Acquisition Duration Optimization Using Visual Grading Regression in [68Ga]FAPI-46 PET Imaging of Oncologic Patients.

Fibroblast activation protein is a promising target for oncologic molecular imaging with radiolabeled fibroblast activation protein inhibitors (FAPI) in a large variety of cancers. However, there are yet no published recommendations on how to set up an optimal imaging protocol for FAPI PET/CT. It is important to optimize the acquisition duration and strive toward an acquisition that is sufficiently short while simultaneously providing sufficient image quality to ensure a reliable diagnosis. The aim of this study was to evaluate the feasibility of reducing the acquisition duration of [68Ga]FAPI-46 imaging while maintaining satisfactory image quality, with certainty that the radiologist's ability to make a clinical diagnosis would not be affected. Methods: [68Ga]FAPI-46 PET/CT imaging was performed on 10 patients scheduled for surgical resection of suspected pancreatic cancer, 60 min after administration of 3.6 ± 0.2 MBq/kg. The acquisition time was 4 min/bed position, and the raw PET data were statistically truncated and reconstructed to represent images with an acquisition duration of 1, 2, and 3 min/bed position, additional to the reference images of 4 min/bed position. Four image quality criteria that focused on the ability to distinguish specific anatomic details, as well as perceived image noise and overall image quality, were scored on a 4-point Likert scale and analyzed with mixed-effects ordinal logistic regression. Results: A trend toward increasing image quality scores with increasing acquisition duration was observed for all criteria. For the overall image quality, there was no significant difference between 3 and 4 min/bed position, whereas 1 and 2 min/bed position were rated significantly (P < 0.05) lower than 4 min/bed position. For the other criteria, all images with a reduced acquisition duration were rated significantly inferior to images obtained at 4 min/bed position. Conclusion: The acquisition duration can be reduced from 4 to 3 min/bed position while maintaining satisfactory image quality. Reducing the acquisition duration to 2 min/bed position or lower is not recommended since it results in inferior-quality images so noisy that clinical interpretation is significantly disrupted.

Validation of automated post-adjustments of HDR prostate brachytherapy treatment plans by quantitative measures and oncologist observer study.

PURPOSE: The aim was to evaluate a postprocessing optimization algorithm's ability to improve the spatial properties of a clinical treatment plan while preserving the target coverage and the dose to the organs at risk. The goal was to obtain a more homogenous treatment plan, minimizing the need for manual adjustments after inverse treatment planning. MATERIALS AND METHODS: The study included 25 previously treated prostate cancer patients. The treatment plans were evaluated on dose-volume histogram parameters established clinical and quantitative measures of the high dose volumes. The volumes of the four largest hot spots were compared and complemented with a human observer study with visual grading by eight oncologists. Statistical analysis was done using ordinal logistic regression. Weighted kappa and Fleiss' kappa were used to evaluate intra- and interobserver reliability. RESULTS: The quantitative analysis showed that there was no change in planning target volume (PTV) coverage and dose to the rectum. There were significant improvements for the adjusted treatment plan in: V150% and V200% for PTV, dose to urethra, conformal index, and dose nonhomogeneity ratio. The three largest hot spots for the adjusted treatment plan were significantly smaller compared to the clinical treatment plan. The observers preferred the adjusted treatment plan in 132 cases and the clinical in 83 cases. The observers preferred the adjusted treatment plan on homogeneity and organs at risk but preferred the clinical plan on PTV coverage. CONCLUSIONS: Quantitative analysis showed that the postadjustment optimization tool could improve the spatial properties of the treatment plans while maintaining the target coverage.

A review on AI-based medical image computing in head and neck surgery.

