CT Image Slices Research Articles

Differentiation of renal angiomyolipoma without visible fat from renal cell carcinoma by machine learning based on whole-tumor computed tomography texture features.

Background Morphological findings showed poor accuracy in differentiating angiomyolipoma without visible fat (AMLwvf) from renal cell carcinoma (RCC). Purpose To determine the performance of a machine learning classifier in differentiating AMLwvf from different subtypes of RCC based on whole-tumor slices of CT images. Material and Methods In this retrospective study, 171 pathologically proven renal masses were collected from a single institution. Texture features were extracted from whole-tumor images in three phases including the pre-contrast (PCP), corticomedullary (CMP), and nephrographic (NP) phases. A support vector machine with the recursive feature elimination method based on fivefold cross-validation (SVM-RFECV) with the synthetic minority oversampling technique (SMOTE) was utilized to establish classifiers for differentiating AMLwvf from all subtypes of RCC (all-RCC), clear cell RCC (ccRCC), and non-ccRCC. The performances of the classifiers based on three-phase and single-phase images were compared with each other and morphological interpretations. Results A machine learning classifier achieved the best performance in differentiating AMLwvf from all-RCC, ccRCC, and non-ccRCC. The performance of the best machine learning classifier for differentiating AMLwvf from all-RCC (area under the curve [AUC] = 0.96) and ccRCC (AUC = 0.97) was higher than that for differentiating AMLwvf from non-ccRCC (AUC = 0.89); morphological interpretations achieved lower performance for differentiating AMLwvf from all-RCC (AUC = 0.67), ccRCC (AUC = 0.68), and non-ccRCC (AUC = 0.64). Conclusion Machine learning can be a useful non-invasive technique for differentiating AMLwvf from all-RCC, ccRCC, and non-ccRCC, and it can be more accurate than morphological interpretation by radiologists.

An Approach for Pulmonary Vascular Extraction from Chest CT Images.

Pulmonary vascular extraction from chest CT images plays an important role in the diagnosis of lung disease. To improve the accuracy rate of pulmonary vascular segmentation, a new pulmonary vascular extraction approach is proposed in this study. First, the lung tissue is extracted from chest CT images by region-growing and maximum between-class variance methods. Then the holes of the extracted region are filled by morphological operations to obtain complete lung region. Second, the points of the pulmonary vascular of the middle slice of the chest CT images are extracted as the original seed points. Finally, the seed points are spread throughout the lung region based on the fast marching method to extract the pulmonary vascular in the gradient image. Results of pulmonary vascular extraction from chest CT image datasets provided by the introduced approach are presented and discussed. Based on the ground truth pixels and the resulting quality measures, it can be concluded that the average accuracy of this approach is about 90%. Extensive experiments demonstrate that the proposed method has achieved the best performance in pulmonary vascular extraction compared with other two widely used methods.

Tensor-based sparse representations of multi-phase medical images for classification of focal liver lesions

Medical images play an important role in clinics. In most clinic sites, the diagnosis of diseases and the comprehending of disease progression need firstly accurate interpretation of the available medical images, which would be time-consuming in manual interpretation of the accumulated large amount of medical images. Thus automatical analysis and understanding of the available medical images become an active research topic, and therein, feature extraction of medical images plays an important role for achieving diagnosis performance. Natural images hold two-dimensional structures and linear statistical methods, such as k-means, GMM, and sparse coding, are widely applied for extracting compact and inherent representations. In contrast, medical images themselves have three-dimensional structures and possibly consist of multi-phase extension. Directly applying linear methods on the available medical data would lead to high dimensional vectors by reshaping the multi-dimensional data and would destroy the correlated relation among different dimensions of the raw medical data domain. Therefore, this study proposes a multilinear extension of the linear sparse coding for extracting compact and effective intermediate representations of the multi-dimensional local structures in multi-phase CT images, and aggregating the intermediate representations in the Bag-of-Visual-Words (BoVW) manner for classification of focal liver lesions (FLLs). In the proposed approach, three-layer volumes from the corresponding slices of multi-phase CT images are formed and spatiotemporal local structures from the volumes are extracted as 3rd-order tensors. Regarding the high dimensional local structures as tensors, we propose a K-CP (CANDECOMP/PARAFAC) algorithm to learn a tensor dictionary in an iterative way and extract the sparse representation with a multilinear OMP method. The aggregation of the sparse representation is implemented in the BoVW manner, which has been proved to be an effective method for extracting features from natural images and medical images. The proposed strategy is evaluated in classification of focal liver lesions and achieved better results than conventional BoVW with linear statistical methods.

