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In light of extended stay-at-home periods during the coronavirus disease 2019 pandemic, recent societal trends have revealed an increased use of online media to remain connected. Simultaneously, interests in at-home cooking and baking, particularly of "comfort foods" have increased. Because flour is a crucial component in many of these products, we analyze how the U.S. public, in social and online media spaces, references "flour" and its use. We also quantify the share of media mentions about flour that are devoted to flour-related food safety risks and/or risk mitigation. It was found that the volume of mentions about flour and its use fluctuate seasonally, often increasing ahead of the winter holiday season (November to December). Further, the volume of interest rapidly increased in March 2020 when stay-at-home orders were issued. The share of media devoted to flour-related food safety risks or associated illness was extremely small but generally corresponded with flour recall announcements or other public risk communications. Overall, the interest in flour and its use remains seasonal and predictably related to societal trends, such as increased baking at home during the holidays or 2020 stay-at-home orders. However, awareness of flour-related food safety risks seems largely absent on the basis of online media data collection and analysis, except in immediate reactions to flour recalls. This study suggests that more flour safety education programs may be desired to support consumers' informed decision making.

The three-dimensional (3D) voxel labeling of lesions requires significant radiologists’ effort in the development of computer-aided detection software. To reduce the time required for the 3D voxel labeling, we aimed to develop a generalized semiautomatic segmentation method based on deep learning via a data augmentation-based domain generalization framework. In this study, we investigated whether a generalized semiautomatic segmentation model trained using two types of lesion can segment previously unseen types of lesion. We targeted lung nodules in chest CT images, liver lesions in hepatobiliary-phase images of Gd-EOB-DTPA-enhanced MR imaging, and brain metastases in contrast-enhanced MR images. For each lesion, the 32 × 32 × 32 isotropic volume of interest (VOI) around the center of gravity of the lesion was extracted. The VOI was input into a 3D U-Net model to define the label of the lesion. For each type of target lesion, we compared five types of data augmentation and two types of input data. For all considered target lesions, the highest dice coefficients among the training patterns were obtained when using a combination of the existing data augmentation-based domain generalization framework and random monochrome inversion and when using the resized VOI as the input image. The dice coefficients were 0.639 ± 0.124 for the lung nodules, 0.660 ± 0.137 for the liver lesions, and 0.727 ± 0.115 for the brain metastases. Our generalized semiautomatic segmentation model could label unseen three types of lesion with different contrasts from the surroundings. In addition, the resized VOI as the input image enables the adaptation to the various sizes of lesions even when the size distribution differed between the training set and the test set.

For 177Lu-DOTATATE treatments, dosimetry based on manual kidney segmentation from computed tomography (CT) is accurate but time consuming and might be affected by misregistration between CT and SPECT images. This study develops a convolution neural network (CNN) for automated kidney segmentation that accurately aligns CT segmented volume of interest (VOI) to the kidneys in SPECT images. The CNN was trained with SPECT/CT images performed over the abdominal area of 137 patients treated with 177Lu-DOTATATE. Activity concentrations in automated and manual segmentations were strongly correlated for both kidneys (r > 0.96, p

To develop a preoperative MRI-based radiomic-clinical nomogram for prediction of residual disease (RD) in patients with advanced high-grade serous ovarian carcinoma (HGSOC). In total, 217 patients with advanced HGSOC were enrolled from January 2014 to June 2019 and randomly divided into a training set (n = 160) and a validation set (n = 57). Finally, 841 radiomic features were extracted from each tumor on T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (CE-T1WI) sequence, respectively. We used two fusion methods, the maximal volume of interest (MV) and the maximal feature value (MF), to fuse the radiomic features of bilateral tumors, so that patients with bilateral tumors have the same kind of radiomic features as patients with unilateral tumors. The radiomic signatures were constructed by using mRMR method and LASSO classifier. Multivariable logistic regression analysis was used to develop a radiomic-clinical nomogram incorporating radiomic signature and conventional clinico-radiological features. The performance of the nomogram was evaluated on the validation set. In total, 342 tumors from 217 patients were analyzed in this study. The MF-based radiomic signature showed significantly better prediction performance than the MV-based radiomic signature (AUC = 0.744 vs. 0.650, p = 0.047). By incorporating clinico-radiological features and MF-based radiomic signature, radiomic-clinical nomogram showed favorable prediction ability with an AUC of 0.803 in the validation set, which was significantly higher than that of clinico-radiological signature and MF-based radiomic signature (AUC = 0.623, 0.744, respectively). The proposed MRI-based radiomic-clinical nomogram provides a promising way to noninvasively predict the RD status. • MRI-based radiomic-clinical nomogram is feasible to noninvasively predict residual disease in patients with advanced HGSOC. • The radiomic signature based on MF showed significantly better prediction performance than that based on MV. • The radiomic-clinical nomogram showed a favorable prediction ability with an AUC of 0.803.

This study proposes a new contribution to solve the problem of automatic landmarks detection in three-dimensional cephalometry. 3D images obtained from CBCT (cone beam computed tomography) equipment were used for automatic identification of twelve landmarks. The proposed method is based on a local geometry and intensity criteria of skull structures. After the step of preprocessing and binarization, the algorithm segments the skull into three structures using the geometry information of nasal cavity and intensity information of the teeth. Each targeted landmark was detected using local geometrical information of the volume of interest containing this landmark. The ICC and confidence interval (95% CI) for each direction were 0, 91 (0.75 to 0.96) for x- direction; 0.92 (0.83 to 0.97) for y-direction; 0.92 (0.79 to 0.97) for z-direction. The mean error of detection was calculated using the Euclidian distance between the 3D coordinates of manually and automatically detected landmarks. The overall mean error of the algorithm was 2.76 mm with a standard deviation of 1.43 mm. Our proposed approach for automatic landmark identification in 3D cephalometric was capable of detecting 12 landmarks on 3D CBCT images which can be facilitate the use of 3D cephalometry to orthodontists.

In the last few decades, the medical and healthcare scientific communities have focused their attention on the use or development of real-time monitoring devices and remote control systems. New generations of wearable, portable, and implantable devices offer better and more accurate measurements/prognosis for those that suffer from diseases and/or disabilities. Thus, there are still challenging issues of current ultrasound imaging (USI) systems, such as low-quality ultrasound images, slow time response to emergencies, and location-based operation. Thus, in response to these challenges, we present a new low-cost, portable/wearable 3-D array ultrasound prognosis system with advanced imaging capabilities that offer high-resolution (HR) accurate results in a near real-time response. The USI unique features are based on 2-D array transducers with 3-D overlapping capabilities and a new image enhancement methodology compatible with the system's structural characteristics to compensate for any loss of image quality. This system will offer an alternative way of ultrasound examination, independent of the radiologist's skills, that is, a system to be capable of automatic scanning of the volume of interest (VOI) without the guidance of the radiologist.

BACKGROUND R2* estimation reflects the paramagnetism of the tumor tissue, which may be used to differentiate between benign and malignant liver lesions when contrast agents are contraindicated. AIM To investigate whether R2* derived from multi-echo Dixon imaging can aid differentiating benign from malignant focal liver lesions (FLLs) and the impact of 2D region of interest (2D-ROI) and volume of interest (VOI) on the outcomes. METHODS We retrospectively enrolled 73 patients with 108 benign or malignant FLLs. All patients underwent conventional abdominal magnetic resonance imaging and multi-echo Dixon imaging. Two radiologists independently measured the mean R2* values of lesions using 2D-ROI and VOI approaches. The Bland–Altman plot was used to determine the interobserver agreement between R2* measurements. Intraclass correlation coefficient (ICC) was used to determine the reliability between the two readers. Mean R2* values were compared between benign and malignant FFLs using the nonparametric Mann–Whitney test. Receiver operating characteristic curve analysis was used to determine the diagnostic performance of R2* in differentiation between benign and malignant FFLs. We compared the diagnostic performance of R2* measured by 2D-ROI and VOI approaches. RESULTS This study included 30 benign and 78 malignant FLLs. The interobserver reproducibility of R2* measurements was excellent for the 2D-ROI (ICC = 0.994) and VOI (ICC = 0.998) methods. Bland–Altman analysis also demonstrated excellent agreement. Mean R2* was significantly higher for malignant than benign FFLs as measured by 2D-ROI (P < 0.001) and VOI (P < 0.001). The area under the curve (AUC) of R2* measured by 2D-ROI was 0.884 at a cut-off of 25.2/s, with a sensitivity of 84.6% and specificity of 80.0% for differentiating benign from malignant FFLs. R2* measured by VOI yielded an AUC of 0.875 at a cut-off of 26.7/s in distinguishing benign from malignant FFLs, with a sensitivity of 85.9% and specificity of 76.7%. The AUCs of R2* were not significantly different between the 2D-ROI and VOI methods. CONCLUSION R2* derived from multi-echo Dixon imaging whether by 2D-ROI or VOI can aid in differentiation between benign and malignant FLLs.

