel sample. The absorbed dose readout for the magnetic resonance (MR)-based polymer gel dosimeters requires calibration data for the dose-transverse relaxation rate (R2) response. In this study, we evaluated in detail the dose uncertainty and dose resolution of three calibration methods, the multi-sample and distance methods using the Ir-192 source and the linear accelerator (linac) method using 6MV X-rays. The use of Ir-192 sources increases dose uncertainty with steep dose gradients. We clarified that the uniformly irradiated gel sample improved the signal-to-noise ratio (SNR) due to the large slice thickness of MR images and could acquire an accurate calibration curve using the linac method. The curved tandem and ovoid applicator used for intracavitary irradiation of HDR brachytherapy for cervical cancer were reproduced with a glass tube to verify the dose distribution. The results of comparison with the treatment planning system (TPS) calculation by gamma analysis on the 3%/2mm criterion were in good agreement with a gamma pass rate of 90%. In addition, the prescription dose could be evaluated accurately. We conclude that it is easy to place catheter/applicator in the polymer gel dosimeters, making them a useful tool for verifying the 3D dose distribution of HDR brachytherapy with accurate calibration methods.

Automated cervical tumor segmentation on MR images using multi-view feature attention network

Precise cervical cancer treatment highly relies on accurate segmentation of cervical tumors from magnetic resonance (MR) images. However, this task is challenged by the inhomogeneous intensity distributions in MR images and the large variations in tumor shapes and locations. The large slice thickness further results in limited inter-slice correlations and 3D contextual information can be utilized, which influences the segmentation performance greatly. To tackle the above challenges and make full use of the 3D contextual information, a multi-view feature attention-based segmentation network (MVFA-Net) is proposed in this study. Toward the weak correlations among adjacent MR slices, features from different views of the volumetric MR images are extracted and treated individually and then fused by a channel-wise attention model. A cervical MR data set collected from 160 cervical cancer patients was employed to evaluate the performance of the proposed MVFA-Net. In comparison experiments, the proposed MVFA-Net outperforms the other eight medical image segmentation networks 2.6%-11.1% and 0.39 mm-0.97 mm on Dice similarity coefficient (DSC) and Average surface distance (ASD), respectively. Extensive ablation studies demonstrate the effectiveness of the proposed multi-view attention block and MVFA-Net. Additionally, with the trained segmentation network, no more than 6 s will be taken to segment one unseen patient, which is highly efficient for clinical practice. The presented segmentation network might be useful for cervical cancer treatment routine to improve the segmentation accuracy, consistency, and efficiency. The code is publicly available at: https://github.com/xyndameinv/MVFA-Net.

Short- and Medium-Wave Infrared Drying of Cantaloupe (Cucumis melon L.) Slices: Drying Kinetics and Process Parameter Optimization

The main objective of the present work was to study the drying kinetics and obtain the optimum process parameters of cantaloupe slices using short-and medium-wave infrared radiation (SMIR) drying technology. The effect of three independent variables of infrared radiation temperature (55–65 °C), slice thickness (5–9 mm) and radiation distance (80–160 mm) on the L value, color difference (∆E), hardness and vitamin C content were investigated by using the Response Surface Methodology (RSM). The results showed that the Page model can adequately predict the moisture content between 55 and 65 °C (R2 > 0.99). The effective moisture diffusivity (Deff) varied from 5.26 × 10−10 to 2.09 × 10−9 m2/s and the activation energy (Ea) of the SMIR drying was 31.84 kJ/mol. Infrared radiation temperature and slice thickness exerted extremely significant effects on L value and color difference (ΔE) (p < 0.01), with higher infrared radiation temperature and thin slice thickness leading to a decrease in the L value and an increase in ΔE. Hardness and vitamin C content were significantly affected by infrared radiation temperature, slice thickness and radiation distance, of which the slice thickness was the most distinct factor affecting the hardness value. Higher infrared radiation temperature and larger slice thickness and radiation distance resulted in higher vitamin C degradation. For the given constraints (maximized vitamin C content and L value, minimized ΔE and hardness value), the optimum drying parameters were infrared radiation temperature 58.2 °C, slice thickness 6 mm and radiation distance 90 mm. Under the optimum drying combination conditions, the experimental values were 65.58 (L value), 8.57 (∆E), 10.49 N (hardness) and 106.58 mg/100 g (vitamin C content), respectively. This study is beneficial to the development of the cantaloupe food processing industry and provides more insights for the application of SMIR drying technology to improve the drying rate and product quality of cantaloupe.