Head and neck surgery is a fine surgical procedure with a complex anatomical space, difficult operation and high risk. Medical image computing (MIC) that enables accurate and reliable preoperative planning is often needed to reduce the operational difficulty of surgery and to improve patient survival. At present, artificial intelligence, especially deep learning, has become an intense focus of research in MIC. In this study, the application of deep learning-based MIC in head and neck surgery is reviewed. Relevant literature was retrieved on the Web of Science database from January 2015 to May 2022, and some papers were selected for review from mainstream journals and conferences, such as IEEE Transactions on Medical Imaging, Medical Image Analysis, Physics in Medicine and Biology, Medical Physics, MICCAI, etc. Among them, 65 references are on automatic segmentation, 15 references on automatic landmark detection, and eight references on automatic registration. In the elaboration of the review, first, an overview of deep learning in MIC is presented. Then, the application of deep learning methods is systematically summarized according to the clinical needs, and generalized into segmentation, landmark detection and registration of head and neck medical images. In segmentation, it is mainly focused on the automatic segmentation of high-risk organs, head and neck tumors, skull structure and teeth, including the analysis of their advantages, differences and shortcomings. In landmark detection, the focus is mainly on the introduction of landmark detection in cephalometric and craniomaxillofacial images, and the analysis of their advantages and disadvantages. In registration, deep learning networks for multimodal image registration of the head and neck are presented. Finally, their shortcomings and future development directions are systematically discussed. The study aims to serve as a reference and guidance for researchers, engineers or doctors engaged in medical image analysis of head and neck surgery.

Prior-aware autoencoders for lung pathology segmentation.

Segmentation of lung pathology in Computed Tomography (CT) images is of great importance for lung disease screening. However, the presence of different types of lung pathologies with a wide range of heterogeneities in size, shape, location, and texture, on one side, and their visual similarity with respect to surrounding tissues, on the other side, make it challenging to perform reliable automatic lesion segmentation. To leverage segmentation performance, we propose a deep learning framework comprising a Normal Appearance Autoencoder (NAA) model to learn the distribution of healthy lung regions and reconstruct pathology-free images from the corresponding pathological inputs by replacing the pathological regions with the characteristics of healthy tissues. Detected regions that represent prior information regarding the shape and location of pathologies are then integrated into a segmentation network to guide the attention of the model into more meaningful delineations. The proposed pipeline was tested on three types of lung pathologies, including pulmonary nodules, Non-Small Cell Lung Cancer (NSCLC), and Covid-19 lesion on five comprehensive datasets. The results show the superiority of the proposed prior model, which outperformed the baseline segmentation models in all the cases with significant margins. On average, adding the prior model improved the Dice coefficient for the segmentation of lung nodules by 0.038, NSCLCs by 0.101, and Covid-19 lesions by 0.041. We conclude that the proposed NAA model produces reliable prior knowledge regarding the lung pathologies, and integrating such knowledge into a prior segmentation network leads to more accurate delineations.

Spherical Convolutional Neural Networks for Survival Rate Prediction in Cancer Patients.

Objective: Survival Rate Prediction (SRP) is a valuable tool to assist in the clinical diagnosis and treatment planning of lung cancer patients. In recent years, deep learning (DL) based methods have shown great potential in medical image processing in general and SRP in particular. This study proposes a fully-automated method for SRP from computed tomography (CT) images, which combines an automatic segmentation of the tumor and a DL-based method for extracting rotational-invariant features. Methods: In the first stage, the tumor is segmented from the CT image of the lungs. Here, we use a deep-learning-based method that entails a variational autoencoder to provide more information to a U-Net segmentation model. Next, the 3D volumetric image of the tumor is projected onto 2D spherical maps. These spherical maps serve as inputs for a spherical convolutional neural network that approximates the log risk for a generalized Cox proportional hazard model. Results: The proposed method is compared with 17 baseline methods that combine different feature sets and prediction models using three publicly-available datasets: Lung1 (n=422), Lung3 (n=89), and H&N1 (n=136). We observed comparable C-index scores compared to the best-performing baseline methods in a 5-fold cross-validation on Lung1 (0.59 ± 0.03 vs. 0.62 ± 0.04). In comparison, it slightly outperforms all methods in inter-data set evaluation (0.64 vs. 0.63). The best-performing method from the first experiment reduced its performance to 0.61 and 0.62 for Lung3 and H&N1, respectively. Discussion: The experiments suggest that the performance of spherical features is comparable with previous approaches, but they generalize better when applied to unseen datasets. That might imply that orientation-independent shape features are relevant for SRP. The performance of the proposed method was very similar, using manual and automatic segmentation methods. This makes the proposed model useful in cases where expert annotations are not available or difficult to obtain.

Benign-malignant pulmonary nodule classification in low-dose CT with convolutional features.

