7 May 2020 , Volume 96, Number 3

7 May 2020 , Volume 96, Number 3

7 May 2020 , Volume 96, Number 3

Objectives: The detection of cancer micrometastasis for early diagnosis and treatment poses a great challenge for conventional imaging techniques. The aim of study is to evaluate the performance of photoacoustic imaging (PAI) in detecting hepatic micrometastases from melanoma in a very early stage and perform tumor resection by intraoperative photoacoustic image-guidance. Methods: In vivo studies were performed by following protocols approved by the Ethical Committee for Animal Research at Xiamen University. First, a B16 melanoma hepatic metastasis mouse model (n = 10) was established to study the development of micrometastases in vivo. Next, the hepatic metastasis mice models were imaged by scalable PAI instrument, ultrasound, 9.4 T high-resolution magnetic resonance imaging (MRI), positron emission tomography/computed tomography (PET/CT), and bioluminescence imaging. Photoacoustic images acquired with optical wavelengths spanning from 680 to 850 nm were spectrally unmixed by using a linear least-squares method to differentiate various components. Differences in the signal-to-background ratios among different modalities were determined with the two-tailed paired t test. The diagnosis results were assessed with histologic examinations. Excised liver samples from patients diagnosed with hepatic cancer were also examined to identify tumor boundary. In vivo metastatic melanoma removal in surgery was precisely guided by the portable PAI system. Results: PAI achieved as small as ~400 µm hepatic melanoma detection at a depth up to 7 mm in vivo, which could early detect small melanoma compared with ultrasound and MRI in mouse models. The signal ratio of tumor-to-liver acquired with PAI in micrometastases at 8 days (4.2 ± 0.2, n = 6) and 14 days (9.2 ± 0.4, n = 5) were significantly higher than those obtained with PET/CT (1.8 ± 0.1, n = 5 and 4.5 ± 0.2, n = 5, P <0.001 for both). Functional PAI provided dynamic oxygen saturation changes during tumor growth. The limit of detection was measured to be approximately 219 cells per microliter in vitro. We successfully performed intraoperative photoacoustic image-guided surgery in vivo using the rapid portable PAI system. Conclusion: Our findings offer a rapid and effective tool to noninvasively detect micrometastases and guide intraoperative resection as a complementary clinical imaging application.

Background: Accurate definition of the extent and severity of left-ventricular assist device (LVAD) infection may facilitate therapeutic decision making and targeted surgical intervention. Here, we explore the value of ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) for guidance of patient management. Methods: Fifty-seven LVAD-carrying patients received 85 whole-body ¹⁸F-FDG PET/CT scans for the work-up of device infection. Clinical follow-up was obtained over a period of up to two years. Results: PET/CT showed various patterns of infectious involvement of the 4 LVAD components: driveline entry point (77% of cases), subcutaneous driveline path (87%), pump pocket (49%) and outflow tract (58%). Driveline smears revealed staphylococcus or pseudomonas strains as the underlying pathogen in a majority of cases (48 and 34%, respectively). At receiver-operating characteristics analysis, an ¹⁸F-FDG standardized uptake value (SUV) >2.5 was most accurate to identify smear-positive driveline infection. Infection of 3 or all 4 LVAD components showed a trend towards lower survival vs infection of 2 or less components (P = 0.089), while involvement of thoracic lymph nodes was significantly associated with adverse outcome (P = 0.001 for nodal SUV above vs below median). Finally, patients that underwent early surgical revision within 3 months after PET/CT (n = 21) required significantly less inpatient hospital care during follow-up when compared to those receiving delayed surgical revision (n = 11; p<0.05). Conclusion: Whole-body ¹⁸F-FDG PET/CT identifies the extent of LVAD infection and predicts adverse outcome. Initial experience suggests that early image-guided surgical intervention may facilitate a less complicated subsequent course.

