Guest editors provide an introduction to the Special Section on Medical Image Perception and Observer Performance.

Computer vision syndrome (CVS) is an umbrella term for a pattern of symptoms associated with prolonged digital screen exposure, such as eyestrain, headaches, blurred vision, and dry eyes. Commercially available blue light filtering lenses (BLFL) are advertised as improving CVS. Our pilot study evaluates the effectiveness of BLFL on reducing CVS symptoms and fatigue in a cohort of radiologists. A prospective crossover study was conducted with ten radiology residents randomized into two cohorts: one wearing BLFL first then a sham pair (non-BLFL), and the other wearing a sham pair first then BLFL, over two weeks during normal clinical work. Participants filled out a questionnaire using the validated computer vision syndrome questionnaire (CVS-Q) and the Swedish Occupational Fatigue Inventory (SOFI). The majority of symptoms [11/16 (68.8%) and 13/16 (81.3%) symptoms on the CVS-Q and SOFI, respectively] were reduced (i.e., symptoms less severe) with the BLFL compared to the sham glasses. Females rated symptoms of sleepiness and physical discomfort in the SOFI, and overall CVS-Q, as more severe. Postgraduate year (PGY)-2 residents rated all symptoms as more severe than PGY-3/4s. BLFL may ameliorate CVS symptoms. Future studies with larger sample sizes and participants of different ages are required to verify the potential of BLFL.

Evans et al. (2016) showed that radiologists can classify the mammograms as normal or abnormal at above-chance levels after a 250-ms exposure. Our study documents a similar gist signal in digital breast tomosynthesis (DBT) images. DBT is a relatively new technology that creates a three-dimensional image set of slices through the volume of the breast. It improves performance over two-dimensional (2-D) mammography but at a cost in reading time. In the experiment presented, radiologists (N  =  16) viewed “movies” of DBT images from single breasts for an average of 1.5 s per case. Observers then marked the most likely lesion position on a blank outline and rated each case on a six-point scale from (1) certainly normal to (6) certainly recall. Results show that radiologists can discriminate normal from abnormal DBT cases at above-chance levels as in 2-D mammography. Ability was correlated with experience reading DBT. Observers performed at above-chance levels, even on those images where they could not localize the target, suggesting that this is a global signal that could prove valuable in the clinic.

Current clinical chest CT reporting includes limited qualitative assessment of emphysema with rare mention of lung volumes and limited reporting of emphysema, based upon retrospective review of CT reports. Quantitative CT analysis performed in COPDGene and other research cohorts utilize semiautomated segmentation procedures and well-established research method (Thirona). We compared this reference QCT data with fully automated QCT analysis that can be obtained at the time of CT scan and sent to PACS along with standard chest CT images. 164 COPDGene^® cohort study subjects enrolled at Brigham and Women’s Hospital had baseline and 5-year follow-up CT scans. Subjects included 17 nonsmoking controls, 92 smokers with normal spirometry, 15 preserved ratio impaired spirometry (PRISm) patients, 12 GOLD 1, 20 GOLD 2, and 8 GOLD 3–4. 97% (n  =  319) of clinical reports did not mention lung volumes, and 14% (n  =  46) made no mention of emphysema. Total lung volumes determined by the fully automated algorithm were consistently 47 milliliters (ml) less than the Thirona reference value for all subjects (95% confidence interval −62 to −32  ml). Percent emphysema values were equivalent to the Thirona reference values. Well-established research reference data can be used to evaluate and validate automated QCT software. Validation can be repeated as software is updated.

Computer-aided detection (CAD) systems are used to aid readers interpreting screening mammograms. An expert reader searches the image initially unaided and then once again with the aid of CAD, which prompts automatically detected suspicious regions. This could lead to a “safety-net” effect, where the initial unaided search of the image is adversely affected by the fact that it is preliminary to an additional search with CAD and may, therefore, be less thorough. To investigate the existence of such an effect, we created a visual search experiment for nonexpert observers mirroring breast screening with CAD. Each observer searched 100 images for microcalcification clusters within synthetic images in both prompted (CAD) and unprompted (no-CAD) conditions. Fifty-two participants were recruited for the study, 48 of whom had their eye movements tracked in real-time; the other 4 participants could not be accurately calibrated, so only behavioral data were collected. In the CAD condition, before prompts were displayed, image coverage was significantly lower than coverage in the no-CAD condition (t₄₇  =  5.29, p  <  0.0001). Observer sensitivity was significantly greater for targets marked by CAD than the same targets in the no-CAD condition (t₅₁  =  6.56, p  <  0.001). For targets not marked by CAD, there was no significant difference in observer sensitivity in the CAD condition compared with the same targets in the no-CAD condition (t₅₁  =  0.54, p  =  0.59). These results suggest that the initial search may be influenced by the subsequent availability of CAD; if so, cross-sectional CAD efficacy studies should account for the effect when estimating benefit.