Self-Supervised Learning-Based High-Resolution Ultrasound Imaging for Prostate Brachytherapy

Ultrasound (US) imaging has been widely used in image-guided needle/seed placement in prostate brachytherapy. For 3D US images with large slice thickness, high frequency information in the slice direction is missing and cannot be resolved through interpolation. As an ill-posed problem, most high-resolution generation methods rely on the presence of external/training atlases to learn the transform from low resolution to high resolution images. The anatomical variations among individuals bring unavoidable uncertainties in this atlas-based learning mechanism. In this study, we propose a self-supervised learning method, which does not use any external high-resolution atlas images, yet can resolve high resolution images from the 3D images with a large slice thickness.In 3D US imaging, in-plane spatial resolution is generally much higher than through-plane resolution. Considering the natural isotropy of US imaging on biological tissue, for a certain patient, the mapping from low-resolution to high-resolution images can be established by learning the mapping from down-sampled in-plane images (low-resolution) to original in-plane US images (high-resolution), which enables the generation of high-resolution through-plane images. Two independent cycle consistent generative adversarial networks (CycleGANs) are trained with paired original in-plane US images and two sets of in-plane down-sampled US images. Finally, high-resolution 3D US images are reconstructed by combining the generated 2D images obtained via feeding through-plane images into the two CycleGAN models. A validation study was conducted to quantitatively assess the proposed method using 3D high in-/through-plane resolution breast US images of 50 breast cancer patients. The feasibility of the proposed method for obtaining high-resolution US images for prostate brachytherapy was tested using 3D US images with 2-mm slice thickness from 45 prostate cancer patients.In the breast US images validation test using a three-times spatial resolution enhancement, the proposed method achieved the mean absolute error value of 0.90 (range 0.57-1.35), the peak signal-to-noise ratio value of 37.88 (range 35.84-40.48) dB, and the visual information fidelity value of 0.69 (range 0.68-0.72), which significantly outperforms bicubic interpolation. For the prostate cases, the proposed method outperformed the bicubic interpolation for the spatial resolution enhancement factors of 5 and 10.A novel deep learning-based algorithm for reconstructing high-resolution 3D US images from sparely acquired 2D images was developed and validated. Significant improvement on through-plane resolution has been achieved by using the acquired 2D images without any external atlas images. Its self-supervision capability could accelerate high-resolution US imaging for prostate brachytherapy.

PERFORMANCE ANALYSIS AND TECHNOLOGY OPTIMIZATION OF INFRARED DRYING OF SWEET POTATO SLICE

Sweet potato (Ipomoea batatas L.) is an important tuber crop for the daily consumption. Efficient processing must be taken to reduce wastage, and to improve quality and extend shelf period of sweet potato products. Infrared (IR) drying has advantages of high drying rate, good uniformity, and high production efficiency. A laboratory infrared (IR) dryer was developed to study the drying performance of sweet potato slice and its technology optimization in this paper. Single-factor, orthogonal, and temperature-varying experiments of IR drying of sweet potato slice were conducted sequentially. Temperature, slice thickness and steaming time were defined as control factors, and effective moisture diffusivity (EMD), total color change (TCC), specific energy consumption (SEC) and drying time were defined as evaluation indexes. Same weights were applied to the synthetic evaluation index (SEI). Experiment results and statistical analysis showed that: temperature-varying IR drying technology of temperature-decrease mode, under drying conditions of 70ºC (75min) - 65ºC (to end), showed the best drying performance; the optimal combinations for temperature-constant were slice thickness 3 mm, temperature 70ºC, and steaming time 6 min; Midilli et al. model gave the best approximation to experimental data of moisture ratio, with coefficient of determination 0.99933, reduced Chi-square 0.00007, and root mean square error (RMSE) 0.00838; high temperature (75ºC) and large slice thickness (9 mm) were not suitable for IR drying of sweet potato slice. The results of this study can provide references for research on IR drying technology and design of IR dryer for sweet potato slice.