Recommendations for iliosacral screw placement in dysmorphic sacrum based on modified in-out-in corridors.

(1) Can iliosacral osseous corridor diameters in sacral dysmorphism be enlarged by in-out-in screw placement at the posterior iliosacral recessus? (2) Are lumbosacral transitional vertebra (LSTV) the anatomical cause for sacral dysmorphism? (3) Are there sex-specific differences in sacral dysmorphism? 594 multislice CT scans were screened for sacral dysmorphism and 55 data-sets selected. Each pelvis was segmented manually and cylindrical iliosacral corridors (on the level of S1 and S2 vertebra) were semi-automatically determined. Corridor trajectories, -diameters and -lengths were measured. LSTV (Castellvi-type IIIb and IV) were found in 3 of 55 pelves and these lumbosacral variations are therefore not the anatomical basis for sacral dysmorphism. The prevalence of transsacral osseous corridors with diameters of <7.5 mm in axial CT images correlates with qualitative and quantitative criteria of sacral dysmorphism. Enlarging the osseous corridor diameters by penetration of the posterior iliosacral recessus increase the safe corridor diameters (females versus males) by 26% versus 15% at the level of S1- and 50% versus 48% at the level of S2-vertebra. Sex-specific differences for both corridors (osseous and in-out-in) were only found for the osseous corridor diameters at the level of S1 vertebra, being smaller in females (females versus males: 13.3 ± 3.6 mm versus 15.5 ± 3.8 mm, p = 0.04). Dysmorphic sacra can be reliably detected on standard axial CT slice images. Modified in-out-in corridors on the level of S1-vertebra allow screw placement in all patients, but is still demanding compared to non-dysmorphic sacra, due to the oblique corridor axis. Recommendations for intraoperative orientation for oblique screw placement are defined. © 2018 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res.

Defect Detection and Three Dimensional Reconstruction of Castings

There are defects inevitably in the production of castings. These defects will short the life of castings. In this paper, defects of the castings are detected by industrial CT non-destructive testing method and a series of CT slice images are obtained. Then edge extraction is realized through the Canny operator and the defects in the castings are found. Finally, surfaces and its internal defects are reconstructed for the casting based on secondary development of Unigraphics software and Visual Studio 2013. Experiments show that internal defects of the castings can be extracted by the Canny operator effectively. Internal position and shape of defects can be observed directly by three dimensional reconstruction.

Automatic treatment planning based on three-dimensional dose distribution predicted from deep learning technique.

To develop an automated treatment planning strategy for external beam intensity-modulated radiation therapy (IMRT), including a deep learning-based three-dimensional (3D) dose prediction and a dose distribution-based plan generation algorithm. A residual neural network-based deep learning model is trained to predict a dose distribution based on patient-specific geometry and prescription dose. A total of 270 head-and-neck cancer cases were enrolled in this study, including 195 cases in the training dataset, 25 cases in the validation dataset, and 50 cases in the testing dataset. All patients were treated with IMRT with a variety of different prescription patterns. The model input consists of CT images and contours delineating the organs at risk (OARs) and planning target volumes (PTVs). The algorithm output is trained to predict the dose distribution on the CT image slices. The obtained prediction model is used to predict dose distributions for new patients. Then, an optimization objective function based on these predicted dose distributions is created for automatic plan generation. Our results demonstrate that the deep learning method can predict clinically acceptable dose distributions. There is no statistically significant difference between prediction and real clinical plan for all clinically relevant dose-volume histogram (DVH) indices, except brainstem, right and left lens. However, the predicted plans were still clinically acceptable. The results of plan generation show no statistically significant differences between the automatic generated plan and the predicted plan except PTV70.4 , but the difference is only 0.5% which is still clinically acceptable. This study developed a new automated radiotherapy treatment planning system based on 3D dose prediction and 3D dose distribution-based optimization. It is a promising approach for realizing automated treatment planning in the future.