Background The amount of signal decreases when the acquisition duration is shortened. However, it is not clear how this affects the quantitative values. This study aims to clarify the effect of acquisition time shortening in brain tumor PET/CT using 11C-methionine on the quantitative values. Method This study was a retrospective analysis of 30 patients who underwent clinical 11C-methionine PET/CT examination. PET images were acquired in list mode for 10 min. PET images of acquisition duration from 1 to 10 min with 1-min step were reconstructed. We examined the effect on the quantitative values of acquisition duration. We placed a volume of interest to include the entire tumor and regions of interest in the shape of a large crescent in the contralateral hemisphere in 5 contiguous axial slices as normal tissue. Quantitative values examined were maximum, peak, and mean standardized uptake values (SUVmax, SUVpeak, SUVmean), metabolic tumor volume (MTV), and maximum tumor to normal tissue ratio (TNRmax), with each duration compared to that with 10 min. Results SUVmax, MTV, and TNRmax showed the highest values due to the effects of statistical noise when the acquisition time was 1 min. These values were stable when the acquisition duration was > 6 min. SUVpeak and SUVmean showed mostly consistent values regardless of duration. Conclusions SUVmax, MTV, and TNRmax are affected by acquisition time. If the acquisition duration was > 6 min, the fluctuation could be suppressed within 5% in these quantitative values. However, SUVpeak was suggested to be a robust index regardless of the acquisition duration.

Background Extravasation of radiopharmaceuticals used for vectorized internal radiotherapy can lead to severe tissue damage (van der Pol et al., Eur J Nucl Med Mol Imaging 44:1234–1243, 2017). Clinical management of these extravasations requires the preliminary estimation of the dose distribution in the extravasation area. Data are scarce regarding the dose estimation in the literature. This work presents a methodology for estimating the dose distribution after an extravasation occurred in September 2017, in the arm of a patient during a 7.4-GBq infusion of Lutathera ® (AAA). Methods A local quantification procedure initially developed for renal dosimetry was used. A calibration factor was determined and verified by phantom study. Extravasation volume of interest and its variation in time were determined using 4 whole body (WB) planar acquisitions performed at 2 h (T2h), 5 h (T5h), 20 h (T20h), and 26 h (T26h) after the beginning of the infusion and three SPECT/CT thoracic acquisitions at T5h, T20h, and T26h. For better estimation of initial extravasation volume, 3 volumes were defined on SPECT images using a 3D activity threshold. Cumulated activities and associated absorbed doses (D1, D2, D3) were calculated in the 3 volumes using the MIRD formalism. Results Volumes estimated using 3D threshold were V1 = 1000 mL, V2 =400 mL, and V3 =180 mL. Cumulated activities were evaluated using a monoexponential fit on activities calculated on SPECT images. Estimated local absorbed doses in V1, V2, and V3 were D1 = 2.3 Gy, D2 = 4.1 Gy, and D3 = 6.8 Gy. Evolution in time of local activity in the extravasation area was consistent with an effective local half-life (Teff) of 2.3 h. Conclusions Rapid local dose estimation was permitted thanks to knowledge of the calibration factor determined previous to accidental extravasation. Lutathera® lymphatic drainage was quick in the arm (Teff = 2.3h). Estimated doses were in the lower range of deterministic effects and far under soft tissue necrosis threshold. Thus, no surgical rinse was proposed. The patient did not show any clinical consequence of the extravasation.

The aim of this study was to investigate the relationship of quantitative parameters between the two-dimensional region of interest (ROI) and the three-dimensional volume of interest (VOI) for accumulation of radiopharmaceutical. Single-photon emission computed tomography combined with computed tomography (SPECT/CT) images of the NEMA/IEC phantom were acquired. The ROIs and VOIs were automatically set to the sphere and background in the phantom. We defined as two-dimensional analysis (2D analysis) that which used ROIs set on the center section of the sphere, and as three-dimensional analysis (3D analysis) that which used VOIs set on the center of gravity of the sphere. Dose linearity (DL), the recovery coefficient (RC), the contrast-to-noise ratio (CNR), and standardized uptake value (SUV) were evaluated. Each index value was compared between both analyses. DL was almost 1 under both conditions. RC showed a similar tendency with 2D and 3D analyses. The CNR for 3D analysis was smaller than for 2D analysis. The maximum SUV was almost equal with both analyses. The mean SUV with 3D analysis was underestimated by 4.83% on average compared with 2D analysis. For the same accumulation, a difference may occur in the quantitative index between 2 and 3D analyses. In particular, the quantitative parameters based on the average value tends to be smaller with 3D analysis than 2D analysis. The quantitative parameters in 2D analysis showed dependence upon the cross section used for setting the ROI, whereas 3D analysis showed less dependence on the position of the VOI.

Purpose A standardized method for quantification is required for analyzing PET data, but such standards have not been established for tau PET imaging. The Centiloid scale has recently been proposed as a standard method for quantifying amyloid deposition on PET imaging. Therefore, the present study aimed to apply the Centiloid scale to 18F-THK5351 PET imaging in Alzheimer’s disease (AD). Methods We acquired 18F-THK5351 PET, 11C-PiB PET, and MR images from 47 cognitively normal (CN) individuals and 28 patients with AD with mild to moderate dementia. PET images were spatially normalized to Montreal Neurological Institute space. The PET signals were then normalized using the signal in the reference volume of interest (VOI). Target VOI for specific 18F-THK5351 retention in AD was extracted by voxel-wise comparison of PET images between the 47 CN individuals and 16 AD patients with moderate dementia. Scale anchor points were defined by the CN individuals as 0-anchor points and by that of the average of the typical AD patients as 100-anchor points. Results Specific retention of 18F-THK5351 was predominant in the angular gyrus, inferior temporal cortex, and parieto-occipital regions in patients with AD. Standardized uptake value ratio (SUVR) of 1.227 and 1.797 were defined as 0- and 100-anchor points, respectively. 18F-THK5351 PET data could be expressed using the Centiloid scale, with the SUVR of the 18F-THK5351 PET images converted to Centiloid using our VOI, the standard Centiloid reference VOI, and the following equation: Centiloid = 169.0 × SUVR–204.6. Conclusion Centiloid methods can be applied to tau PET imaging using 18F-THK5351.

3D Computer Vision algorithms are a subject of research and application for several industrial processes. The Volume of Interest (VOI) usually refer to sub-objects with basic shapes for computing these algorithms. However, in many cases the objects are available as irregular shapes with many vertices, and in order to apply algorithms effectively, it is essential to compute the largest volume parallelepipedon contained in 3D objects. There are no other approximation algorithms for finding the largest volume parallelepipedon of arbitrary orientation inscribed in a closed 3D contour with a computational cost better than the algorithm proposed in this paper, been $O(n^{3})$ .

To evaluate the performance of novel spiral MRSI and tissue segmentation pipeline of the brain, to investigate neurometabolic changes in normal-appearing white matter (NAWM) and white matter lesions (WML) of stable relapsing remitting multiple sclerosis (RRMS) compared to healthy controls (HCs).Spiral 3D MRSI using LASER-GOIA-W [16,4] was undertaken on 16 RRMS patients and 9 HCs, to acquire MRSI data from a large volume of interest (VOI) 320 cmCompared to HCs, RRMS demonstrated a statistically significant reduction in all mean brain tissues and increase in CSF volume. Within VOI, WM decreased (-10%) and CSF increased (41%) in RRMS compared to HCs (p 0.001). MRSI revealed that total creatine (tCr) ratios of N-acetylaspartate and glutamate+glutamine in WML were significantly lower than NAWM-MS (-9%, -8%) and HCs (-14%, -10%), respectively. Myo-inositol/tCr in WML was significantly higher than NAWM-MS (14%) and HCs (10%). SVM of MRSI yielded accuracy, sensitivity and specificity of 86%, 95%, and 70%, respectively for HCs vs WML, which were higher than HC vs NAWM and WML vs NAWM models.This study demonstrates the benefit of MRSI in evaluating MS neurometabolic changes in NAWM. SVM of MRSI data in the MS brain may be suited for clinical monitoring and progression of MS patients. Longitudinal MRSI studies are warranted.

This paper explores a technique for visually/diagnostically lossless coding in the wavelet domain to effectively compress the three-dimensional medical image data. The quantisation module based on ...