PURPOSE: Low-Dose Computed Tomography (LDCT) is the most common imaging modality for lung cancer diagnosis. The presence of nodules in the scans does not necessarily portend lung cancer, as there is an intricate relationship between nodule characteristics and lung cancer. Therefore, benign-malignant pulmonary nodule classification at early detection is a crucial step to improve diagnosis and prolong patient survival. The aim of this study is to propose a method for predicting nodule malignancy based on deep abstract features. METHODS: To efficiently capture both intra-nodule heterogeneities and contextual information of the pulmonary nodules, a dual pathway model was developed to integrate the intra-nodule characteristics with contextual attributes. The proposed approach was implemented with both supervised and unsupervised learning schemes. A random forest model was added as a second component on top of the networks to generate the classification results. The discrimination power of the model was evaluated by calculating the Area Under the Receiver Operating Characteristic Curve (AUROC) metric. RESULTS: Experiments on 1297 manually segmented nodules show that the integration of context and target supervised deep features have a great potential for accurate prediction, resulting in a discrimination power of 0.936 in terms of AUROC, which outperformed the classification performance of the Kaggle 2017 challenge winner. CONCLUSION: Empirical results demonstrate that integrating nodule target and context images into a unified network improves the discrimination power, outperforming the conventional single pathway convolutional neural networks.

A Comparative Study of Radiomics and Deep-Learning Based Methods for Pulmonary Nodule Malignancy Prediction in Low Dose CT Images.

OBJECTIVES: Both radiomics and deep learning methods have shown great promise in predicting lesion malignancy in various image-based oncology studies. However, it is still unclear which method to choose for a specific clinical problem given the access to the same amount of training data. In this study, we try to compare the performance of a series of carefully selected conventional radiomics methods, end-to-end deep learning models, and deep-feature based radiomics pipelines for pulmonary nodule malignancy prediction on an open database that consists of 1297 manually delineated lung nodules. METHODS: Conventional radiomics analysis was conducted by extracting standard handcrafted features from target nodule images. Several end-to-end deep classifier networks, including VGG, ResNet, DenseNet, and EfficientNet were employed to identify lung nodule malignancy as well. In addition to the baseline implementations, we also investigated the importance of feature selection and class balancing, as well as separating the features learned in the nodule target region and the background/context region. By pooling the radiomics and deep features together in a hybrid feature set, we investigated the compatibility of these two sets with respect to malignancy prediction. RESULTS: The best baseline conventional radiomics model, deep learning model, and deep-feature based radiomics model achieved AUROC values (mean ± standard deviations) of 0.792 ± 0.025, 0.801 ± 0.018, and 0.817 ± 0.032, respectively through 5-fold cross-validation analyses. However, after trying out several optimization techniques, such as feature selection and data balancing, as well as adding context features, the corresponding best radiomics, end-to-end deep learning, and deep-feature based models achieved AUROC values of 0.921 ± 0.010, 0.824 ± 0.021, and 0.936 ± 0.011, respectively. We achieved the best prediction accuracy from the hybrid feature set (AUROC: 0.938 ± 0.010). CONCLUSION: The end-to-end deep-learning model outperforms conventional radiomics out of the box without much fine-tuning. On the other hand, fine-tuning the models lead to significant improvements in the prediction performance where the conventional and deep-feature based radiomics models achieved comparable results. The hybrid radiomics method seems to be the most promising model for lung nodule malignancy prediction in this comparative study.

Early survival prediction in non-small cell lung cancer from PET/CT images using an intra-tumor partitioning method.

PURPOSE: To explore prognostic and predictive values of a novel quantitative feature set describing intra-tumor heterogeneity in patients with lung cancer treated with concurrent and sequential chemoradiotherapy. METHODS: Longitudinal PET-CT images of 30 patients with non-small cell lung cancer were analysed. To describe tumor cell heterogeneity, the tumors were partitioned into one to ten concentric regions depending on their sizes, and, for each region, the change in average intensity between the two scans was calculated for PET and CT images separately to form the proposed feature set. To validate the prognostic value of the proposed method, radiomics analysis was performed and a combination of the proposed novel feature set and the classic radiomic features was evaluated. A feature selection algorithm was utilized to identify the optimal features, and a linear support vector machine was trained for the task of overall survival prediction in terms of area under the receiver operating characteristic curve (AUROC). RESULTS: The proposed novel feature set was found to be prognostic and even outperformed the radiomics approach with a significant difference (AUROCSALoP = 0.90 vs. AUROCradiomic = 0.71) when feature selection was not employed, whereas with feature selection, a combination of the novel feature set and radiomics led to the highest prognostic values. CONCLUSION: A novel feature set designed for capturing intra-tumor heterogeneity was introduced. Judging by their prognostic power, the proposed features have a promising potential for early survival prediction.