Void Content Measurement in Fiber Reinforced Plastic Composites by X-Ray Computed Tomography

The void formation in FRP composites is unavoidable and the void content measurement is very important to study its deleterious effects on the mechanical properties of the material. Generally destructive methods are used to calculate the void volume fraction. But the recent advances in X-ray computed tomography can be used to detect and quantify the void content in the composites in a non-destructive manner. In this study average area method is proposed and validated for the void volume measurement from the X-ray CT image slices through digital image processing. The effects of void size, shape and position in the accuracy of the measurement is studied and presented here. The void volume fraction of a CFRP laminate manufactured with compression molding technique is calculated by this method and found to be around 1%.

Radiomics Analysis Using Contrast-Enhanced CT for Preoperative Prediction of Occult Peritoneal Metastasis in Advanced Gastric Cancer

Background: To evaluate the predictive value of CT radiomics features derived from primary lesions in discriminating occult peritoneal metastasis (PM) in advanced gastric cancer (AGC). Methods: Preoperative CT images of 233 patients with AGC were retrospectively analyzed. The region of interest was manually drawn along the margin of the lesion on the largest slice of venous CT images, and a total of 539 quantified features were extracted automatically. The intraclass correlation coefficient (ICC) and the absolute correlation coefficient (ACC) were calculated for selecting influential features. The predictive value of each influential feature was assessed using the Mann-Whitney U test and receiver operating characteristic (ROC) analysis. A multivariate logistic regression model was constructed based on the training cohort (124 without PM, 34 with PM), and the testing cohort (64 without PM, 11 with PM) validated the reliability of the model. Moreover, another model based on the preoperative clinicopathological features was also developed. A comparison between the diagnostic performances of the two models was performed using ROC analysis and the Akaike information criterion (AIC) value. Results: Our results showed that 6 radiomics features (ID_Energy, LoG(0.5)_Energy, Compactness2, Max Diameter, Orientation and Surface Area Density) differed significantly between AGCs with and without PM (all P < 0.05) and performed well in distinguishing AGCs with PM from those without PM in the primary cohort (AUC = 0.618-0.658, all P <0.05). The radiomics model including Compactness2, Max Diameter and Orientation showed a higher AUC value than each single radiomics feature in the primary cohort (0.741 vs 0.618-0.658) and similar diagnosis performance in the validation cohort. The radiomics model showed better diagnostic efficacy and model fit accuracy than the clinicopathological model (AUC, 0.724 vs 0.649; AIC, 157.2 vs 172.1). Conclusion: Preoperative radiomics analysis using venous CT images provided valuable information for predicting occult PM in AGC. Funding Statement: This work was supported by National Natural Science Foundation of China (ID: 81501441, 81601463, 81871410), Foundation of National Health and Family Planning Commission of China (W201306), Social Development Foundation of Jiangsu Province (BE2015605), the Natural Science Foundation of Jiangsu Province (ID: BK20131281, BK20150109), Jiangsu Province Health and Family Planning Commission Youth Scientific Research Project (ID: Q201508), Six Talent Peaks Project of Jiangsu Province (ID: 2015-WSN-079), and Jiangsu Provincial Medical Youth Talent (ID: QNRC2016040) Declaration of Interests: The authors declare that they have no competing interests. Ethics Approval Statement: This retrospective study was approved by the ethics committee of the Institutional Review Board of Nanjing Drum Tower Hospital, and the requirement for informed consent was waived.

Deteksi Tumor Hati dengan Graph Cut dan Taksiran Volume Tumornya

Liver is one of the most important organs in the human body. One of the dangerous diseases of the liver is tumor. In the CT scan image, the tumor has different texture, color, shape, and position, according to patient's condition. In this study, a tumor detection was carried out by tree stages: firstly some steps of preprocessing, such as filtering, edge detection, and erotion; secondly, finding the liver among organs in abdomen using segmentation and checking the liver position in the right abdomen; and thirdly performing the tumor detection in the liver using graph cut and push relabel algorithm. Usually, segmentation using graph cut needs two interactive inputs, namely sample of object area and sample of background area. In this paper, the interactive inputs on graph cut were replaced by deviation standard calculation. Testing using three sets of CT image and the ground truth produces average of the dice similarity coefficient (DSC), volumetric overlap error (VOE), and absolute volume difference (AVD) parameters of 78.15%, 25.72%, 19.30%, respectively. Furthermore, volume of liver tumor is approximated by utilizing area of tumor in each slice of CT image, then displayed in 3D view.