Purpose: To assess the performance of full dose (FD) positron emission tomography (PET) image synthesis in both image and projection space from low-dose (LD) PET images/sinograms without sacrificing diagnostic quality using deep learning techniques. Methods: Clinical brain PET/CT studies of 140 patients were retrospectively employed for LD to FD PET conversion. 5% of the events were randomly selected from the FD list-mode PET data to simulate a realistic LD acquisition. A modified 3D U-Net model was implemented to predict FD sinograms in the projection-space (PSS) and FD images in image-space (PIS) from their corresponding LD sinograms/images, respectively. The quality of the predicted PET images was assessed by two nuclear medicine specialists using a five-point grading scheme. Quantitative analysis using established metrics including the peak signal-to-noise ratio (PSNR), structural similarity index metric (SSIM), region-wise standardized uptake value (SUV) bias, as well as first-, second- and high-order texture radiomic features in 83 brain regions for the test and evaluation dataset was also performed. Results: All PSS images were scored 4 or higher (good to excellent) by the nuclear medicine specialists. PSNR and SSIM values of 0.96 ± 0.03, 0.97 ± 0.02 and 31.70 ± 0.75, 37.30 ± 0.71 were obtained for PIS and PSS, respectively. The average SUV bias calculated over all brain regions was 0.24 ± 0.96% and 1.05 ± 1.44% for PSS and PIS, respectively. The Bland-Altman plots reported the lowest SUV bias (0.02) and variance (95% CI: -0.92, +0.84) for PSS compared with the reference FD images. The relative error of the homogeneity radiomic feature belonging to the Grey Level Co-occurrence Matrix category was -1.07 ± 1.77 and 0.28 ± 1.4 for PIS and PSS, respectively Conclusion: The qualitative assessment and quantitative analysis demonstrated that the FD PET prediction in projection space led to superior performance, resulting in higher image quality and lower SUV bias and variance compared to FD PET prediction in the image domain.

Respiratory motion during the CT and PET parts of a PET/CT scan leads to imperfect alignment of anatomical features seen by the two modalities. In this work, we concentrate on the effects of motion during CT. We propose a novel approach for improving the alignment. Methods: Respiratory waveform data were gathered during the CT and PET parts of 28 PET/CT scans of cancer patients with 40 lesions up to 3 cm size in the lung or upper abdomen. PET list-mode data were reconstructed by three reconstruction methods: PET/static, PET/EX or end of expiration (OncoFreeze), and a novel PET/matched method that used both waveforms. The three methods were compared. The distance between tumor positions in PET and CT were characterized in visual interpretation by physicians as well as quantitatively. Tumor standardized uptake values (SUV_max and SUVpeak) were determined relative to SUV based on the static method. Image noise was evaluated in the liver and compared to PET/static. Results: In visual interpretation, the rate of good alignment was 13/21, 13/23 and 18/21 for PET/static, PET/EX and PET/matched methods, respectively, and the mean PET-CT distances were 3.5, 5.1 and 2.8 mm. In visual comparison with PET/EX, the rate of good alignment was increased in 1/10 and 7/10 cases for PET/static and PET/matched. SUV_max was on average 21% higher than PET/static when either PET/EX or PET/matched was used. SUVpeak was 12% higher. Image noise in the liver was 15% higher than static for the PET/EX method, and 40% higher for PET/matched; that is, noise was much lower than in gated PET. Conclusion: Acquiring respiratory waveforms both in PET (as in the current state of the art) and in CT (an unusual key step in this approach) has the potential to improve the alignment of PET and CT images. A proposed method for using this information was tested. Improved alignment was demonstrated.

Gone are the days when medical imaging was used primarily to visualize anatomical structures. The emergence of molecular imaging, championed by radiolabeled fluorodeoxyglucose positron emission tomography (¹⁸FDG PET) has expanded the information content derived from imaging to include pathophysiological and molecular processes. Cancer imaging, in particular, has leveraged advances in molecular imaging agents and technology to improve the accuracy of tumor detection, interrogate tumor heterogeneity, monitor treatment response, focus surgical resection, and enable image-guided biopsy. Surgeons are actively latching on to the incredible opportunities provided by medical imaging for preoperative planning, intraoperative guidance, and postoperative monitoring. From label-free techniques to enabling cancer-selective imaging agents, image-guided surgery provides surgical oncologists and interventional radiologists both macroscopic and microscopic views of cancer in the operating room. This review highlights the current state of molecular imaging and sensing approaches available for surgical guidance. Salient features of nuclear, optical, and multimodal approaches will be discussed, including their strengths, limitations and clinical applications. To address the increasing complexity and diversity of methods available today, this review provides a framework to identify a contrast mechanism, suitable modality, and device. Emerging low cost, portable, and user-friendly imaging systems make the case for adopting some of these technologies as the global standard of care in surgical practice.