PURPOSE Over the last two decades, extended criteria have promoted an increased number of donor livers available for liver transplantation. But posttransplant graft loss is still a major concern. Macrovesicular hepatic steatosis (MHS) is recognized as the most significant prognostic histologic parameter in predicting posttransplant graft loss. We aimed to evaluate the utility of ex vivo volumetric quantitative MRI for quantifying MHS before liver transplantation using proton density fat-fraction (PDFF-MRI) histogram analysis. METHODS PDFF-MRI was performed at 3.0T in 40 livers. We obtained histogram parameters of whole-liver volume of interest, including the mean, median, 5th, 10th, 25th, 75th, 90th, and 95th percentile PDFF; skewness; kurtosis; entropy; and volume. RESULTS Livers from 40 cadaveric donors were included, and histologic ex vivo fat quantification was available for 33 livers. Ten livers had MHS and 23 had normal fat content. The MHS group had higher mean, median, 5th, 10th, 25th, 75th, 90th, and 95th percentile PDFF, and entropy than the group with normal fat content (P < .05). Median PDFF had greater area under the curve value than other parameters. Mean PDFF showed an excellent correlation with entropy and a moderate correlation with MHS quantification on histology. CONCLUSIONS Ex vivo volumetric quantitative PDFF-MRI histogram analysis is a very useful and noninvasive method to detect MHS before liver transplantation. Median PDFF was the best predictor of the presence of MHS. Entropy is a very promising parameter.

Aim: To differentiate Warthin tumors (WTs) and pleomorphic adenomas (PAs) measuring heterogeneity of intravoxel incoherent motion (IVIM) and dynamic-contrast enhanced-magnetic resonance imaging biomarkers. Methods: Volumes of interest were traced on 18 WT and 18 PA in 25 patients. For each IVIM and dynamic-contrast enhanced biomarker, histogram parameters were calculated and then compared using the Wilcoxon-signed-rank test. Receiver operating characteristic curves and multivariate analysis were employed to identify the parameters and their pairs with the best accuracy. Results: Most of the biomarkers exhibited significant difference (p < 0.05) between PA and WT for histogram parameters. Time to peak median and skewness, and D* median and entropy showed the highest area under the curve. No meaningful improvement of accuracy was obtained using two features. Conclusion: IVIM and dynamic-contrast enhanced histogram descriptors may help in the classification of WT and PA.

Volumetric quantitative analyses of bone micromorphometry changes following orthodontic tooth movements are hardly standardizable. The present study aimed at validating and applying a novel microcomputed tomography (CT)-based approach that enables the segmentation of teeth and definition of a standardized volume of interest (VOI) around the roots to assess local bone micromorphometry.The jaws of 3 untreated and 14 orthodontically treated mice (protraction of the upper right molar for 11 days with 0.5 N; untreated left upper molar) were scanned with a micro-CT. The first molars and the alveolar bone were segmented, and a standardized VOI was defined around the teeth. The bone volume per total volume (BV/TV) was assessed within the VOI, and BV/TV values were compared between contralateral sites in both untreated (method validation) and treated animals (method application).The intraclass correlation coefficient of 0.99 revealed high reliability of the method. In the untreated animals, Bland-Altman analysis confirmed comparable BV/TV fractions (mean difference: -1.93, critical difference: 1.91, Wilcoxon: p = 0.03). In the orthodontically treated animals, BV/TV values were significantly lower at the test compared to the control site (test: 33.23% ± 5.74%, control: 41.33% ± 4.91%, Wilcoxon: p 0.001).Within the limits of the study, the novel approach demonstrated the applicability to evaluate bone micromorphometry around teeth subjected to orthodontic treatment.HINTERGRUND: Volumetrische quantitative Analysen von Veränderungen der Knochenmikrostruktur nach kieferorthopädischen Zahnbewegungen sind schwer standardisierbar. Ziel der vorliegenden Studie war die Validierung und Anwendung eines neuartigen mikro-computertomographie(µ-CT)-basierten Ansatzes, der die Segmentierung von Zähnen und die Definition eines standardisierten „volume of interest“ (VOI) um die Wurzeln herum ermöglicht, um die lokale Knochenmikrostruktur zu beurteilen.Die Kiefer von 3 unbehandelten und 14 kieferorthopädisch behandelten Mäusen (Protraktion des oberen rechten Molaren für 11 Tage mit 0,5 N; unbehandelter linker oberer Molar) wurden mit einem Mikro-CT gescannt. Die ersten Molaren und der Alveolarknochen wurden segmentiert und ein standardisiertes VOI um die Zähne definiert. Das Knochenvolumen pro Gesamtvolumen (BV/TV) wurde innerhalb des VOIs bestimmt. Die BV/TV-Werte wurden zwischen den kontralateralen Seiten sowohl bei unbehandelten (Methodenvalidierung) als auch bei kieferorthopädisch behandelten Tieren (Methodenanwendung) verglichen.Der Intraklassenkorrelationskoeffizient von 0,99 zeigte eine hohe Reliabilität der Methode. Bei den unbehandelten Tieren bestätigte die Bland-Altman-Analyse vergleichbare BV/TV-Werte (mittlere Differenz: −1,93, kritische Differenz: 1,91, Wilcoxon-Test: p = 0,03). Bei den kieferorthopädisch behandelten Tieren waren die BV/TV-Werte in der Test- im Vergleich zur Kontrollgruppe signifikant niedriger (Test: 33,23 % ± 5,74 %, Kontrolle: 41,33 % ± 4,91 %, Wilcoxon-Test: p 0,001).Im Rahmen der Studie zeigte der neuartige Ansatz die Anwendbarkeit zur Bewertung der Knochenmikrostruktur um Zahnwurzeln von Molaren, die einer kieferorthopädischen Behandlung unterzogen wurden.

The investigations on ceramic foams with porous, heterogeneous microstructure are based on the standards and dimensions for dense materials due to the lack of alternatives. It is known that variations in pore size, shape and porosity have a significant effect on the physical properties, however, the considered sample volume is treated here as a secondary interest. To determine the representative volume-of-interest (REVOI), image data from micro tomography measurements of 30,45 and 60 ppi (pore per inch) Al2O3 foams were used. The REVOI is the minimum volume that comprehensively represents the microstructure and can thus be considered a unit volume. The Minkowski functionals allow an invariant microstructure description by single value using stereology approaches. Based on these values of defined volume sets, the REVOI was determined as a novelty in microstructure characterization. Additional analysis of the pore sizes in the 3D volume and determination of tortuosity and permeability at the previously defined volumes allowed validation of the REVOI.

OBJECTIVES: To investigate whether quantitative textural features, extracted from pretreatment MRI, can predict sustained complete response to radiofrequency ablation (RFA) in patients with hepatocellular carcinoma (HCC). METHODS: In this IRB-approved study, patients were selected from a maintained six-year database of consecutive patients who underwent both pretreatment MRI imaging with a probable or definitive imaging diagnosis of HCC (LI-RADS 4 or 5) and loco-regional treatment with RFA. An experienced radiologist manually segmented the hepatic nodules in MRI arterial and equilibrium phases to obtain the volume of interest (VOI) for extraction of 107 quantitative textural features, including shape and first- and second-order features. Statistical analysis was performed to evaluate associations between textural features and complete response. RESULTS: The study consisted of 34 patients with 51 treated hepatic nodules. Sustained complete response was achieved by 6 patients (4 with single nodule and 2 with multiple nodules). Of the 107 features from the arterial and equilibrium phases, 20 (18%) and 25 (23%) achieved AUC >0.7, respectively. The three best performing features were found in the equilibrium phase: Dependence Non-Uniformity Normalized and Dependence Variance (both GLDM class, with AUC of 0.78 and 0.76, respectively) and Maximum Probability (GLCM class, AUC of 0.76). CONCLUSIONS: This pilot study demonstrates that a radiomic analysis of pre-treatment MRI might be useful in identifying patients with HCC who are most likely to have a sustained complete response to RFA. Second-order features (GLDM and GLCM) extracted from equilibrium phase obtained highest discriminatory performance.

