Ultra-fast whole-body bone tomoscintigraphies achieved with a high-sensitivity 360° CZT camera and a dedicated deep-learning noise reduction algorithm.

This study aimed to determine whether the whole-body bone Single Photon Emission Computed Tomography (SPECT) recording times of around 10 min, routinely provided by a high-sensitivity 360° cadmium and zinc telluride (CZT) camera, can be further reduced by a deep-learning noise reduction (DLNR) algorithm. METHODS: DLNR was applied on whole-body images recorded after the injection of 545 ± 33 MBq of [99mTc]Tc-HDP in 19 patients (14 with bone metastasis) and reconstructed with 100%, 90%, 80%, 70%, 60%, 50%, 40%, and 30% of the original SPECT recording times. RESULTS: Irrespective of recording time, DLNR enhanced the contrast-to-noise ratios and slightly decreased the standardized uptake values of bone lesions. Except in one markedly obese patient, the quality of DLNR processed images remained good-to-excellent down to 60% of the recording time, corresponding to around 6 min SPECT-recording. CONCLUSION: Ultra-fast SPECT recordings of 6 min can be achieved when DLNR is applied on whole-body bone 360° CZT-SPECT.

Curved planar reformatting and convolutional neural network-based segmentation of the small bowel for visualization and quantitative assessment of pediatric Crohn's disease from MRI.

BACKGROUND: Contrast-enhanced MRI of the small bowel is an effective imaging sequence for the detection and characterization of disease burden in pediatric Crohn's disease (CD). However, visualization and quantification of disease burden requires scrolling back and forth through 3D images to follow the anatomy of the bowel, and it can be difficult to fully appreciate the extent of disease. PURPOSE: To develop and evaluate a method that offers better visualization and quantitative assessment of CD from MRI. STUDY TYPE: Retrospective. POPULATION: Twenty-three pediatric patients with CD. FIELD STRENGTH/SEQUENCE: 1.5T MRI system and T1 -weighted postcontrast VIBE sequence. ASSESSMENT: The convolutional neural network (CNN) segmentation of the bowel's lumen, wall, and background was compared with manual boundary delineation. We assessed the reproducibility and the capability of the extracted markers to differentiate between different levels of disease defined after a consensus review by two experienced radiologists. STATISTICAL TESTS: The segmentation algorithm was assessed using the Dice similarity coefficient (DSC) and boundary distances between the CNN and manual boundary delineations. The capability of the extracted markers to differentiate between different disease levels was determined using a t-test. The reproducibility of the extracted markers was assessed using the mean relative difference (MRD), Pearson correlation, and Bland-Altman analysis. RESULTS: Our CNN exhibited DSCs of 75 ± 18%, 81 ± 8%, and 97 ± 2% for the lumen, wall, and background, respectively. The extracted markers of wall thickness at the location of min radius (P = 0.0013) and the median value of relative contrast enhancement (P = 0.0033) could differentiate active and nonactive disease segments. Other extracted markers could differentiate between segments with strictures and segments without strictures (P < 0.05). The observers' agreement in measuring stricture length was >3 times superior when computed on curved planar reformatting images compared with the conventional scheme. DATA CONCLUSION: The results of this study show that the newly developed method is efficient for visualization and assessment of CD. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1565-1576.

A method for improving iodine contrast enhancement in abdominal computed tomography: experimental study in a pig model.

OBJECTIVES: To investigate the improvement in diagnostic image quality of an iodine contrast enhancement tool in an animal model for computed tomography (CT). METHODS: One pig was examined over several consecutive days with a CT system. The quantity of iodine as contrast medium (0.6-1.2 ml/kg) varied among different acquisitions. The contrast enhancement in the reconstructed slices was improved via a post-processing tool. The post-processing tool is an algorithm designed for enhancement of iodine contrast in CT data. Contrast-to-noise ratio (CNR), the detectability between soft-tissue and vascular structures, and quantitative image analysis were assessed. RESULTS: When reducing the quantity of contrast medium, our subjective image quality assessment revealed that it is visually possible to generate similar enhancement with less iodine. This observation was confirmed quantitatively in our CNR results. While employing the algorithm, the CNR between vascular structures and subcutaneous fat significantly improved. For unenhanced regions, we identified no change in HU values and no significant strengthening of artefacts. CONCLUSIONS: With post-processing there was a significantly improved diagnostic image quality compared with non-processed data. In particular, similar contrast enhancement could be achieved with a reduced quantity of contrast medium injected during the CT acquisition.

FDG PET/CT early dynamic blood flow and late standardized uptake value determination in hepatocellular carcinoma.