AngleNav: MEMS Tracker to Facilitate CT-Guided Puncture.

As a low-cost needle navigation system, AngleNav may be used to improve the accuracy, speed, and ease of CT-guided needle punctures. The AngleNav hardware includes a wireless device with a microelectromechanical (MEMS) tracker that can be attached to any standard needle. The physician defines the target, desired needle path and skin entry point on a CT slice image. The accuracy of AngleNav was first tested in a 3D-printed calibration platform in a benchtop setting. An abdominal phantom study was then performed in a CT scanner to validate the accuracy of the device's angular measurement. Finally, an in vivo swine study was performed to guide the needle towards liver targets (n=8). CT scans of the targets were used to quantify the angular errors and needle tip-to-targeting distance errors between the planned needle path and the final needle position. The MEMS tracker showed a mean angular error of 0.01° with a standard deviation (SD) of 0.62° in the benchtop setting. The abdominal phantom test showed a mean angular error of 0.87° with an SD of 1.19° and a mean tip-to-target distance error of 4.89mm with an SD of 1.57mm. The animal experiment resulted in a mean angular error of 6.6° with an SD of 1.9° and a mean tip-to-target distance error of 8.7mm with an SD of 3.1mm. These results demonstrated the feasibility of AngleNav for CT-guided interventional workflow. The angular and distance errors were reduced by 64.4 and 54.8% respectively if using AngleNav instead of freehand insertion, with a limited number of operators. AngleNav assisted the physicians to deliver accurate needle insertion during CT-guided intervention. The device could potentially reduce the learning curve for physicians to perform CT-guided needle targeting.

Expression of HMGB1 in maternal exposure to fine particulate air pollution induces lung injury in rat offspring assessed with micro-CT

ObjectivesMaternal particulate matter with less than 2.5 μm in diameter (PM2.5) is associated with an increased risk for acute lower respiratory infections and allergic airway inflammation; however, its effect on the developing lung remains unclear. The aim of this study is to determine the effect of maternal PM2.5 during pregnancy on lung development in offspring. MethodsTimed pregnant Sprague-Dawley rats were treated with PM2.5 (0.1, 0.5, 2.5, or 7.5 mg/kg) once every 3 days from day 0–18 of pregnancy and delivered at term. Lungs were obtained on postnatal day 0, the structure of the lung was analyzed by quantitative micro-computed tomography (CT) and the levels of proinflammatory cytokines were analyzed using enzyme-linked immunosorbent assay (ELISA). The expression of high mobility group box-1 (HMGB1) was also detected by immunohistochemistry, Western blotting, and quantitative RT-PCR. ResultsGround-glass opacity and high-density volumes in CT slice images of maternal PM2.5-exposure rats were observed. The concentrations of IL-1, IL-6 and TNF-α were significantly increased by 2.36-, 3.91- and 4.36-fold, respectively, in the rats of the PM-7.5 group compared with the rats in the control group. The PM2.5-treated rats showed a significant upregulated expression of HMGB1 in lungs. ConclusionsPM2.5 exposure during pregnancy results in lung inflammation in offspring mediated by increased HMGB1 expression, followed by upregulated IL-1, IL-6 and TNF-α secretions, which may contribute to the development of inflammatory lung diseases in later life.

Ply-orientation measurements in composites using structure-tensor analysis of volumetric ultrasonic data

Ply wrinkling in carbon fibre reinforced polymer (CFRP) laminates is often geometrically complex and difficult to quantify using non-destructive techniques. In this paper, an ultrasonic technique for mapping ply wrinkling is presented. The instantaneous-phase three-dimensional dataset obtained from a pulse-echo ultrasonic inspection is processed using the structure-tensor image processing technique to quantify the orientations of the internal plies of a CFRP laminate. It is shown that consideration must be given to the wrapped nature of the phase dataset during processing to obtain accurate orientation maps. Three dimensional ply orientation and ply-location maps obtained from a test coupon are compared with true ply angles and locations by overlaying the ultrasonically-derived results on X-ray CT image slices, showing that accurate orientation maps can be obtained using the proposed technique.