Objective: To evaluate the effect that external cooling of the salivary glands (ECSG) has on the uptake of gallium-68-labeled prostate-specific membrane antigen (68Ga-PSMA), as an indirect assessment of the capacity of ECSG to reduce the local dose in lutetium-177-PSMA-617 radioligand therapy. Materials and Methods: Ten patients with prostate cancer were submitted to 68Ga-PSMA positron emission tomography/computed tomography with unilateral ECSG. The ECSG was started at 30 min before the injection of the radiotracer and maintained until the end of image acquisition (1 h after injection). Each salivary gland was assessed by determining the maximum, mean, and peak standardized uptake values (SUVmax, SUVmean, and SUVpeak, respectively). The volume of each gland was determined in a volume of interest delineated by a threshold SUVmax of 10%. Paired Student’s t-tests were used in order to compare the results. Results: In terms of the SUV parameters, there were no statistically significant differences between the cooled and contralateral salivary glands. However, the mean volume was 27% lower in the cooled parotid glands than in the contralateral parotid glands (p = 0.004). Conclusion: The use of ECSG does not appear to reduce 68Ga-PSMA uptake by the salivary glands. In addition, there is yet no evidence that ECSG is effective in preventing salivary gland toxicity. Resumo Objetivo: Avaliar o impacto do resfriamento externo de glândulas salivares (REGS) na captação de 68Ga-PSMA como marcador indireto dessa intervenção para redução da dose local na terapia com 177Lu-PSMA. Materiais e Métodos: Dez pacientes com câncer de próstata foram submetidos a PET/CT com 68Ga-PSMA com REGS unilateral. O resfriamento se iniciou 30 minutos antes da injeção do radiofármaco até o fim da aquisição de imagem, 1 hora após a injeção. Cada glândula foi avaliada para os valores de captação padronizados máximo, médio e pico (SUVmáx, SUVmédio e SUVpico, respectivamente). O volume foi definido por um isocontorno usando 10% do SUVmáx. Os resultados foram comparados com o teste t de Student. Resultados: Não houve diferença estatisticamente significante entre os valores de SUV das glândulas resfriadas e seus controles. Houve 27% de redução volumétrica (p = 0,004) nas parótidas resfriadas em comparação ao controle. Conclusão: Não houve redução da captação de 68Ga-PSMA nas glândulas salivares ao REGS. Atualmente não há evidências que suportem essa prática clínica.

Background 18F-NaF positron emission tomography (PET) targets microcalcifications. We compared in vitro microPET assessed 18F-NaF uptake between culprit and non-culprit human carotid plaques. Furthermore, we compared 18F-NaF uptake with calcification visualized on microcomputed tomography (microCT). Methods Carotid plaques from stroke patients undergoing surgery were incubated in 18F-NaF and scanned using a microPET and a microCT scan. The average PET assessed 18F-NaF uptake was expressed as percentage of the incubation dose per gram (%Inc/g). 18F-NaF PET volume of interest (VOI) was compared with CT calcification VOI. Results 23 carotid plaques (17 culprit, 6 non-culprit) were included. The average 18F-NaF uptake in culprit carotid plaques was comparable with the uptake in non-culprit carotid plaques (median 2.32 %Inc/g [IQR 1.98 to 2.81] vs. median 2.35 %Inc/g [IQR 1.77 to 3.00], P = 0.916). Only a median of 10% (IQR 4 to 25) of CT calcification VOI showed increased 18F-NaF uptake, while merely a median of 35% (IQR 6 to 42) of 18F-NaF PET VOI showed calcification on CT. Conclusions 18F-NaF PET represents a different stage in the calcification process than CT. We observed a similar PET assessed 18F-NaF uptake and pattern in culprit and non-culprit plaques of high-risk patients, indicating that this method may be of more value in early atherosclerotic stenosis development. Electronic supplementary material The online version of this article (10.1007/s12350-018-1325-5) contains supplementary material, which is available to authorized users.

Aim:The study aimed to assess the clinical feasibility of employing an automatic match during cone beam computed tomography (CBCT) imaging using prostatic calcifications within the 95% isodose set as the region of interest.Materials and methods:CBCT images were analysed on the 5th fraction in 34 patients evaluating the difference between standard manual soft tissue anatomy matching versus auto calcification matching. An assessment of the clinical feasibility of using prostatic calcifications during matching alongside considering the effect a more automated matching process has been conducted on interobserver variability.Results:The standard deviation values of the difference between the soft tissue match (baseline) versus automatic calcification matches fluctuated around 1 mm in all three axes for all of the matches carried out. The interobserver variability observed between the two radiographers was 0·055, 0·065 and 0·045 cm in the vertical, longitudinal and lateral axes, respectively.Findings:The clarity of the calcifications on the CBCT images might explain the low interobserver variability displayed by the two matching radiographers. A calcification provides a clear starting point for image matching before commencing a check of volumetric coverage, if the matching process begins in the same place, it can allow for a standardisation of matching technique between radiographers.

Objective The aim of this pilot study was to explore the integrated positron emission tomography and magnetic resonance imaging scanner (PET/MR) for biological characterization of nasopharyngeal carcinoma (NPC) and potential therapeutic applications of dose painting (DP). Patients and methods Twenty-one NPC patients with PET/MR were included in this study. Overlap of tumor volumes was analyzed on T2-weighted images (volume of interest, VOIT2), diffusion-weighted magnetic resonance imaging (VOIDWI) and 18F-fluorodeoxyglucose positron emission tomography (VOIPET). The overlap percentages of low-metabolic sub-region (cluster 1) and high-metabolic sub-region (cluster 2) in VOIPET and VOIDWI were analyzed by cluster analysis. Results Both the VOIDWI and VOIPET were encompassed in the VOIT2, respectively 99.6% and 97.5%. The median tumor overlap was 94.4% (VOIDWI within VOIPET). The median overlap of cluster 2 in VOIPET and VOIDWI was 43.61% (27.67–52.66%) and 21.86%(10.47–40.89%), respectively. The median overlap of cluster 1 in VOIPET and VOIDWI was 48.03% (23.91–63.15%) and 24.40% (7.44–51.44%), respectively. Separation between clusters appeared to be defined by a SUV value. Conclusion For NPC, the VOIs of DWI and FDG PET were not overlapped completely and the volume defined by cluster-analysis might be meaningful for DP.

Accurate and reliable quantification of brain metabolites measured in vivo using 1H magnetic resonance spectroscopy (MRS) is a topic of continued interest. Aside from differences in the basic approach to quantification, the quantification of metabolite data acquired at different sites and on different platforms poses an additional methodological challenge. In this study, spectrally edited γ-aminobutyric acid (GABA) MRS data were analyzed and GABA levels were quantified relative to an internal tissue water reference. Data from 284 volunteers scanned across 25 research sites were collected using GABA+ (GABA + co-edited macromolecules (MM)) and MM-suppressed GABA editing. The unsuppressed water signal from the volume of interest was acquired for concentration referencing. Whole-brain T1-weighted structural images were acquired and segmented to determine gray matter, white matter and cerebrospinal fluid voxel tissue fractions. Water-referenced GABA measurements were fully corrected for tissue-dependent signal relaxation and water visibility effects. The cohort-wide coefficient of variation was 17% for the GABA + data and 29% for the MM-suppressed GABA data. The mean within-site coefficient of variation was 10% for the GABA + data and 19% for the MM-suppressed GABA data. Vendor differences contributed 53% to the total variance in the GABA + data, while the remaining variance was attributed to site- (11%) and participant-level (36%) effects. For the MM-suppressed data, 54% of the variance was attributed to site differences, while the remaining 46% was attributed to participant differences. Results from an exploratory analysis suggested that the vendor differences were related to the unsuppressed water signal acquisition. Discounting the observed vendor-specific effects, water-referenced GABA measurements exhibit similar levels of variance to creatine-referenced GABA measurements. It is concluded that quantification using internal tissue water referencing is a viable and reliable method for the quantification of in vivo GABA levels. ispartof: NEUROIMAGE vol:191 pages:537-548 ispartof: location:United States status: published

Due to bone deformation and joint space narrowing in diseased hips, accurate segmentation for pelvis, and femur from hip computed tomography (CT) images remains a challenging task. Therefore, the paper presents a fully automatic segmentation framework for the pelvis and femur in both of healthy and diseased hips. The framework involves three steps: preprocessing, coarse segmentation, and refinement. It starts with a preprocessing procedure to extract the volume of interest (VOI) from original CT images. Then, a coarse segmentation of bone has been obtained by classifying the VOI as bone and nonbone parts based on conditional random field (CRF) model. Finally, the bone is further divided into the pelvis and femur using a patch-based refinement method. The innovation of this study is the novel patch-based refinement method that is particularly suitable for diseased hips. The refinement method starts from the boundary of coarse segmentation, and propagates to the neighbors only when the label is not consistent with the label of CRF-based classification, it increases the reliability of segmentation for diseased hips with bone deformation. We incorporate neighborhood information to label fusion so that final label estimation is more accurate and robust for diseased hips with joint space narrowing. In total, 60 CT data sets, which included 78 healthy hemi-hips and 42 diseased hemi-hips, were used, and three-fold cross validations were carried out. Compared to two state-of-the-art methods, our method achieved significantly increased segmentation accuracy for the diseased hemi-hips, and is, therefore, more suited for automatic segmentation of diseased hips.