How CT reconstruction parameters effect measurement error of pulmonary nodules volume

One of the the widely used way to follow up oncological disease is estimation of lesion size differences. Volumetry is one of the most accurate approaches of lesion size estimation. However, being highly sensitive, volumetric errors can reach 60%, which significantly limits the applicability of the method.
 Purpose was to estimate the effect of reconstruction parameters on volumetry error.
 Materials and methods. 32 patients with pulmonary metastases underwent a CT scanning with 326 foci detected. 326 pulmonary were segmented. Volumetry error was estimated for every lesion with each combination of slice thickness and reconstruction kernel. The effect was measured with linear regression analysis
 Results. Systematic and stochastic errors are impacted by slice thickness, reconstruction kernel, lesion position and its diameter. FC07 kernel and larger slice thickness is associated with high systematic error. Both systematic and stochastic errors decrease with lesion enlargment. intrapulmonary lesions have the lowest error regardless the reconstruction parameters.
 Lineal regression model was created to prognose error rate. Model standart error was 6.7%. There was corelation between model remnants deviation and slice thickness, reconstruction kernel, lesion position and its diameter.
 Conclusion. The systematic error depends on the focal diameter, slice thickness and reconstruction kernel. It can be estimated using the proposed model with a 6% error. Stochastic error mainly depends on lesion size.

Intra-Scanner and Inter-Scanner Reproducibility of Automatic White Matter Hyperintensities Quantification.

Objectives: To evaluate white matter hyperintensities (WMH) quantification reproducibility from multiple aspects of view and examine the effects of scan–rescan procedure, types of scanner, imaging protocols, scanner software upgrade, and automatic segmentation tools on WMH quantification results using magnetic resonance imaging (MRI).Methods: Six post-stroke subjects (4 males; mean age = 62.8, range = 58–72 years) were scanned and rescanned with both 3D T1-weighted, 2D and 3D T2-weighted fluid-attenuated inversion recovery (T2-FLAIR) MRI across four different MRI scanners within 12 h. Two automated WMH segmentation and quantification tools were used to measure WMH volume based on each MR scan. Robustness was assessed using the coefficient of variation (CV), Dice similarity coefficient (DSC), and intra-class correlation (ICC).Results: Experimental results show that the best reproducibility was achieved by using 3D T2-FLAIR MRI under intra-scanner setting with CV ranging from 2.69 to 2.97%, while the largest variability resulted from comparing WMH volumes measured based on 2D T2-FLAIR MRI with those of 3D T2-FLAIR MRI, with CV values in the range of 15.62%–29.33%. The WMH quantification variability based on 2D MRIs is larger than 3D MRIs due to their large slice thickness. The DSC of WMH segmentation labels between intra-scanner MRIs ranges from 0.63 to 0.77, while that for inter-scanner MRIs is in the range of 0.63–0.65. In addition to image acquisition, the choice of automatic WMH segmentation tool also has a large impact on WMH quantification.Conclusion: WMH reproducibility is one of the primary issues to be considered in multicenter and longitudinal studies. The study provides solid guidance in assisting multicenter and longitudinal study design to achieve meaningful results with enough power.

Automatic 3D Bi-Ventricular Segmentation of Cardiac Images by a Shape-Refined Multi- Task Deep Learning Approach.