A two-step convolutional neural network based computer-aided detection scheme for automatically segmenting adipose tissue volume depicting on CT images

Accurately assessment of adipose tissue volume inside a human body plays an important role in predicting disease or cancer risk, diagnosis and prognosis. In order to overcome limitation of using only one subjectively selected CT image slice to estimate size of fat areas, this study aims to develop and test a computer-aided detection (CAD) scheme based on deep learning technique to automatically segment subcutaneous fat areas (SFA) and visceral fat areas (VFA) depicting on volumetric CT images. A retrospectively collected CT image dataset was divided into two independent training and testing groups. The proposed CAD framework consisted of two steps with two convolution neural networks (CNNs) namely, Selection-CNN and Segmentation-CNN. The first CNN was trained using 2,240 CT slices to select abdominal CT slices depicting SFA and VFA. The second CNN was trained with 84,000pixel patches and applied to the selected CT slices to identify fat-related pixels and assign them into SFA and VFA classes. Comparing to the manual CT slice selection and fat pixel segmentation results, the accuracy of CT slice selection using the Selection-CNN yielded 95.8%, while the accuracy of fat pixel segmentation using the Segmentation-CNN was 96.8%. This study demonstrated the feasibility of applying a new deep learning based CAD scheme to automatically recognize abdominal section of human body from CT scans and segment SFA and VFA from volumetric CT data with high accuracy or agreement with the manual segmentation results.

Stereographic measurement of orbital volume, a digital reproducible evaluation method

Background/AimsUp to date, no standardised reproducible orbital volume measurement method is available. Therefore, this study aimed to investigate the accuracy of a new measurement method, which delineates the boundaries of...

Aorta-Lesion-Attenuation-Difference (ALAD) on contrast-enhanced CT: a potential imaging biomarker for differentiating malignant from benign oncocytic neoplasms.

To evaluate whether the Aorta-Lesion-Attenuation-Difference on contrast-enhanced CT can aid in the differentiation of malignant and benign oncocytic renal neoplasms. Two independent cohorts-an initial (biopsy) dataset and a validation (surgical) dataset-with oncocytomas and chromophobe renal cell carcinomas (chRCC) were included in this IRB-approved retrospective study. A region of interest was placed on the renal mass and abdominal aorta on the same CT image slice to calculate an Aorta-Lesion-Attenuation-Difference (ALAD). ROC curves were plotted for different enhancement phases, and diagnostic performance of ALAD for differentiating chRCC from oncocytomas was calculated. Seventy-nine renal masses (56 oncocytomas, 23 chRCC) were analyzed in the initial (biopsy) dataset. Thirty-six renal masses (16 oncocytomas, 20 chRCC) were reviewed in the validation (surgical) cohort. ALAD showed a statistically significant difference between oncocytomas and chromophobes during the nephrographic phase (p<0.001), early excretory phase (p<0.001), and excretory phase (p=0.029). The area under the ROC curve for the nephrographic phase was 1.00 (95% CI: 1.00-1.00) for the biopsy dataset and showed the narrowest confidence interval. At a threshold value of 25.5 HU, sensitivity was 100 (82.2%-100%) and specificity was 81.5 (61.9%-93.7%). When tested on the validation dataset on measurements made by an independent reader, the AUROC was 0.93 (95% CI: 0.84-1.00) with a sensitivity of 100 (80.0%-100%) and a specificity of 87.5 (60.4%-97.8%). Nephrographic phase ALAD has potential to differentiate benign and malignant oncocytic renal neoplasms on contrast-enhanced CT if histologic evaluation on biopsy is indeterminate.

Abstract P1-10-11: Prone vs. supine positioning for optimizing radiation heart exposure during left breast radiotherapy: A simple clinical tool for prediction*