Objective The aim of this study was to determine which apparent diffusion coefficient-derived texture features are associated with malignancy in Bosniak IIF and III renal cystic lesions. Methods Twenty benign and 7 malignant Bosniak IIF (22) or III (5) renal cysts, as evaluated with magnetic resonance imaging, were assessed for progression to pathology-confirmed malignancy. Whole-cyst volumes of interest were manually segmented from apparent diffusion coefficient maps. Texture features were extracted from each volume of interest, including first-order histogram-based features and higher-order features, and data were analyzed with the Mann-Whitney U test to predict malignant progression. Results Eleven of 17 first-order features were significantly greater in benign compared with malignant cysts. Eight higher-order gray-level co-occurrence matrix (GLCM) texture features were significantly different between groups, 5 of which were greater in the benign population. Conclusions Apparent diffusion coefficient-derived texture measures may help differentiate between benign and malignant Bosniak IIF and III cysts.

Aim: To evaluate dynamic contrast-enhanced (DCE)-MRI and diffusion weighted (DW)-MRI diagnostic value to differentiate Warthin tumors (WT) by pleomorphic adenomas (PA). Materials & methods: Seven WT and seven PA were examined. DCE- and DW-MRI parameters were extracted from volumes of interest; volume of interest-based averages and standard deviations were calculated. Statistical analysis included: linear discriminant analysis, receiver operating characteristic curves, sensitivity and specificity. Results: No single feature was able to differentiate WT by PA (p > 0.05); linear discriminant analysis analysis showed that a combination of all features or combinations of feature pairs (namely: Ktrans(std) & f(std), Ktrans(std) & D(std), kep(std) & D(std), MRE(av) & TTP(av)) might achieve sensitivity (SENS), specificity (SPEC) = 100%, with a slight reduction after cross-validation analysis (SENS = 0.875; SPEC = 1). Conclusion: Although preliminary and not conclusive, our results suggest that differentiation between WT and PA is possible through a multiparametric approach based on combination of DCE- and DW-MRI parameters.

Objective This study proposes an automated classification of benign and malignant in highly integrated regions in bone single-photon emission computed tomography/computed tomography (SPECT/CT) using a three-dimensional deep convolutional neural network (3D-DCNN). Methods We examined 100 regions of 35 patients with bone SPECT/CT classified as benign and malignant by other examinations and follow-ups. First, SPECT and CT images were extracted at the same coordinates in a cube, with a long side two times the diameter of a high concentration in SPECT images. Next, we inputted the extracted image to DCNN and obtained the probability of benignity and malignancy. Integrating the output from DCNN of each SPECT and CT image provided the overall result. To validate the efficacy of the proposed method, the malignancy of all images was assessed using the leave-one-out cross-validation method; besides, the overall classification accuracy was evaluated. Furthermore, we compared the analysis results of SPECT/CT, SPECT alone, CT alone, and whole-body planar scintigraphy in the highly integrated region of the same site. Results The extracted volume of interest was 50 benign and malignant regions, respectively. The overall classification accuracy of SPECT alone and CT alone was 73% and 68%, respectively, while that of the whole-body planar analysis at the same site was 74%. When SPECT/CT images were used, the overall classification accuracy was the highest (80%), while the classification accuracy of malignant and benign was 82 and 78%, respectively. Conclusions This study suggests that DCNN could be used for the direct classification of benign and malignant regions without extracting the features of SPECT/CT accumulation patterns.

Clinical evaluation poorly predicts outcomes in lung cancer treated with immunotherapy. The aim of the study is to assess whether CT-derived texture parameters can predict overall survival (OS) and progression-free survival (PFS) in patients with advanced non-small-cell lung cancer (NSCLC) treated with first line Pembrolizumab. Twenty-one patients with NSLC were prospectively enrolled; they underwent contrast enhanced CT (CECT) at baseline and during Pembrolizumab treatment. Response to therapy was assessed both with clinical and iRECIST criteria. Two radiologists drew a volume of interest of the tumor at baseline CECT, extracting several texture parameters. ROC curves, a univariate Kaplan-Meyer analysis and Cox proportional analysis were performed to evaluate the prognostic value of texture analysis. Twelve (57%) patients showed partial response to therapy while nine (43%) had confirmed progressive disease. Among texture parameters, mean value of positive pixels (MPP) at fine and medium filters showed an AUC of 72% and 74% respectively (P MPP MPP4 and OS (P = 0.0038; HR = 0.89[CI 95%:0.83,0.96]). In conclusion, MPP could be used as predictive imaging biomarkers of OS and PFS in patients with NSLC with first line immune treatment.

Yanqing Ma,1 Xiren Xu,1 Peipei Pang,2 Yang Wen1 1Department of Radiology, Zhejiang Provincial People’s Hospital, People’s Hospital of Hangzhou Medical College, Hangzhou, 310000, People’s Republic of China; 2Department of Pharmaceuticals Diagnosis, GE Healthcare, Hangzhou, 310000, People’s Republic of ChinaCorrespondence: Yanqing MaDepartment of Radiology, Zhejiang Provincial People’s Hospital, People’s Hospital of Hangzhou Medical College, Hangzhou, 310000, People’s Republic of ChinaEmail 704180026@qq.comObjective: This study aimed to evaluate the role of tumor and mini-peritumor in the context of CT-based radiomics analysis to differentiate fat-poor angiomyolipoma (fp-AML) from clear cell renal cell carcinoma (ccRCC).Methods: A total of 58 fp-AMLs and 172 ccRCCs were enrolled. The volume of interest (VOI) was manually delineated in the standardized CT images and radiomics features were automatically calculated with software. After methods of feature selection, the CT-based logistic models including tumoral model (Ra-tumor), mini-peritumoral model (Ra-peritumor), perirenal model (Ra-Pr), perifat model (Ra-Pf), and tumoral+perirenal model (Ra-tumor+Pr) were constructed. The area under curves (AUCs) were calculated by DeLong test to evaluate the efficiency of logistic models.Results: The AUCs of Ra-peritumor of nephrographic phase (NP) were slightly higher than those of corticomedullary phase (CMP). Furthermore, the Ra-Pr showed significant higher efficiency than the Ra-Pf, and relative more optimal radiomics features were selected in the Ra-Pr than Ra-Pf. The Ra-tumor+Pr combined tumoral and perirenal radiomics analysis was of most significant in distinction compared with Ra-tumor and Ra-peritumor.Conclusion: The validity of NP to differentiate fp-AML from ccRCC was slightly higher than that of CMP. To the NP analysis, the Ra-Pr was superior to the Ra-Pf in distinction, and the lesions invaded to the perirenal tissue more severely than to the perifat tissue. It is important to the individual therapeutic surgeries according to the different lesion location. The pooled tumoral and perirenal radiomics analysis was the most promising approach in distinguishing fp-AML and ccRCC.Keywords: computed tomography, angiomyolipoma, clear cell renal cell carcinoma, peritumor, radiomics

A two-step concept for 3D segmentation on 5 abdominal organs inside volumetric CT images is presented. Firsteach relevant organ’s volume of interest is extracted as bounding box. The extracted volume acts as input for asecond stage, wherein two compared U-Nets with different architectural dimensions re-construct an organ segmen-tation as label mask. In this work, we focus on comparing 2D U-Nets vs. 3D U-Net counterparts. Our initial resultsindicate Dice improvements of about 6% at maximum. In this study to our surprise, liver and kidneys for instancewere tackled significantly better using the faster and GPU-memory saving 2D U-Nets. For other abdominal keyorgans, there were no significant differences, but we observe highly significant advantages for the 2D U-Net interms of GPU computational efforts for all organs under study.

Contains fulltext : 238088.pdf (Publisher’s version ) (Open Access) BACKGROUND: Central necrosis can be detected on [(18)F]FDG PET/CT as a region with little to no tracer uptake. Currently, there is no consensus regarding the inclusion of regions of central necrosis during volume of interest (VOI) delineation for radiomic analysis. The aim of this study was to assess how central necrosis affects radiomic analysis in PET. METHODS: Forty-three patients, either with non-small cell lung carcinomas (NSCLC, n = 12) or with pheochromocytomas or paragangliomas (PPGL, n = 31), were included retrospectively. VOIs were delineated with and without central necrosis. From all VOIs, 105 radiomic features were extracted. Differences in radiomic features between delineation methods were assessed using a paired t-test with Benjamini-Hochberg multiple testing correction. In the PPGL cohort, performances of the radiomic models to predict the noradrenergic biochemical profile were assessed by comparing the areas under the receiver operating characteristic curve (AUC) for both delineation methods. RESULTS: At least 65% of the features showed significant differences between VOI(vital-tumour) and VOI(gross-tumour) (65%, 79% and 82% for the NSCLC, PPGL and combined cohort, respectively). The AUCs of the radiomic models were not significantly different between delineation methods. CONCLUSION: In both tumour types, almost two-third of the features were affected, demonstrating that the impact of whether or not to include central necrosis in the VOI on the radiomic feature values is significant. Nevertheless, predictive performances of both delineation methods were comparable. We recommend that radiomic studies should report whether or not central necrosis was included during delineation.