Deep learning approaches have achieved state-of-the-art performance in cardiac magnetic resonance (CMR) image segmentation. However, most approaches have focused on learning image intensity features for segmentation, whereas the incorporation of anatomical shape priors has received less attention. In this paper, we combine a multi-task deep learning approach with atlas propagation to develop a shape-refined bi-ventricular segmentation pipeline for short-axis CMR volumetric images. The pipeline first employs a fully convolutional network (FCN) that learns segmentation and landmark localisation tasks simultaneously. The architecture of the proposed FCN uses a 2.5D representation, thus combining the computational advantage of 2D FCNs networks and the capability of addressing 3D spatial consistency without compromising segmentation accuracy. Moreover, a refinement step is designed to explicitly impose shape prior knowledge and improve segmentation quality. This step is effective for overcoming image artefacts (e.g. due to different breath-hold positions and large slice thickness), which preclude the creation of anatomically meaningful 3D cardiac shapes. The pipeline is fully automated, due to network’s ability to infer landmarks, which are then used downstream in the pipeline to initialise atlas propagation. We validate the pipeline on 1831 healthy subjects and 649 subjects with pulmonary hypertension. Extensive numerical experiments on the two datasets demonstrate that our proposed method is robust and capable of producing accurate, high-resolution and anatomically smooth bi-ventricular 3D models, despite the presence of artefacts in input CMR volumes.

Automated multi-atlas segmentation of cardiac 4D flow MRI

Four-dimensional (4D) flow magnetic resonance imaging (4D Flow MRI) enables acquisition of time-resolved three-directional velocity data in the entire heart and all major thoracic vessels. The segmentation of these tissues is typically performed using semi-automatic methods. Some of which primarily rely on the velocity data and result in a segmentation of the vessels only during the systolic phases. Other methods, mostly applied on the heart, rely on separately acquired balanced Steady State Free Precession (b-SSFP) MR images, after which the segmentations are superimposed on the 4D Flow MRI. While b-SSFP images typically cover the whole cardiac cycle and have good contrast, they suffer from a number of problems, such as large slice thickness, limited coverage of the cardiac anatomy, and being prone to displacement errors caused by respiratory motion. To address these limitations we propose a multi-atlas segmentation method, which relies only on 4D Flow MRI data, to automatically generate four-dimensional segmentations that include the entire thoracic cardiovascular system present in these datasets. The approach was evaluated on 4D Flow MR datasets from a cohort of 27 healthy volunteers and 83 patients with mildly impaired systolic left-ventricular function. Comparison of manual and automatic segmentations of the cardiac chambers at end-systolic and end-diastolic timeframes showed agreements comparable to those previously reported for automatic segmentation methods of b-SSFP MR images. Furthermore, automatic segmentation of the entire thoracic cardiovascular system improves visualization of 4D Flow MRI and facilitates computation of hemodynamic parameters.

Clinical Utility of Electronic Alberta Stroke Program Early Computed Tomography Score Software in the ENCHANTED Trial Database.

Clinical utility of electronic Alberta Stroke Program Early CT Score (e-ASPECTS), an automated system for quantifying signs of infarction, was evaluated in a large database of thrombolyzed patients with acute ischemic stroke. All baseline noncontrast computed tomographic scans of patients with anterior circulation acute ischemic stroke who participated in the alteplase dose arm of the randomized controlled trial ENCHANTED (Enhanced Control of Hypertension and Thrombolysis Stroke Study) were reviewed; poor quality and large (>6 mm) slice thickness were excluded. Included scans had e-ASPECTS scores correlated with baseline neurological severity (National Institutes of Health Stroke Scale scores) and 90-day disability outcomes (modified Rankin Scale scores). Multivariable logistic regression models were used to determine the predictive ability of e-ASPECTS for disability outcomes and symptomatic intracranial hemorrhage. Of 2426 available computed tomographic images, 1480 (61%) were included in analyses of e-ASPECTS scores (median 9 [interquartile range, 8-10], 77% with good [range, 8-10] scores). Lower e-ASPECTS scores (per 1-point decrease) were significantly associated with increasing baseline National Institutes of Health Stroke Scale scores (r, -0.31; P<0.0001) and 90-day poor outcome (modified Rankin Scale scores, 2-6; r, -0.27; P<0.001). Adjusted odds ratios and 95% confidence intervals for 90-day outcomes were death or disability (modified Rankin Scale scores, 2-6; 0.91 [0.85-0.97]), death and major disability (modified Rankin Scale scores, 3-6; 0.89 [0.83-0.95]), and death (0.86 [0.79-0.95]); and for symptomatic intracranial hemorrhage, according to the Implementation of Thrombolysis in Stroke-Monitoring Study criteria was 0.87 (0.72-1.05). e-ASPECT scores from thin computed tomographic slices (≤6 mm) were highly correlated with baseline neurological severity and independently predict functional recovery and adverse outcomes in acute ischemic stroke. URL: https://www.clinicaltrials.gov. Unique identifier: NCT01422616.