Early tumor response prediction for lung cancer patients using novel longitudinal pattern features from sequential PET/CT image scans.

PURPOSE: A new set of quantitative features that capture intensity changes in PET/CT images over time and space is proposed for assessing the tumor response early during chemoradiotherapy. The hypothesis whether the new features, combined with machine learning, improve outcome prediction is tested. METHODS: The proposed method is based on dividing the tumor volume into successive zones depending on the distance to the tumor border. Mean intensity changes are computed within each zone, for CT and PET scans separately, and used as image features for tumor response assessment. Doing so, tumors are described by accounting for temporal and spatial changes at the same time. Using linear support vector machines, the new features were tested on 30 non-small cell lung cancer patients who underwent sequential or concurrent chemoradiotherapy. Prediction of 2-years overall survival was based on two PET-CT scans, acquired before the start and during the first 3 weeks of treatment. The predictive power of the newly proposed longitudinal pattern features was compared to that of previously proposed radiomics features and radiobiological parameters. RESULTS: The highest areas under the receiver operating characteristic curves were 0.98 and 0.93 for patients treated with sequential and concurrent chemoradiotherapy, respectively. Results showed an overall comparable performance with respect to radiomics features and radiobiological parameters. CONCLUSIONS: A novel set of quantitative image features, based on underlying tumor physiology, was computed from PET/CT scans and successfully employed to distinguish between early responders and non-responders to chemoradiotherapy.

Direct estimation of human trabecular bone stiffness using cone beam computed tomography.

OBJECTIVES: The aim of this study was to evaluate the possibility of estimating the biomechanical properties of trabecular bone through finite element simulations by using dental cone beam computed tomography data. STUDY DESIGN: Fourteen human radius specimens were scanned in 3 cone beam computed tomography devices: 3-D Accuitomo 80 (J. Morita MFG., Kyoto, Japan), NewTom 5 G (QR Verona, Verona, Italy), and Verity (Planmed, Helsinki, Finland). The imaging data were segmented by using 2 different methods. Stiffness (Young modulus), shear moduli, and the size and shape of the stiffness tensor were studied. Corresponding evaluations by using micro-CT were regarded as the reference standard. RESULTS: The 3-D Accuitomo 80 (J. Morita MFG., Kyoto, Japan) showed good performance in estimating stiffness and shear moduli but was sensitive to the choice of segmentation method. NewTom 5 G (QR Verona, Verona, Italy) and Verity (Planmed, Helsinki, Finland) yielded good correlations, but they were not as strong as Accuitomo 80 (J. Morita MFG., Kyoto, Japan). The cone beam computed tomography devices overestimated both stiffness and shear compared with the micro-CT estimations. CONCLUSIONS: Finite element-based calculations of biomechanics from cone beam computed tomography data are feasible, with strong correlations for the Accuitomo 80 scanner (J. Morita MFG., Kyoto, Japan) combined with an appropriate segmentation method. Such measurements might be useful for predicting implant survival by in vivo estimations of bone properties.

Quantitative MRI for analysis of peritumoral edema in malignant gliomas.

BACKGROUND AND PURPOSE: Damage to the blood-brain barrier with subsequent contrast enhancement is a hallmark of glioblastoma. Non-enhancing tumor invasion into the peritumoral edema is, however, not usually visible on conventional magnetic resonance imaging. New quantitative techniques using relaxometry offer additional information about tissue properties. The aim of this study was to evaluate longitudinal relaxation R1, transverse relaxation R2, and proton density in the peritumoral edema in a group of patients with malignant glioma before surgery to assess whether relaxometry can detect changes not visible on conventional images. METHODS: In a prospective study, 24 patients with suspected malignant glioma were examined before surgery. A standard MRI protocol was used with the addition of a quantitative MR method (MAGIC), which measured R1, R2, and proton density. The diagnosis of malignant glioma was confirmed after biopsy/surgery. In 19 patients synthetic MR images were then created from the MAGIC scan, and ROIs were placed in the peritumoral edema to obtain the quantitative values. Dynamic susceptibility contrast perfusion was used to obtain cerebral blood volume (rCBV) data of the peritumoral edema. Voxel-based statistical analysis was performed using a mixed linear model. RESULTS: R1, R2, and rCBV decrease with increasing distance from the contrast-enhancing part of the tumor. There is a significant increase in R1 gradient after contrast agent injection (P < .0001). There is a heterogeneous pattern of relaxation values in the peritumoral edema adjacent to the contrast-enhancing part of the tumor. CONCLUSION: Quantitative analysis with relaxometry of peritumoral edema in malignant gliomas detects tissue changes not visualized on conventional MR images. The finding of decreasing R1 and R2 means shorter relaxation times closer to the tumor, which could reflect tumor invasion into the peritumoral edema. However, these findings need to be validated in the future.