Computer-aided diagnosis of polyps has played an important role in advancing the screening capability of computed tomographic colonography (CTC). Texture features, including intensity, gradient, and curvature, are the essential components in differentiation of neoplastic and nonneoplastic polyps. Clinical study has shown that the malignancy rates of polyps increase in correlation to their size. In this chapter, we present a study to investigate the effect of separating polyps based on size on the performance of machine learning. First, the volume of interest of each polyp was extracted and further confirmed by Radiologists. All polyp masses in this study have a diameter size ranging from 6 to 30 mm. Then, we group polyps into three groups based on their sizes: 6–9 mm, 10–30 mm, and a combined group of 6–30 mm. The corresponding malignancy risks of polyps were also recorded. From each polyp volume, we extracted the traditional 14 Haralick texture features plus 16 additional features with a total 30 texture features. Those features were further grouped as descriptors for intensity, gradient, and curvature characteristics. Finally, we employed the Random Forest classifier to differentiate neoplastic and nonneoplastic polyps. The proposed texture feature analysis was studied using 228 polyp masses. We generated a measure of area under the curve (AUC) values of the receiver operating characteristics (ROC) curve. The performance was determined by assessing the sensitivity and specificity values. Experimental results demonstrated that gradient and curvature features were ideal for differentiation of the malignancy risk for medium-sized polyps, whereas intensity feature was better for smaller-sized polyps.

An effective strategic communication management model should address two aspects: communication within and across MOOCs, taking into account the heterogeneity of audiences and their needs; and communication about MOOCs and their platforms. We have conducted a content analysis of the websites and the presence on social networks of a convenience sample of platforms for massive open online courses: Udacity, Coursera, EdX, and ECO. The variables considered are: graphic development; the website in its different language versions; the public descriptor; the mission stated; the volume of interest triggered (Google Trends); and the digital spectrum based on the presence of the MOOCs on the social network Instagram according to the Deep Social database. This review of the brand spectrum of each platform, and the specific approach to communication, especially given the characteristics of MOOCs as a product, offers us a holistic view of this educational reality.

The cingulate island sign (CIS) on fludeoxyglucose (FDG)-positron emission tomography (PET) is a supporting biomarker of dementia with Lewy bodies (DLB). Its diagnostic accuracy has only been investigated in FDG-PET, however. The present prospective study compared the CIS on I-iodoamphetamine-single photon emission computed tomography (SPECT) among patients with mild cognitive impairment (MCI), AD, or DLB. Fifty-eight patients with MCI, 42 with probable AD, and 58 with probable DLB were enrolled. The “CIScore” used to evaluate the CIS was defined as the ratio of volume of interest (VOI)-1 (indicating posterior cingulate gyrus [PCG]) to VOI-2 (area of significantly reduced regional cerebral blood perfusion [rCBF] in DLB patients compared with in healthy controls). It was calculated using eZIS software. The CIScore for MCI, DLB, and AD was 0.22, 0.23, and 0.28, respectively. The CIScore in the AD group was significantly higher than that in the DLB or MCI groups (AD vs. DLB: p < 0.001, AD vs. MCI: p < 0.005). This suggests that the CIScore can discriminate DLB from AD, if the decrease in rCBF in the PCG is similar between them. We believe that it is difficult to identify MCI based on the CIScore, as the decrease in rCBF in the PCG is not severe. The diagnostic accuracy of the CIScore may be low as it often shows an increase in elderly DLB patients, in whom the pathologically common form is most prevalent (1). Further study should include assessment of multiple components such as symptom classification and age.

Advances in iterative image reconstruction enable absolute quantification of SPECT/CT studies by incorporating compensations for collimator–detector response, attenuation, and scatter. This study aimed to assess the quantitative accuracy of SPECT/CT based on different levels of (99m)Tc activity (low/high) using different SUV metrics (SUV(mean), SUV(max), SUV(0.6 max), and SUV(0.75 max) [the average values that include pixels greater than 60% and 75% of the SUV(max) in the volume of interest, respectively]). Methods: A Jaszczak phantom equipped with 6 fillable spheres was set up with low and high activity ratios of 1:4 and 1:10 (background-to-sphere) on background activities of 10 and 60 kBq/mL, respectively. The fixed-size volume of interest based on the diameter of each sphere was drawn on SPECT images using various metrics for SUV quantification purposes. Results: The convergence of activity concentration was dependent on the number of iterations and application of postfiltering. For the background-to-sphere ratio of 1:10 with a low background activity concentration, the SUV(mean) metric showed an underestimation of about 38% from the actual SUV, and SUV(max) exhibited an overestimation of about 24% for the largest sphere diameter. Meanwhile, bias reductions of as much as −6% and −7% for SUV(0.6 max) and SUV(0.75 max), respectively, were observed. SUV(max) gave a more accurate reading than the others, although points that exceeded the actual value were detected. At 1:4 and 1:10 background activity of 10 kBq/mL, a low activity concentration attained a value close to the actual ratio. Use of 2 iterations and 10 subsets without postfiltering gave the most accurate values for reconstruction and the best image overall. Conclusion: SUV(max) is the best metric in a high- or low-contrast-ratio phantom with at least 2 iterations, 10 subsets, and no postfiltering.

BackgroundThe clinicopathological classification of breast cancer is proposed according to therapeutic purposes. It is simplified and can be conducted easily in clinical practice, and this subtyping undoubtedly contributes to the treatment selection of breast cancer. This study aims to investigate the feasibility of using a Fisher discriminant analysis model based on radiomic features of diffusion-weighted MRI for predicting the clinicopathological subtypes of breast cancer.MethodsPatients who underwent breast magnetic resonance imaging were confirmed by retrieving data from our institutional picture archiving and communication system (PACS) between March 2013 and September 2017. Five clinicopathological subtypes were determined based on the status of ER, PR, HER2 and Ki-67 from the immunohistochemical test. The radiomic features of diffusion-weighted imaging were derived from the volume of interest (VOI) of each tumour. Fisher discriminant analysis was performed for clinicopathological subtyping by using a backward selection method. To evaluate the diagnostic performance of the radiomic features, ROC analyses were performed to differentiate between immunohistochemical biomarker-positive and -negative groups.ResultsA total of 84 radiomic features of four statistical methods were included after preprocessing. The overall accuracy for predicting the clinicopathological subtypes was 96.4% by Fisher discriminant analysis, and the weighted accuracy was 96.6%. For predicting diverse clinicopathological subtypes, the prediction accuracies ranged from 92 to 100%. According to the cross-validation, the overall accuracy of the model was 82.1%, and the accuracies of the model for predicting the luminal A, luminal BHER2-, luminal BHER2+, HER2 positive and triple negative subtypes were 79, 77, 88, 92 and 73%, respectively. According to the ROC analysis, the radiomic features had excellent performance in differentiating between different statuses of ER, PR, HER2 and Ki-67.ConclusionsThe Fisher discriminant analysis model based on radiomic features of diffusion-weighted MRI is a reliable method for the prediction of clinicopathological breast cancer subtypes.

Background As the rupture of cerebral aneurysm may lead to fatal results, early detection of unruptured aneurysms may save lives. At present, the contrast-unenhanced time-of-flight magnetic resonance angiography is one of the most commonly used methods for screening aneurysms. The computer-assisted detection system for cerebral aneurysms can help clinicians improve the accuracy of aneurysm diagnosis. As fully convolutional network could classify the image pixel-wise, its three-dimensional implementation is highly suitable for the classification of the vascular structure. However, because the volume of blood vessels in the image is relatively small, 3D convolutional neural network does not work well for blood vessels. Results The presented study developed a computer-assisted detection system for cerebral aneurysms in the contrast-unenhanced time-of-flight magnetic resonance angiography image. The system first extracts the volume of interest with a fully automatic vessel segmentation algorithm, then uses 3D-UNet-based fully convolutional network to detect the aneurysm areas. A total of 131 magnetic resonance angiography image data are used in this study, among which 76 are training sets, 20 are internal test sets and 35 are external test sets. The presented system obtained 94.4% sensitivity in the fivefold cross-validation of the internal test sets and obtained 82.9% sensitivity with 0.86 false positive/case in the detection of the external test sets. Conclusions The proposed computer-assisted detection system can automatically detect the suspected aneurysm areas in contrast-unenhanced time-of-flight magnetic resonance angiography images. It can be used for aneurysm screening in the daily physical examination.