7 May 2020 , Volume 96, Number 3

7 May 2020 , Volume 96, Number 3

Stephen G. Young
May 1, 2020; 61:589-590
Editorials

7 May 2020 , Volume 96, Number 3

7 May 2020 , Volume 96, Number 3

1 June 2012 , Volume 68, Number 4

Eyes in the skies keeping watch on a planet under stress. Click on the PDF link to view the graphic

Motels -- Australia -- Pictorial works.

Feminism and art.

[London] : [publisher not identified], [2019]

Salem M. Al-Gezeri, Robert G. Aykroyd.

Source: Brazilian Journal of Probability and Statistics, Volume 33, Number 3, 498--519.

Abstract:
The use of Markov random field (MRF) models has proven to be a fruitful approach in a wide range of image processing applications. It allows local texture information to be incorporated in a systematic and unified way and allows statistical inference theory to be applied giving rise to novel output summaries and enhanced image interpretation. A great advantage of such low-level approaches is that they lead to flexible models, which can be applied to a wide range of imaging problems without the need for significant modification. This paper proposes and explores the use of conditional MRF models for situations where multiple images are to be processed simultaneously, or where only a single image is to be reconstructed and a sequential approach is taken. Although the coupling of image intensity values is a special case of our approach, the main extension over previous proposals is to allow the direct coupling of other properties, such as smoothness or texture. This is achieved using a local modulating function which adjusts the influence of global smoothing without the need for a fully inhomogeneous prior model. Several modulating functions are considered and a detailed simulation study, motivated by remote sensing applications in archaeological geophysics, of conditional reconstruction is presented. The results demonstrate that a substantial improvement in the quality of the image reconstruction, in terms of errors and residuals, can be achieved using this approach, especially at locations with rapid changes in the underlying intensity.

Methods for generating synthetic data have become of increasing importance to build large datasets required for Convolution Neural Networks (CNN) based deep learning techniques for a wide range of computer vision applications. In this work, we extend existing methodologies to show how 2D thermal facial data can be mapped to provide 3D facial models. For the proposed research work we have used tufts datasets for generating 3D varying face poses by using a single frontal face pose. The system works by refining the existing image quality by performing fusion based image preprocessing operations. The refined outputs have better contrast adjustments, decreased noise level and higher exposedness of the dark regions. It makes the facial landmarks and temperature patterns on the human face more discernible and visible when compared to original raw data. Different image quality metrics are used to compare the refined version of images with original images. In the next phase of the proposed study, the refined version of images is used to create 3D facial geometry structures by using Convolution Neural Networks (CNN). The generated outputs are then imported in blender software to finally extract the 3D thermal facial outputs of both males and females. The same technique is also used on our thermal face data acquired using prototype thermal camera (developed under Heliaus EU project) in an indoor lab environment which is then used for generating synthetic 3D face data along with varying yaw face angles and lastly facial depth map is generated.

Jeffrey Regier, Andrew C. Miller, David Schlegel, Ryan P. Adams, Jon D. McAuliffe, Prabhat.

Source: The Annals of Applied Statistics, Volume 13, Number 3, 1884--1926.

Abstract:
We present a new, fully generative model for constructing astronomical catalogs from optical telescope image sets. Each pixel intensity is treated as a random variable with parameters that depend on the latent properties of stars and galaxies. These latent properties are themselves modeled as random. We compare two procedures for posterior inference. One procedure is based on Markov chain Monte Carlo (MCMC) while the other is based on variational inference (VI). The MCMC procedure excels at quantifying uncertainty, while the VI procedure is 1000 times faster. On a supercomputer, the VI procedure efficiently uses 665,000 CPU cores to construct an astronomical catalog from 50 terabytes of images in 14.6 minutes, demonstrating the scaling characteristics necessary to construct catalogs for upcoming astronomical surveys.

Qiwei Li, Xinlei Wang, Faming Liang, Guanghua Xiao.

Source: The Annals of Applied Statistics, Volume 13, Number 3, 1708--1732.