A Study of Coronary Bifurcation Shape in a Normal Population.

During percutaneous coronary intervention, stents are placed in narrowings of the arteries to restore normal blood flow. Despite improvements in stent design, deployment techniques and drug-eluting coatings, restenosis and stent thrombosis remain a significant problem. Population stent design based on statistical shape analysis may improve clinical outcomes. Computed tomographic (CT) coronary angiography scans from 211 patients with a zero calcium score, no stenoses and no intermediate artery, were used to create statistical shape models of 446 major coronary artery bifurcations (left main, first diagonal and obtuse marginal and right coronary crux). Coherent point drift was used for registration. Principal component analysis shape scores were tested against clinical risk factors, quantifying the importance of recognised shape features in intervention including size, angles and curvature. Significant differences were found in (1) vessel size and bifurcation angle between the left main and other bifurcations; (2) inlet and curvature angle between the right coronary crux and other bifurcations; and (3) size and bifurcation angle by sex. Hypertension, smoking history and diabetes did not appear to have an association with shape. Physiological diameter laws were compared, with the Huo-Kassab model having the best fit. Bifurcation coronary anatomy can be partitioned into clinically meaningful modes of variation showing significant shape differences. A computational atlas of normal coronary bifurcation shape, where disease is common, may aid in the design of new stents and deployment techniques, by providing data for bench-top testing and computational modelling of blood flow and vessel wall mechanics.

Visualization of liver lesions in standardized video-documented ultrasonography - inter-observer agreement and effect of contrast injection.

UNLABELLED: The AIM of this study was to evaluate the inter-observer agreement and effect of contrast injection on the visibility of liver lesions by radiologists reviewing ultrasound examinations acquired by a radiographer using a standardized examination protocol. MATERIAL AND METHOD: A retrospective review was conducted by two radiologists, independently of each other, of 115 ultrasound examinations of the liver with standardized examination protocols between January 2008 and December 2012. All patients included in the study had undergone surgery for colorectal cancer. Patients attending the two-year follow-up were included. RESULTS: Focal findings, the most common of which were cysts, were seen in 42-43 out of the 115 patients before intravenous contrast and in 46-47 patients after intravenous contrast (p=0.012). The inter-observer agreement for focal findings was 86.1% before contrast, and 90.4% after contrast (n.s.), and the corresponding kappa values were 0.72 and 0.84, respectively. CONCLUSIONS: A good inter-observer agreement between two radiologists reviewing ultrasound examinations (standardized ultrasound cine-loop method acquired by a radiographer) after surgery for colorectal cancer was obtained. Injection of contrast medium increased the visibility of liver lesions.

Visual assessment of biliary excretion of Gd-EOB-DTPA in patients with suspected diffuse liver disease - A biopsy-verified prospective study.

OBJECTIVES: To qualitatively evaluate late dynamic contrast phases, 10, 20 and 30 min, after administration of Gd-EOB-DTPA with regard to biliary excretion in patients presenting with elevated liver enzymes without clinical signs of cirrhosis or hepatic decompensation and to compare the visual assessment of contrast agent excretion with histo-pathological fibrosis stage, contrast uptake parameters and blood tests. METHODS: 29 patients were prospectively examined using 1.5 T MRI. The visually assessed presence or absence of contrast agent for each of five anatomical regions in randomly reviewed time-series was summarized on a four grade scale for each patient. The scores, including a total visual score, were related to the histo-pathological findings, the quantitative contrast agent uptake parameters, expressed as K Hep or LSC_N, and blood tests. RESULTS: No relationship between the fibrosis grade or contrast uptake parameters could be established. A negative correlation between the visual assessment and alkaline phosphatase (ALP) was found. Comparing a sub-group of cholestatic patients with fibrosis score and Gd-EOB-DTPA dynamic parameters did not add any additional significant correlation. CONCLUSIONS: No correlation between visually assessed biliary excretion of Gd-EOB-DTPA and histo-pathological or contrast uptake parameters was found. A negative correlation between the visual assessment and alkaline phosphatase (ALP) was found.