Abstract The benefit of reduced radiation heart exposure in the prone vs. supine position, individually differs according to the anatomical features of the patient (Varga Z, Int J Radiat Oncol Biol Phys. 2009;75:94-110). Preferable positioning may be predicted by a statistical model. This validated calculator using the patient's body mass index (BMI), the median distance of the anterior surface of the left coronary artery (LAD) from the chest wall (Dmed) and the heart area (Aheart) included in the radiation field in a single CT scan at the middle of the heart (median plane, Pmed) provides quantitative estimates of the dose differences to the LAD or heart in the two positions (Varga Z, Acta Oncol. 2014;53:58-64). In this prospective cohort study, the goal was to develop a reliable method to collect data for the calculator without acquiring a full series of CT scans in both positions. The study was approved by the Institutional Review Board of the University of Szeged, and all the enrolled patients gave their written informed consent to participation. Eligible patients needed postoperative left breast radiotherapy. In 100 patients, a single CT slice image representing the middle of the heart (reference plane, Pref) was acquired by using the AP scout view in the supine position for the selection of the transversal plane appropriate for the measurements of Dmed and Aheart. Thereafter, CT series were acquired in both positions, and analyses were performed on 1. the conformance of the Pref with Pmed , 2. the effect of plane miss on the choice of preferable position. 3. Finally the sensitivity and specificity of this simple clinical method was reevaluated based on the dosimetry data (mean LAD dose, V25Gy heart) obtained using the topogram for selecting the position. In 55 cases, Pref was the same as Pmed, while in 27 and 18 cases, Pref and Pmed differed by 2 or more planes, respectively. No difference was found between the values of Dmed or Aheart asmeasured in Pref vs. Pmed, independently whether Pref was identified correctly or not.The suggestion on treatment position per Pref measurements, was correct in 85 cases, was ambiguous in 8 cases and incorrect in 7 cases. Sensitivity of the calculator based on Pref measurements was 90% (both mean LAD dose and V25Gy heart), while specificity was 72% (mean LAD dose) and 68% (V25Gy heart). The simple clinical method was tested in additional 60 left breast radiotherapy cases, by taking a topogram and a CT series in the suggested position only. Mean LAD dose (mean [CI95%] prone vs. supine: 6.5 [5.7-7.3] Gy vs. 7.5 [5.6-9.5] Gy) and V25Gy heart (mean [CI95%] prone vs. supine: 0.87 [0.67-1.11] % vs. 1.13 [0.55-1.71]%) well corresponded to the institutional dose constraints based on dosimetry data achieved with individual positioning in &amp;gt;300 patients with left-sided breast cancer. We consider this simple clinical tool appropriate for assisting individual positioning aiming at maximum heart protection during left breast irradiation. *Supported by the VKSZ 12-1-2013-0012 project. Citation Format: Kahán Z, Gaál S, Cserháti A, Rárosi F, Boda K, Varga Z. Prone vs. supine positioning for optimizing radiation heart exposure during left breast radiotherapy: A simple clinical tool for prediction* [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P1-10-11.

SU‐D‐209‐06: Study On the Dose Conversion Coefficients in Pediatric Radiography with the Development of Children Voxel Phantoms

Purpose:Conversion coefficients of organ dose normalized to entrance skin dose (ESD) are widely used to evaluate the organ doses directly using ESD without time‐consuming dose measurement, this work aims to investigate the dose conversion coefficients in pediatric chest and abdomen radiography with the development of 5 years and 10 years old children voxel phantoms.Methods:After segmentation of organs and tissues from CT slice images of ATOM tissue‐equivalent phantoms, a 5‐year‐old and a 10‐year‐old children computational voxel phantoms were developed for Monte Carlo simulation. The organ doses and the entrance skin dose for pediatric chest postero‐anterior projection and abdominal antero‐posterior projection were simulated at the same time, and then the organ dose conversion coefficients were calculated.To verify the simulated results, dose measurement was carried out with ATOM tissue‐equivalent phantoms for 5 year chest radiography.Results:Simulated results and experimental results matched very well with each other, the result differences of all the organs covered in radiation field were below 16% for 5‐year‐old child in chest projection. I showed that the conversion coefficients of organs covered in the radiation field were much larger than organs out of the field for all the study cases, for example, the conversion coefficients of stomach, liver intestines, and pancreas are larger for abdomen radiography while conversion coefficients of lungs are larger for chest radiography.Conclusion:The voxel children phantoms were helpful to evaluate the radiation doses more accurately and efficiently. Radiation field was the essential factor that affects the organ dose, use reasonably small field should be encouraged for radiation protection.This work was supported by the National Natural Science Foundation of China(11475047)

3D documentation and visualization of external injury findings by integration of simple photography in CT/MRI data sets (IprojeCT)