PURPOSE: To prospectively determine whether fluorine 18 fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) early dynamic blood flow estimates could be used to discriminate hepatocellular carcinoma (HCC) from background liver and to characterize HCC in patients with and those without angioinvasion; and to evaluate the association between blood flow measures at FDG PET/CT with metabolism in HCCs. MATERIALS AND METHODS: Institutional review board approval and written informed consent were obtained for this prospective study. Twenty-one consecutive patients (mean age, 65 years) with 30 established HCCs (mean size, 5.5 cm; seven lesions in five patients with angioinvasion) underwent a blood flow study with an FDG dynamic scan divided into 18 sequences of 5 seconds each and a standard PET/CT scan. On the dynamic study, three independent operators obtained volumes of interest (VOIs) for which three blood flow estimates were calculated (hepatic perfusion index [HPI], time to peak [TTP], and peak intensity [PI]). On the late study, a VOI was placed on the fused scan for each HCC, and maximum standardized uptake value (SUV(max)) was obtained. By using a mixed-effects model analysis, comparison of blood flow estimates between HCC with and that without angioinvasion and background liver was performed. The association between blood flow estimates and SUV(max) was also assessed. RESULTS: HPI and TTP showed better performance than did SUV(max) for discriminating HCC and background liver (areas under receiver operating characteristic curve: 0.96, 0.95, and 0.83, respectively; P < .05). HPI was higher in HCC in patients with angioinvasion (0.91 ± 0.15 [standard deviation]) than in those without angioinvasion (0.80 ± 0.18; P = .03). There was no difference in SUV(max) between HCC in patients with and those without angioinvasion (7.8 ± 2.9 vs 6.3 ± 3.4; P = .85). No clear association was found between HPI, PI, or TTP and SUV(max) (P = .49, .77, and .91, respectively). CONCLUSION: Early dynamic blood flow FDG PET/CT may be used to help discriminate and characterize HCC tumors.

Automated 4-dimensional regional myocardial strain evaluation using cardiac computed tomography.

Evaluation of myocardial regional function is generally performed by visual "eyeballing" which is highly subjective. A robust quantifiable parameter of regional function is required to provide an objective, repeatable and comparable measure of myocardial performance. We aimed to evaluate the clinical utility of novel regional myocardial strain software from cardiac computed tomography (CT) datasets. 93 consecutive patients who had undergone retrospectively gated cardiac CT were evaluated by the software, which utilizes a finite element based tracking algorithm through the cardiac cycle. Circumferential (CS), longitudinal (LS) and radial (RS) strains were calculated for each of 16 myocardial segments and compared to a visual assessment, carried out by an experienced cardiologist on cine movies of standard "echo" views derived from the CT data. A subset of 37 cases was compared to speckle strain by echocardiography. The automated software performed successfully in 93/106 cases, with minimal human interaction. Peak CS, LS and RS all differentiated well between normal, hypokinetic and akinetic segments. Peak strains for akinetic segments were generally post-systolic, peaking at 50 ± 17% of the RR interval compared to 43 ± 9% for normokinetic segments. Using ROC analysis to test the ability to differentiate between normal and abnormal segments, the area under the curve was 0.84 ± 0.01 for CS, 0.80 ± 0.02 for RS and 0.68 ± 0.02 for LS. There was a moderate agreement with speckle strain. Automated 4D regional strain analysis of CT datasets shows a good correspondence to visual analysis and successfully differentiates between normal and abnormal segments, thus providing an objective quantifiable map of myocardial regional function.

Semi-Automated Extraction of Crohns Disease MR Imaging Markers using a 3D Residual CNN with Distance Prior.

We propose a 3D residual convolutional neural network (CNN) algorithm with an integrated distance prior for segmenting the small bowel lumen and wall to enable extraction of pediatric Crohns disease (pCD) imaging markers from T1-weighted contrast-enhanced MR images. Our proposed segmentation framework enables, for the first time, to quantitatively assess luminal narrowing and dilation in CD aimed at optimizing surgical decisions as well as analyzing bowel wall thickness and tissue enhancement for assessment of response to therapy. Given seed points along the bowel lumen, the proposed algorithm automatically extracts 3D image patches centered on these points and a distance map from the interpolated centerline. These 3D patches and corresponding distance map are jointly used by the proposed residual CNN architecture to segment the lumen and the wall, and to extract imaging markers. Due to lack of available training data, we also propose a novel and efficient semi-automated segmentation algorithm based on graph-cuts technique as well as a software tool for quickly editing labeled data that was used to train our proposed CNN model. The method which is based on curved planar reformation of the small bowel is also useful for visualizing, manually refining, and measuring pCD imaging markers. In preliminary experiments, our CNN network obtained Dice coefficients of 75 ± 18%, 81 ± 8% and 97 ± 2% for the lumen, wall and background, respectively.

Strain analysis from 4-D cardiac CT image data.

Strain is a discriminative parameter of regional myocardial dysfunction. Despite the large body of research on myocardial strain analysis in echocardiography and MR images, such techniques have not often been applied to cardiac CT data. Reasons for this include the challenges of sparse image deformation clues and the low temporal resolution. In the current study, we propose an algorithm that uses cardiac CT data to evaluate the mechanical function of the left ventricle. The algorithm is based on a deformable LV model that contains both the myocardium and the blood pool regions and that accounts for the elasticity and incompressibility of the myocardium with the rapid contraction of the blood pool. Our algorithm uses the image intensities of the trabeculle and papillary muscles as well as the border edges in an optical flow manner to extract the 3-D velocities. The resulting strains and rotational values derived from a set of normal patients correlate highly with values from the research literature. We validated our algorithm against 2-D speckle tracking analysis and against visual scores obtained by an expert. Our study shows that strain analysis using CT data can be used in clinical practice.