A Deep Learning Based Anti-aliasing Self Super-resolution Algorithm for MRI.

High resolution magnetic resonance (MR) images are desired in many clinical applications, yet acquiring such data with an adequate signal-to-noise ratio requires a long time, making them costly and susceptible to motion artifacts. A common way to partly achieve this goal is to acquire MR images with good in-plane resolution and poor through-plane resolution (i.e., large slice thickness). For such 2D imaging protocols, aliasing is also introduced in the through-plane direction, and these high-frequency artifacts cannot be removed by conventional interpolation. Super-resolution (SR) algorithms which can reduce aliasing artifacts and improve spatial resolution have previously been reported. State-of-the-art SR methods are mostly learning-based and require external training data consisting of paired low resolution (LR) and high resolution (HR) MR images. However, due to scanner limitations, such training data are often unavailable. This paper presents an anti-aliasing (AA) and self super-resolution (SSR) algorithm that needs no external training data. It takes advantage of the fact that the in-plane slices of those MR images contain high frequency information. Our algorithm consists of three steps: 1) We build a self AA (SAA) deep network followed by 2) an SSR deep network, both of which can be applied along different orientations within the original images, and 3) recombine the multiple orientations output from Steps 1 and 2 using Fourier burst accumulation. We perform our SAA+SSR algorithm on a diverse collection of MR data without modification or preprocessing other than N4 inhomogeneity correction, and demonstrate significant improvement compared to competing SSR methods.

3D ultrasound computer tomography: Hardware setup, reconstruction methods and first clinical results

A promising candidate for improved imaging of breast cancer is ultrasound computer tomography (USCT). Current experimental USCT systems are still focused in elevation dimension resulting in a large slice thickness, limited depth of field, loss of out-of-plane reflections, and a large number of movement steps to acquire a stack of images. 3D USCT emitting and receiving spherical wave fronts overcomes these limitations. We built an optimized 3D USCT, realizing for the first time the full benefits of a 3D system. The point spread function could be shown to be nearly isotropic in 3D, to have very low spatial variability and fit the predicted values. The contrast of the phantom images is very satisfactory in spite of imaging with a sparse aperture. The resolution and imaged details of the reflectivity reconstruction are comparable to a 3 T MRI volume. Important for the obtained resolution are the simultaneously obtained results of the transmission tomography. The KIT 3D USCT was then tested in a pilot study on ten patients. The primary goals of the pilot study were to test the USCT device, the data acquisition protocols, the image reconstruction methods and the image fusion techniques in a clinical environment. The study was conducted successfully; the data acquisition could be carried out for all patients with an average imaging time of six minutes per breast. The reconstructions provide promising images. Overlaid volumes of the modalities show qualitative and quantitative information at a glance. This paper gives a summary of the involved techniques, methods, and first results.

Hyoid Bone Development: An Assessment Of Optimal CT Scanner Parameters and Three-Dimensional Volume Rendering Techniques.