Two new contact circumstances called ‘stand-lie’ and ‘front–rear’ are implemented to the merged phantom tool. To allow more flexibility for users when they calculate the dose for a volume of interest (VOI) with arbitrary geometry, an optional utility to convert segmented structure information from the CARIMAS software into parallel geometry of GEANT4 is provided. The effective dose for a person who has been in contact with a male patient being treated for thyroid cancer with 131I is calculated for four circumstances: opposite, side by side, stand-lie and front–rear. The biggest dose is the ‘opposite’ circumstance and the smallest one is the ‘stand-lie’ circumstance. Using the dose distribution in the patient’s body and applying the right circumstance should be done to optimise the dose calculation for the contact person.

It is well understood that the level of molecular oxygen (O2) in tissue is a very important factor impacting both physiology and pathological processes as well as responsiveness to some treatments. Data on O2 in tissue could be effectively utilized to enhance precision medicine. However, the nature of the data that can be obtained using existing clinically applicable techniques is often misunderstood, and this can confound the effective use of the information. Attempts to make clinical measurements of O2 in tissues will inevitably provide data that are aggregated over time and space and therefore will not fully represent the inherent heterogeneity of O2 in tissues. Additionally, the nature of existing techniques to measure O2 may result in uneven sampling of the volume of interest and therefore may not provide accurate information on the “average” O2 in the measured volume. By recognizing the potential limitations of the O2 measurements, one can focus on the important and useful information that can be obtained from these techniques. The most valuable clinical characterizations of oxygen are likely to be derived from a series of measurements that provide data about factors that can change levels of O2, which then can be exploited both diagnostically and therapeutically. The clinical utility of such data ultimately needs to be verified by careful studies of outcomes related to the measured changes in levels of O2.

Objective This study aimed to use quantitative values, calculated from bone single photon emission computed tomography (SPECT) imaging, to estimate the reliability of progression evaluation for anti-resorptive agent-related osteonecrosis of the jaw (ARONJ). Methods The study population consisted of 21 patients (23 lesions), clinically diagnosed with mandibular ARONJ, who underwent SPECT/CT scanning. Diagnosis and staging of ARONJ were performed according to the American Association of Oral and Maxillofacial Surgeons (AAOMS) definition and the recommendations of the International Task Force on ONJ. Hybrid SPECT/CT imaging quantitative analyses were performed on a workstation. Each volume of interest (VOI) was semi-automatically placed over a lesion with areas of high tracer accumulation, using the GI-BONE® software default threshold method settings. Additionally, control VOI was manually set over an unaffected area. Measured parameters included standardized uptake values (SUV)—maximum (SUVmax) and mean (SUVmean), metabolic bone volume (MBV)—the total volume above the threshold, and total bone uptake (TBU) as calculated by MBV × SUVmean. We also calculated the SUV ratio (rSUV) between the lesion and control area, factoring for differences in individual bone metabolism; the ratios were termed rSUVmax and rSUVmean, accordingly. The product of multiplying the rSUVmean by MBV of a lesion was defined as the ratio of TBU (rTBU). Quantitative values were compared between clinical stages by the Kruskal–Wallis test and subsequent post hoc analysis. Results MBVs (cm3) were: median, [IQR] Stage 1, 8.28 [5.62–9.49]; Stage 2, 15.28 [10.64–24.78]; and Stage 3, 34.61 [29.50–40.78]. MBV tended to increase with stage increase. Furthermore, only MBV showed a significant difference between clinical stages (p p = 0.12) but a significant difference between stages 2 and 3 (p = 0.048). rSUVmax and rTBU tended to increase with stage increase, but the differences between the stages were not significant (p = 0.10 and p = 0.055, respectively). Conclusion MBV, which includes the concept of volume, showed significant differences between clinical stages and tended to increase with the stage increase. As an objective and reliable indicator, MBV might be an adjunct diagnostic method for staging ARONJ.

Background Brain F-FDG uptake reportedly starts to decline more than 10 years before the onset of cognitive decline in dominantly inherited Alzheimer disease (AD). We compared longitudinal F-FDG images in sporadic AD to aging data from a large sample size to expand the current knowledge of F-FDG reduction for AD progression. Methods Participants comprised 2 individuals (subjects A and B at ages 65 and 68 years, respectively) and 107 control subjects (67.9 [SD, 4.9] years). Subject A underwent F-FDG PET a total of 8 times over 9 years from the preclinical to early dementia stages. Subject B underwent F-FDG PET a total of 11 times over 12 years from the preclinical to mild cognitive impairment stages. Control subjects underwent F-FDG PET twice over a mean follow-up period of 7.8 years. After placing the volume of interest on the AD-related hypometabolic regions, the longitudinal F-FDG images were compared among the subjects and control subjects. Results For the control group, the rate of F-FDG reduction was 2.2% per decade (ie, aging effects). The rates of F-FDG reduction were 9.41% over 9 years and 9.07% over 12 years in subjects A and B, respectively. We estimated that F-FDG uptake started to decrease 4 and 2 years before indications of memory loss in subjects A and B, respectively. Conclusions The present study suggests that the time between the beginning of F-FDG reduction and the onset of cognitive decline may be shorter in elderly individuals with AD compared with the recently estimated period in dominantly inherited AD.

Light-field fluorescence microscopy uniquely provides fast, synchronous volumetric imaging by capturing an extended volume in one snapshot, but often suffers from low contrast due to the background signal generated by its wide-field illumination strategy. We implemented light-field-based selective volume illumination microscopy (SVIM), where illumination is confined to only the volume of interest, removing the background generated from the extraneous sample volume, and dramatically enhancing the image contrast. We demonstrate the capabilities of SVIM by capturing cellular-resolution 3D movies of flowing bacteria in seawater as they colonize their squid symbiotic partner, as well as of the beating heart and brain-wide neural activity in larval zebrafish. These applications demonstrate the breadth of imaging applications that we envision SVIM will enable, in capturing tissue-scale 3D dynamic biological systems at single-cell resolution, fast volumetric rates, and high contrast to reveal the underlying biology., Thai Truong et al. present light-field-based selective volume illumination microscopy (SVIM), a method for enhancing image contrast and resolution by combining light-field microscopy and selective plane illumination microscopy. They generate cellular-resolution 3D movies by applying SVIM to flowing bacteria in seawater and to the beating heart and whole brain of larval zebrafish.

Introduction Several studies investigated the changes in diffusion of water molecules in skeletal muscle cells of lifestyle-related-disease patients who performed a hybrid training (HYBT) for six months. They reported that the apparent diffusion coefficient (ADC) and all diffusion eigenvalues (λ1, λ2, and λ3) increased after the HYBT, owing to the enlargement of the intramyocellular diffusion space (intracellular space) caused by the muscular hypertrophy. We assumed that the HYBT promoted metabolism of the whole skeletal muscle including lipids, which reduced the amount of intramyocellular lipid (IMCL), and led to a secondary enlargement of the diffusion space in the skeletal muscle cells. However, the IMCL has to be a diffusion limiting factor in order to verify this hypothesis. Until now, there is no report on whether IMCL is a diffusion limiting factor for water molecules. The objective of this study was to examine whether the IMCL is a diffusion limiting factor in skeletal muscle cells. Materials and methods We performed a three-dimensional quantification of the IMCL in triceps surae muscles of lifestyle-related-disease patients and healthy volunteers. In addition, we measured the ADC in the volume of interest (VOI), diffusion anisotropy (FA), and diffusion eigenvalues (λ1, λ2, and λ3), and evaluated the correlations between these diffusion parameters and IMCL. Results The results showed that the amount of IMCL was positively and negatively correlated with the FA and λ3, respectively, in lifestyle-related-disease patients. In addition, there was a weak negative correlation between IMCL and ADC, λ1, and λ2. There was no correlation between the amount of IMCL and diffusion parameters of healthy volunteers. Discussion Above a certain amount, the IMCL correlates with the diffusion parameters. A higher amount of IMCL leads to smaller diffusion eigenvalues. This result suggested that IMCL possibility of influencing diffusion of water molecules in skeletal muscle cells. However, in order for the influence of IMCL to be reflected in the diffusion eigenvalues, it was needed large amount of IMCL existed, and we thought that the influence was smaller than the influence by the already reported cell membrane.