Non-rigid deformation pipeline for compensation of superficial brain shift.

The correct visualization of anatomical structures is a critical component of neurosurgical navigation systems, to guide the surgeon to the areas of interest as well as to avoid brain damage. A major challenge for neuronavigation systems is the brain shift, or deformation of the exposed brain in comparison to preoperative Magnetic Resonance (MR) image sets. In this work paper, a non-rigid deformation pipeline is proposed for brain shift compensation of preoperative imaging datasets using superficial blood vessels as landmarks. The input was preoperative and intraoperative 3D image sets of superficial vessel centerlines. The intraoperative vessels (obtained using 3 Near-Infrared cameras) were registered and aligned with preoperative Magnetic Resonance Angiography vessel centerlines using manual interaction for the rigid transformation and, for the non-rigid transformation, the non-rigid point set registration method Coherent Point Drift. The rigid registration transforms the intraoperative points from the camera coordinate system to the preoperative MR coordinate system, and the non-rigid registration deals with local transformations in the MR coordinate system. Finally, the generation of a new deformed volume is achieved with the Thin-Plate Spline (TPS) method using as control points the matches in the MR coordinate system found in the previous step. The method was tested in a rabbit brain exposed via craniotomy, where deformations were produced by a balloon inserted into the brain. There was a good correlation between the real state of the brain and the deformed volume obtained using the pipeline. Maximum displacements were approximately 4.0 mm for the exposed brain alone, and 6.7 mm after balloon inflation.

Separation of advanced from mild hepatic fibrosis by quantification of the hepatobiliary uptake of Gd-EOB-DTPA.

OBJECTIVES: To apply dynamic contrast-enhanced (DCE) MRI on patients presenting with elevated liver enzymes without clinical signs of hepatic decompensation in order to quantitatively compare the hepatocyte-specific uptake of Gd-EOB-DTPA with histopathological fibrosis stage. METHODS: A total of 38 patients were prospectively examined using 1.5-T MRI. Data were acquired from regions of interest in the liver and spleen by using time series of single-breath-hold symmetrically sampled two-point Dixon 3D images (non-enhanced, arterial and venous portal phase; 3, 10, 20 and 30 min) following a bolus injection of Gd-EOB-DTPA (0.025 mmol/kg). The signal intensity (SI) values were reconstructed using a phase-sensitive technique and normalised using multiscale adaptive normalising averaging (MANA). Liver-to-spleen contrast ratios (LSC_N) and the contrast uptake rate (K (Hep)) were calculated. Liver biopsy was performed and classified according to the Batts and Ludwig system. RESULTS: Area under the receiver-operating characteristic curve (AUROC) values of 0.71, 0.80 and 0.78, respectively, were found for K (Hep), LSC_N10 and LSC_N20 with regard to severe versus mild fibrosis. Significant group differences were found for K (Hep) (borderline), LSC_N10 and LSC_N20. CONCLUSIONS: Liver fibrosis stage strongly influences the hepatocyte-specific uptake of Gd-EOB-DTPA. Potentially the normalisation technique and K (Hep) will reduce patient and system bias, yielding a robust approach to non-invasive liver function determination.

Real-time intraoperative visualization of myocardial circulation using augmented reality temperature display.

For direct visualization of myocardial ischemia during cardiac surgery, we tested the feasibility of presenting infrared (IR) tissue temperature maps in situ during surgery. A new augmented reality (AR) system, consisting of an IR camera and an integrated projector having identical optical axes, was used, with a high resolution IR camera as control. The hearts of five pigs were exposed and an elastic band placed around the middle of the left anterior descending coronary artery to induce ischemia. A proximally placed ultrasound Doppler probe confirmed reduction of flow. Two periods of complete ischemia and reperfusion were studied in each heart. There was a significant decrease in IR-measured temperature distal to the occlusion, with subsequent return to baseline temperatures after reperfusion (baseline 36.9 ± 0.60 (mean ± SD) versus ischemia 34.1 ± 1.66 versus reperfusion 37.4 ± 0.48; p < 0.001), with no differences occurring in the non-occluded area. The AR presentation was clear and dynamic without delay, visualizing the temperature changes produced by manipulation of the coronary blood flow, and showed concentrically arranged penumbra zones during ischemia. Surface myocardial temperature changes could be assessed quantitatively and visualized in situ during ischemia and subsequent reperfusion. This method shows potential as a rapid and simple way of following myocardial perfusion during cardiac surgery. The dynamics in the penumbra zone could potentially be used for visualizing the effect of therapy on intraoperative ischemia during cardiac surgery.