The hyoid bone anchors and supports the vocal tract. Its complex shape is best studied in three dimensions, but it is difficult to capture on computed tomography (CT) images and three-dimensional volume renderings. The goal of this study was to determine the optimal CT scanning and rendering parameters to accurately measure the growth and developmental anatomy of the hyoid and to determine whether it is feasible and necessary to use these parameters in the measurement of hyoids from in vivo CT scans. Direct linear and volumetric measurements of skeletonized hyoid bone specimens were compared with corresponding CT images to determine the most accurate scanning parameters and three-dimensional rendering techniques. A pilot study was undertaken using in vivo scans from a retrospective CT database to determine feasibility of quantifying hyoid growth. Scanning parameters and rendering technique affected accuracy of measurements. Most linear CT measurements were within 10% of direct measurements; however, volume was overestimated when CT scans were acquired with a slice thickness greater than 1.25 mm. Slice-by-slice thresholding of hyoid images decreased volume overestimation. The pilot study revealed that the linear measurements tested correlate with age. A fine-tuned rendering approach applied to small slice thickness CT scans produces the most accurate measurements of hyoid bones. However, linear measurements can be accurately assessed from in vivo CT scans at a larger slice thickness. Such findings imply that investigation into the growth and development of the hyoid bone, and the vocal tract as a whole, can now be performed using these techniques.

Statistical analysis of lung nodule volume measurements with CT in a large-scale phantom study.

To determine inter-related factors that contribute substantially to measurement error of pulmonary nodule measurements with CT by assessing a large-scale dataset of phantom scans and to quantitatively validate the repeatability and reproducibility of a subset containing nodules and CT acquisitions consistent with the Quantitative Imaging Biomarker Alliance (QIBA) metrology recommendations. The dataset has about 40 000 volume measurements of 48 nodules (5-20 mm, four shapes, three radiodensities) estimated by a matched-filter estimator from CT images involving 72 imaging protocols. Technical assessment was performed under a framework suggested by QIBA, which aimed to minimize the inconsistency of terminologies and techniques used in the literature. Accuracy and precision of lung nodule volume measurements were examined by analyzing the linearity, bias, variance, root mean square error (RMSE), repeatability, reproducibility, and significant and substantial factors that contribute to the measurement error. Statistical methodologies including linear regression, analysis of variance, and restricted maximum likelihood were applied to estimate the aforementioned metrics. The analysis was performed on both the whole dataset and a subset meeting the criteria proposed in the QIBA Profile document. Strong linearity was observed for all data. Size, slice thickness × collimation, and randomness in attachment to vessels or chest wall were the main sources of measurement error. Grouping the data by nodule size and slice thickness × collimation, the standard deviation (3.9%-28%), and RMSE (4.4%-68%) tended to increase with smaller nodule size and larger slice thickness. For 5, 8, 10, and 20 mm nodules with reconstruction slice thickness ≤0.8, 3, 3, and 5 mm, respectively, the measurements were almost unbiased (-3.0% to 3.0%). Repeatability coefficients (RCs) were from 6.2% to 40%. Pitch of 0.9, detail kernel, and smaller slice thicknesses yielded better (smaller) RCs than those from pitch of 1.2, medium kernel, and larger slice thicknesses. Exposure showed no impact on RC. The overall reproducibility coefficient (RDC) was 45%, and reduced to about 20%-30% when the slice thickness and collimation were fixed. For nodules and CT imaging complying with the QIBA Profile (QIBA Profile subset), the measurements were highly repeatable and reproducible in spite of variations in nodule characteristics and imaging protocols. The overall measurement error was small and mostly due to the randomness in attachment. The bias, standard deviation, and RMSE grouped by nodule size and slice thickness × collimation in the QIBA Profile subset were within ±3%, 4%, and 5%, respectively. RCs are within 11% and the overall RDC is equal to 11%. The authors have performed a comprehensive technical assessment of lung nodule volumetry with a matched-filter estimator from CT scans of synthetic nodules and identified the main sources of measurement error among various nodule characteristics and imaging parameters. The results confirm that the QIBA Profile set is highly repeatable and reproducible. These phantom study results can serve as a bound on the clinical performance achievable with volumetric CT measurements of pulmonary nodules.