This study evaluated the feasibility of documenting patterned injury using three dimensions and true colour photography without complex 3D surface documentation methods. This method is based on a generated 3D surface model using radiologic slice images (CT) while the colour information is derived from photographs taken with commercially available cameras. The external patterned injuries were documented in 16 cases using digital photography as well as highly precise photogrammetry-supported 3D structured light scanning. The internal findings of these deceased were recorded using CT and MRI. For registration of the internal with the external data, two different types of radiographic markers were used and compared. The 3D surface model generated from CT slice images was linked with the photographs, and thereby digital true-colour 3D models of the patterned injuries could be created (Image projection onto CT/IprojeCT). In addition, these external models were merged with the models of the somatic interior. We demonstrated that 3D documentation and visualization of external injury findings by integration of digital photography in CT/MRI data sets is suitable for the 3D documentation of individual patterned injuries to a body. Nevertheless, this documentation method is not a substitution for photogrammetry and surface scanning, especially when the entire bodily surface is to be recorded in three dimensions including all external findings, and when precise data is required for comparing highly detailed injury features with the injury-inflicting tool.

Preoperative Renal Volume: A Surrogate Measure for Radical Nephrectomy-Induced Chronic Kidney Disease

Surgically induced chronic kidney disease (CKD) has been found to have less impact on survival as well as function when compared to medical causes for CKD. The aim of this study is to evaluate whether preoperative remaining kidney volume correlates with renal function after nephrectomy, which represents an individual's renal reserve before surgically induced CKD. A retrospective review of 75 consecutive patients (29.3% females) who underwent radical nephrectomy (RN) (2000-2010) was performed. Normal side kidney parenchyma, excluding renal vessels and central sinus fat, was manually outlined in each transverse slice of CT image and multiplied by slice thickness to calculate volume. Estimated glomerular filtration rate (eGFR) was determined using the Modification of Diet in Renal Disease equation. CKD is defined as eGFR < 60 mL/min/1.73 m(2). Mean preoperative normal kidney parenchymal volume (mean age 55 [SD 13] years) is 150.7 (SD 36.4) mL. Over median follow-up of 36 months postsurgery, progression to CKD occurred in 42.6% (n = 32) of patients. On multivariable analysis, preoperative eGFR and preoperative renal volume <144 mL are independent predictors for postoperative CKD. On Kaplan-Meier analysis, median time to reach CKD postnephrectomy is 12.7 (range 0.03-43.66) months for renal volume <144 mL but not achieved if renal volume is >144 mL. Normal kidney parenchymal volume and preoperative eGFR are independent predictive factors for postoperative CKD after RN and may represent renal reserve for both surgically and medically induced CKD, respectively. Preoperative remaining kidney volume may be an adjunct representation of renal reserve postsurgery and predict later renal function decline due to perioperative loss of nephrons.

Abstract 1487: Rotating 3D projections of prostate lesions imaged by Cu-64-VPAC1 ligand PET and CT

Abstract Surgeons are obliged to examine multiple planar images of fine needle biopsies, CT, and PET images for diagnosis and evaluation of prostate cancer. We hypothesize that optimal comprehension of prostate lesions in the prostate, as they present in the surrounding tissue, will be achieved by 3D registration and presentation of all available images. In particular, PET imaging of a cancer cell surface marker maximizes specificity, relative to imaging of F-18-deoxyglucose uptake. We have demonstrated specific PET imaging in patients of vasoactive intestinal peptide and pituitary adenylate cyclase activating peptide receptor 1 (VPAC1), which is overexpressed on breast cancer and prostate cancer cells. Our PET agent is Cu-64-TP3805, a peptide derived from vasoactive intestinal peptide. From our ongoing prostate cancer patient imaging study, we segmented and co-registered CT image slices and TP3805 PET image slices, then created moving 3D images. Viewing the rotating 3D images from all sides revealed clear delineation of prostate lesions from the adjoining bladder. The lesion images colocalized in space with intraprostatic tumor foci identified from H&amp;E staining of whole mount slices after prostatectomy. Thus, rapid 3D presentation of anatomical, histological, and molecular images could be a useful preparative tool to minimize complications during prostate cancer surgery. This result supports proceeding to 3D analysis of a larger prostate cancer cohort. Citation Format: Matthew E. Wampole, Bishnuhari Paudyal, Edouard J. Trabulsi, Mathew L. Thakur, Eric Wickstrom. Rotating 3D projections of prostate lesions imaged by Cu-64-VPAC1 ligand PET and CT. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1487. doi:10.1158/1538-7445.AM2015-1487