Agradecimentos: The Nuclear and Energy Research Institute (IPEN-CNEN), Sao Paulo, Brazil, kindly supplied the radiopharmaceuticals used in the present project (IPEN/UNICAMP agreement 01342000458/2017-15). Financial support was provided by FAPESP (Fundacao de Amparo a Pesquisa do Estado de Sao Paulo, grants 2009/54065-0 and 2018/00654-4). Celso Ramos has a research grant from CNPq (National Council of Research, proc 311841/2018-0). Irene Metze has a research grant from CNPq (National Council of Research, proc 305110/2018-7). No other potential conflict of interest relevant to this article was reported Abstract: Quantification of metabolic tumor volume (MTV) and total lesion glycolysis (TLG) can be time-consuming. We evaluated the performance of an automatic multifocal segmentation (MFS) method of quantification in patients with different stages of Hodgkin lymphoma, using the multiple VOI (MV) method as reference. Methods: This prospective bicentric study included 50 patients with Hodgkin lymphoma who underwent staging F-18-FGD PET/CT. The examinations were centrally reviewed and processed with commercial MFS software to obtain MTV and TLG using 2 fixed relative thresholds (40% and 20% of SUV max ) for each lesion. All PET/CT scans were processed using the MV and MFS methods. Interclass correlation coefficients and Bland-Altman plots were used for statistical analysis. Repeated calculations of MW and TLG values by 2 observers with different degrees of PET/CT imaging experience were used to ascertain interobserver agreement on the MFS method. Results: The means and SDs obtained for the MW with MV and MFS were, respectively, 736 +/- 856 mL and 660 +/- 699 mL for the 20% threshold and 313 +/- 359 mL and 372 +/- 434 mL for the 40% threshold. The time spent calculating the MW was much shorter with the MFS method than with the MV method (median time, 11.6 min [range, 1-30 min] and 64.4 min [range, 1-240 min], respectively), especially in patients with advanced disease. Time spent was similar in patients with localized disease. There were no statistical differences between the MFS values obtained by the 2 different observers. Conclusion: MTV and TLG calculations using MFS are reproducible, generate similar results to those obtained with MV, and are much less timing-consuming. Main differences between the 2 methods were related to difficulties in avoiding overlay of VOIs in the MV technique. MV and MFS perform equally well in patients with a small number of lesions FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQ Fechado

Objective To calculate the specific binding ratio (SBR) appropriately in dopamine transporter (DAT) imaging, a method for extracting the striatal volume of interest (VOI) was developed. Methods This study included 200 patients (72 ± 10 years) who were suspected of parkinsonian syndromes (PS) or dementia with Lewy body (DLB). The patients were divided into three groups of PS with dopaminergic degeneration, DLB and non-PS after [123I]ioflupane (FP-CIT) SPECT and clinical follow-up. The image data were reconstructed with CT attenuation correction and scatter correction, and with only CT attenuation correction (CTAC). The new method extracted striatal VOI according to the high-level counts and the average striatum volume, and calculated SBR using the reference occipital counts. The SBR values for each patient were obtained using the Tossici-Bolt method (SBRBolt) and our method. Reproducibility of SBR calculation using our method was compared by two operators. Results The mean SBR values for the PS and DLB groups were significantly different from that of the non-PS group with both methods. The coefficients of variation of the SBR were significantly smaller with the proposed method compared with those of SBRBolt (p

Background The medical imaging to differentiate World Health Organization (WHO) grade II (ODG2) from III (ODG3) oligodendrogliomas still remains a challenge. We investigated whether combination of machine leaning with radiomics from conventional T1 contrast-enhanced (T1 CE) and fluid attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) offered superior efficacy. Methods Thirty-six patients with histologically confirmed ODGs underwent T1 CE and 33 of them underwent FLAIR MR examination before any intervention from January 2015 to July 2017 were retrospectively recruited in the current study. The volume of interest (VOI) covering the whole tumor enhancement were manually drawn on the T1 CE and FLAIR slice by slice using ITK-SNAP and a total of 1072 features were extracted from the VOI using 3-D slicer software. Random forest (RF) algorithm was applied to differentiate ODG2 from ODG3 and the efficacy was tested with 5-fold cross validation. The diagnostic efficacy of radiomics-based machine learning and radiologist’s assessment were also compared. Results Nineteen ODG2 and 17 ODG3 were included in this study and ODG3 tended to present with prominent necrosis and nodular/ring-like enhancement (P Conclusions Machine-learning based on radiomics of T1 CE and FLAIR offered superior efficacy to that of radiologists in differentiating ODG2 from ODG3.

Background: This study explores different approaches for estimating the clearance rate of reference tissue (k2') parameter, used for pharmacokinetic modelling using the simplified reference tissue model 2 (SRMT2) and its effect on the binding potential (BPnd) of 11C-labelled Pittsburgh Compound B (PIB) PET scans. Methods: Thirty subjects underwent a dynamic PIB PET scan and were classified as PIB positive (+) or negative (-). Thirteen regions were defined from where to estimate k2': whole masked imagebrain, eight anatomical based on the Hammer’s atlas, one SPM voxel-based comparison between groups, and three using different BPnd(SRTM) thresholds. Results: The different approaches resulted in distinct k2' estimations. The median value of the estimated k2' across all subjects was of 0.057. In general, PIB+ subjects presented smaller k2' estimates than this median, and PIB-, larger. Furthermore, only grey matter and SPMthreshold and white matter methods resulted in non-significant bigger differences between groups in the k2' estimations. A sensitivity analysis was done by fixing k2' to a range of values and assessing the effects on BPnd(SRTM2), and a non-linear relationship between the parameters was observed. Furthermore, Tthreshold approaches yielded the best correlation between BPnd(SRTM) and BPnd(SRTM2) for both groups (R²=0.85 for PIB+, and R²=0.88 for PIB-). Lastly, sensitivity analysis shows that overestimating k2' values resulted in less bias in the BPnd(SRTM2) estimates. Conclusion: Results suggested that setting a threshold on BPnd(SRTM) might be the best method for estimating k2' in voxel-based modelling approaches, while the use of a white matter region of interest might be a better option for volume of interest-based analysis.

Purpose Recently, updated EARL specifications (EARL2) have been developed and announced. This study aims at investigating the impact of the EARL2 specifications on the quantitative reads of clinical PET–CT studies and testing a method to enable the use of the EARL2 standards whilst still generating quantitative reads compliant with current EARL standards (EARL1). Methods Thirteen non-small cell lung cancer (NSCLC) and seventeen lymphoma PET–CT studies were used to derive four image datasets—the first dataset complying with EARL1 specifications and the second reconstructed using parameters as described in EARL2. For the third (EARL2F6) and fourth (EARL2F7) dataset in EARL2, respectively, 6 mm and 7 mm Gaussian post-filtering was applied. We compared the results of quantitative metrics (MATV, SUVmax, SUVpeak, SUVmean, TLG, and tumor-to-liver and tumor-to-blood pool ratios) obtained with these 4 datasets in 55 suspected malignant lesions using three commonly used segmentation/volume of interest (VOI) methods (MAX41, A50P, SUV4). Results We found that with EARL2 MAX41 VOI method, MATV decreases by 22%, TLG remains unchanged and SUV values increase by 23–30% depending on the specific metric used. The EARL2F7 dataset produced quantitative metrics best aligning with EARL1, with no significant differences between most of the datasets (p>0.05). Different VOI methods performed similarly with regard to SUV metrics but differences in MATV as well as TLG were observed. No significant difference between NSCLC and lymphoma cancer types was observed. Conclusions Application of EARL2 standards can result in higher SUVs, reduced MATV and slightly changed TLG values relative to EARL1. Applying a Gaussian filter to PET images reconstructed using EARL2 parameters successfully yielded EARL1 compliant data.

PurposeWe present a modified version of the SISCOM procedure that uses interictal PET instead of interictal SPECT for seizure onset zone localization. We called this new nuclear imaging processing technique PISCOM (PET interictal subtracted ictal SPECT coregistered with MRI).MethodsWe retrospectively studied 23 patients (age range 4-61years) with medically refractory epilepsy who had undergone MRI, ictal SPECT, interictal SPECT and interictal FDG PET and who had been seizure-free for at least 2years after surgical treatment. FDG PET images were reprocessed (rFDG PET) to assimilate SPECT features for image subtraction. Interictal SPECT and rFDG PET were compared using statistical parametric mapping (SPM). PISCOM and SISCOM images were evaluated visually and using an automated volume of interest-based analysis. The results of the two studies were compared with each other and with the known surgical resection site.ResultsSPM showed no significant differences in cortical activity between SPECT and rFDG PET images. PISCOM and SISCOM showed equivalent results in 17 of 23 patients (74%). The seizure onset zone was successfully identified in 19 patients (83%) by PISCOM and in 17 (74%) by SISCOM: in 15 patients (65%) the two techniques showed concordant successful results. The volume of interest-based analysis showed no significant differences between PISCOM and SISCOM in identifying the extension of the seizure onset zone. However, PISCOM showed a lower amount of indeterminate activity due to propagation, background or artefacts.ConclusionPreliminary findings of this initial proof-of-concept study suggest that perfusion and glucose metabolism in the cerebral cortex can be correlated and that PISCOM may be a valid technique for identification of the seizure onset zone. However, further studies are needed to validate these results., This work was supported by AGAUR (Agencia de Gestio d'Ajuts Universitaris I de Recerca) 2014 SGR 279 grants.