Image Super-Resolution by Supervised Adaption of Patchwise Self-similarity from High-Resolution Image.

Image super-resolution is of great interest in medical imaging field. However, different from natural images studied in computer vision field, the low-resolution (LR) medical imaging data is often a stack of high-resolution (HR) 2D slices with large slice thickness. Consequently, the goal of super-resolution for medical imaging data is to reconstruct the missing slice(s) between any two consecutive slices. Since some modalities (e.g., T1-weighted MR image) are often acquired with high-resolution (HR) image, it is intuitive to harness the prior self-similarity information in the HR image for guiding the super-resolution of LR image (e.g., T2-weighted MR image). The conventional way is to find the profile of patchwise self-similarity in the HR image and then use it to reconstruct the missing information at the same location of LR image. However, the local morphological patterns could vary significantly across the LR and HR images, due to the use of different imaging protocols. Therefore, such direct (un-supervised) adaption of self-similarity profile from HR image is often not effective in revealing the actual information in the LR image. To this end, we propose to employ the existing image information in the LR image to supervise the estimation of self-similarity profile by requiring it not only being optimal in representing patches in the HR image, but also producing less reconstruction errors for the existing image information in the LR image. Moreover, to make the anatomical structures spatially consistent in the reconstructed image, we simultaneously estimate the self-similarity profiles for a stack of patches across consecutive slices by solving a group sparse patch representation problem. We have evaluated our proposed super-resolution method on both simulated brain MR images and real patient images with multiple sclerosis lesion, achieving promising results with more anatomical details and sharpness.

Reconstructing 3D x-ray CT images of polymer gel dosimeters using the zero-scan method

In this study x-ray CT has been used to produce a 3D image of an irradiated PAGAT gel sample, with noise-reduction achieved using the 'zero-scan' method. The gel was repeatedly CT scanned and a linear fit to the varying Hounsfield unit of each pixel in the 3D volume was evaluated across the repeated scans, allowing a zero-scan extrapolation of the image to be obtained. To minimise heating of the CT scanner's x-ray tube, this study used a large slice thickness (1 cm), to provide image slices across the irradiated region of the gel, and a relatively small number of CT scans (63), to extrapolate the zero-scan image. The resulting set of transverse images shows reduced noise compared to images from the initial CT scan of the gel, without being degraded by the additional radiation dose delivered to the gel during the repeated scanning. The full, 3D image of the gel has a low spatial resolution in the longitudinal direction, due to the selected scan parameters. Nonetheless, important features of the dose distribution are apparent in the 3D x-ray CT scan of the gel. The results of this study demonstrate that the zero-scan extrapolation method can be applied to the reconstruction of multiple x-ray CT slices, to provide useful 2D and 3D images of irradiated dosimetry gels.

Effects of slice thickness and head rotation when measuring glioma sizes on MRI: in support of volume segmentation versus two largest diameters methods

This paper presents a study of the effects of scanning parameters variability when assessing glioma sizes on MRI. A database of lesions of various shapes and sizes, segmented on 3D-SPGR MRI images, was acquired on 65 patients with low-grade glioma. Simulations of large slice thickness and patient's head rotation were performed, allowing us to study their influence on two size indices: the bi-dimensional diameter product index (computed with the two largest diameters method) and the equivalent diameter index (computed with the volume segmentation method). Results show that thick slices and axial plane rotation can induce average (maximal) uncertainties on the bi-dimensional diameter product index between 32 and 6 % (150 %) for small and large tumors (size range 0.5-286 ml). The uncertainty on the equivalent diameter index, for the same categories of tumors, drops below 8 and 0.1 % (23 %). This study shows that the volume segmentation method is subject to less variability inherent to scanning conditions compared to the two largest diameters method. It also emphasizes the need for strict clinical guidelines on the replication of scanning conditions when performing MRI follow-ups on patients harboring small tumors. These implications await confirmation on a series of real patients being re-scanned with FLAIR MRI.