Radiographer-acquired and radiologist-reviewed ultrasound examination--agreement with radiologist's bedside evaluation.

BACKGROUND: Growing demand for ultrasound examinations and higher quality requirements motivate searching for routines combining the diagnostic accuracy of radiologist-performed examinations with the economical advantages of sonographer-performed examinations. One possible approach is to use strictly standardized acquisition and documentation schemes that give the radiologist access to all relevant information after the examination. PURPOSE: To compare a recently introduced routine, combining acquisition by a radiographer, documentation as standardized cine-loops, and review by a radiologist ('standardized method'), with the formerly used routine where the diagnosis is made bedside by the radiologist ('traditional method'). MATERIAL AND METHODS: In 64 policlinic patients, the kidneys (n=27) or the gallbladder (n=37) were examined with both the standardized and the traditional method. The radiologists' findings of hydronephrosis, tumors, cysts, echogenicity changes, and cortical thickness (in the kidneys), and wall thickness, concrements, and polyps (in the gallbladder) were compared between the methods with respect to agreement (proportion of agreement and kappa coefficient) as well as systematic differences (McNemar's test). RESULTS: The findings at the gallbladder examination showed a median agreement of 97% (86-100%; kappa=0.64-1.00), and those at the kidney examination, an agreement of 90% (78-100%; kappa=0.69-1.00). There were no significant systematic differences between the methods. CONCLUSION: The satisfactory agreement in this preliminary study indicates that the new workflow with ultrasound examinations performed by a radiographer and analyzed off-line by a radiologist is promising, and motivates further studies.

Making the PACS workstation a browser of image processing software: a feasibility study using inter-process communication techniques.

PURPOSE: To enhance the functional expandability of a picture archiving and communication systems (PACS) workstation and to facilitate the integration of third-part image-processing modules, we propose a browser-server style method. METHODS: In the proposed solution, the PACS workstation shows the front-end user interface defined in an XML file while the image processing software is running in the background as a server. Inter-process communication (IPC) techniques allow an efficient exchange of image data, parameters, and user input between the PACS workstation and stand-alone image-processing software. Using a predefined communication protocol, the PACS workstation developer or image processing software developer does not need detailed information about the other system, but will still be able to achieve seamless integration between the two systems and the IPC procedure is totally transparent to the final user. RESULTS: A browser-server style solution was built between OsiriX (PACS workstation software) and MeVisLab (Image-Processing Software). Ten example image-processing modules were easily added to OsiriX by converting existing MeVisLab image processing networks. Image data transfer using shared memory added <10ms of processing time while the other IPC methods cost 1-5 s in our experiments. CONCLUSION: The browser-server style communication based on IPC techniques is an appealing method that allows PACS workstation developers and image processing software developers to cooperate while focusing on different interests.

Integrating automatic and interactive methods for coronary artery segmentation: let the PACS workstation think ahead.

PURPOSE: To present newly developed software that can provide fast coronary artery segmentation and accurate centerline extraction for later lesion visualization and quantitative measurement while minimizing user interaction. METHODS: Previously reported fully automatic and interactive methods for coronary artery extraction were optimized and integrated into a user-friendly workflow. The user's waiting time is saved by running the non-supervised coronary artery segmentation and centerline tracking in the background as soon as the images are received. When the user opens the data, the software provides an intuitive interactive analysis environment. RESULTS: The average overlap between the centerline created in our software and the reference standard was 96.0%. The average distance between them was 0.38 mm. The automatic procedure runs for 1.4-2.5 min as a single-thread application in the background. Interactive processing takes 3 min in average. CONCLUSION: In preliminary experiments, the software achieved higher efficiency than the former interactive method, and reasonable accuracy compared to manual vessel extraction.