Abstract:
With the advance of imaging technology, digital pathology imaging of tumor tissue slides is becoming a routine clinical procedure for cancer diagnosis. This process produces massive imaging data that capture histological details in high resolution. Recent developments in deep-learning methods have enabled us to identify and classify individual cells from digital pathology images at large scale. Reliable statistical approaches to model the spatial pattern of cells can provide new insight into tumor progression and shed light on the biological mechanisms of cancer. We consider the problem of modeling spatial correlations among three commonly seen cells observed in tumor pathology images. A novel geostatistical marking model with interpretable underlying parameters is proposed in a Bayesian framework. We use auxiliary variable MCMC algorithms to sample from the posterior distribution with an intractable normalizing constant. We demonstrate how this model-based analysis can lead to sharper inferences than ordinary exploratory analyses, by means of application to three benchmark datasets and a case study on the pathology images of $188$ lung cancer patients. The case study shows that the spatial correlation between tumor and stromal cells predicts patient prognosis. This statistical methodology not only presents a new model for characterizing spatial correlations in a multitype spatial point pattern conditioning on the locations of the points, but also provides a new perspective for understanding the role of cell–cell interactions in cancer progression.

[London?], [199-?]

New calculations show that the ring of light surrounding a black hole is actually made up of infinite subrings that can’t be seen with current technology

Biologist Tagide deCarvalho created this award-winning image of the tardigrade using fluorescent stains

The London institution's online offerings include 280,000 newly added Creative Commons images

Artists from Michigan and around the world are painting 50 murals in Flint to refocus the city's image on art rather than the lead-tainted water crisis.

The post SOLIDWORKS Apps for Kids How-To: Trim an Image appeared first on SOLIDWORKS Education Blog.

SOLIDWORKS Image Quality settings have the biggest impact on file size and system performance. We always assumed that it would have an impact on overall performance also. The effect varies widely across different files, so we decided to do SOLIDWORKS

Author information

The post The Impact of Image Quality on SOLIDWORKS Performance and File Size appeared first on SOLIDWORKS Tech Blog.

CNA Staff, Mar 31, 2020 / 06:01 pm (CNA).- Local media in Nicaragua are reporting that despite the nation’s bishops’ conference suspending Mass and prohibiting large gatherings, the mayor of Granada is going ahead with an annual pilgrimage that the bishops had canceled.

For more than 150 years, Catholics in Nicaragua have venerated an image of Jesus del Rescate (Jesus of the Rescue) in Popoyuapa, near the city of Rivas. The image represents the crowning of thorns and the flogging of Christ.

Normally there is an annual pilgrimage to the image, which pilgrims make by wagon and on foot, during the "Week of Sorrows" that marks "the final stretch" of the time of Lent, according to ACI Prensa, CNA’s Spanish language news partner.

However, the bishops have said that this year it will not be possible to carry it out in the traditional way, given the coronavirus (COVID-19) pandemic.

Nevertheless, Julia Mena, mayor of Granada, has organized and funded a group of pilgrims to do the pilgrimage, providing them with food and personal hygiene supplies, according to the Nicaraguan news website Confidencial. The priest at the shrine told Confidencial that he has “no plans to receive the pilgrimage.”

The Nicaraguan Episcopal Conference has not commented publicly on the matter.

Nicaragua has five confirmed cases of COVID-19. The government has not yet decreed any kind of alert or emergency regarding the pandemic, nor has it ordered a suspension of classes, Confidencial reports.

The country’s Ministry of Health has maintained that the internal movements of travelers arriving from countries with outbreaks of coronavirus would not be restricted, La Prensa reports.

President Daniel Ortega has been president of Nicaragua since 2007, and oversaw the abolition of presidential term limits in 2014.

The Church had suggested that elections, which are not scheduled until 2021, be held this year, but Ortega has ruled this out.

Ortega was a leader in the Sandinista National Liberation Front, which had ousted the Somoza dictatorship in 1979 and fought US-backed right-wing counterrevolutionaries during the 1980s. Ortega was also leader of Nicaragua from 1979 to 1990.

Google's reverse image search is a breeze on a desktop, but what about when you're on a mobile device? Google, Bing, and others have options.

Apple's new $4,999 Pro Display XDR monitor (and its $1,000 stand) may sound mighty expensive on the surface. It has the potential to upend a whole industry, however—it's made to compete with panels five to ten times the price. We have one in-house, and here are our first impressions.

Here Mudassar Ahmed Khan has explained with an example, how to show Calendar Icon (Image) next to TextBox using Bootstrap DatePicker (Calendar) plugin. The Calendar Icon (Image) will be displayed using Calendar Icon with Bootstrap FontAwesome Glyphicon Icon. When the Bootstrap FontAwesome Glyphicon Icon is clicked, the Bootstrap DatePicker Calendar is shown.