Radiologists can identify whether a radiograph is abnormal or normal at above chance levels in breast and lung images presented for half a second or less. This early perceptual processing has only been demonstrated in static two-dimensional images (e.g., mammograms). Can radiologists rapidly extract the “gestalt” from more complex imaging modalities? For example, prostate multiparametric magnetic resonance imaging (mpMRI) displays a series of images as a virtual stack and comprises multiple imaging sequences: anatomical information from the T2-weighted (T2W) sequence, functional information from diffusion-weighted imaging, and apparent diffusion coefficient sequences. We first tested rapid perceptual processing in static T2W images then among the two functional sequences. Finally, we examined whether this rapid radiological perception could be observed using T2W multislice imaging. Readers with experience in prostate mpMRI could detect and localize lesions in all sequences after viewing a 500-ms static image. Experienced prostate readers could also detect and localize lesions when viewing multislice image stacks presented as brief movies, with image slices presented at either 48, 96, or 144 ms. The ability to quickly extract the perceptual gestalt may be a general property of expert perception, even in complex imaging modalities.

Prior research has demonstrated that perceptual training can improve the ability of healthcare trainees in identifying abnormalities on medical images, but it is unclear if the improved performance is due to learning or attentional shift—the diversion of perceptional resources away from other activities to a specified task. Our objective is to determine if research subject performance in perceiving the central venous catheter position on radiographs is improved after perceptional training and if improved performance is due to learning or an attentional shift. Forty-one physician assistant students were educated on the appropriate radiographic position of central venous catheters and then asked to evaluate the catheter position in two sets of radiographic cases. The experimental group was provided perceptional training between case sets one and two. The control group was not. Participants were asked to characterize central venous catheters for appropriate positioning (task of interest) and to assess radiographs for cardiomegaly (our marker for attentional shift). Our results demonstrated increased confidence in localization in the experimental group (p-value <0.001) but not in the control group (p-value  =  0.882). The ability of subjects to locate the catheter tip significantly improved in both control and experimental groups. Both the experimental (p-value  =  0.007) and control groups (p-value  =  0.001) demonstrated equivalent decreased performance in assessing cardiomegaly; the difference between groups was not significant (p-value  =  0.234). This suggests the performance improvement was secondary to learning not due to an attentional shift.

Purpose: Computer-aided detection (CAD) alerts radiologists to findings potentially associated with breast cancer but is notorious for creating false-positive marks. Although a previous paper found that radiologists took more time to interpret mammograms with more CAD marks, our impression was that this was not true in actual interpretation. We hypothesized that radiologists would selectively disregard these marks when present in larger numbers.

Approach: We performed a retrospective review of bilateral digital screening mammograms. We use a mixed linear regression model to assess the relationship between number of CAD marks and ln (interpretation time) after adjustment for covariates. Both readers and mammograms were treated as random sampling units.

Results: Ten radiologists, with median experience after residency of 12.5 years (range 6 to 24) interpreted 1832 mammograms. After accounting for number of images, Breast Imaging Reporting and Data System category, and breast density, the number of CAD marks was positively associated with longer interpretation time, with each additional CAD mark proportionally increasing median interpretation time by 4.35% for a typical reader.

Conclusions: We found no support for our hypothesis that radiologists will selectively disregard CAD marks when they are present in larger numbers.

We sought to characterize local lung complexity in chest computed tomography (CT) and to characterize its impact on the detectability of pulmonary nodules. Forty volumetric chest CT scans were created by embedding between three and five simulated 5-mm lung nodules into one of three volumetric chest CT datasets. Thirteen radiologists evaluated 157 nodules, resulting in 2041 detection opportunities. Analyzing the substrate CT data prior to nodule insertion, 14 image features were measured within a region around each nodule location. A generalized linear mixed-effects statistical model was fit to the data to verify the contribution of each metric on detectability. The model was tuned for simplicity, interpretability, and generalizability using stepwise regression applied to the primary features and their interactions. We found that variables corresponding to each of five categories (local structural distractors, local intensity, global context, local vascularity, and contiguity with structural distractors) were significant (p  <  0.01) factors in a standardized model. Moreover, reader-specific models conveyed significant differences among readers with significant distraction (missed detections) influenced by local intensity- versus local-structural characteristics being mutually exclusive. Readers with significant local intensity distraction (n  =  10) detected substantially fewer lung nodules than those who were significantly distracted by local structure (n  =  2), 46.1% versus 65.3% mean nodules detected, respectively.

The scientific, clinical, and pedagogical significance of devising methodologies to train nonprofessional subjects to recognize diagnostic visual patterns in medical images has been broadly recognized. However, systematic approaches to doing so remain poorly established. Using mammography as an exemplar case, we use a series of experiments to demonstrate that deep learning (DL) techniques can, in principle, be used to train naïve subjects to reliably detect certain diagnostic visual patterns of cancer in medical images. In the main experiment, subjects were required to learn to detect statistical visual patterns diagnostic of cancer in mammograms using only the mammograms and feedback provided following the subjects’ response. We found not only that the subjects learned to perform the task at statistically significant levels, but also that their eye movements related to image scrutiny changed in a learning-dependent fashion. Two additional, smaller exploratory experiments suggested that allowing subjects to re-examine the mammogram in light of various items of diagnostic information may help further improve DL of the diagnostic patterns. Finally, a fourth small, exploratory experiment suggested that the image information learned was similar across subjects. Together, these results prove the principle that DL methodologies can be used to train nonprofessional subjects to reliably perform those aspects of medical image perception tasks that depend on visual pattern recognition expertise.

Purpose: With three-dimensional (3-D) images displayed as stacks of 2-D images, radiologists rely more heavily on vision away from their fixation point to visually process information, guide eye movements, and detect abnormalities. Thus the ability to detect targets away from the fixation point, commonly characterized as the useful field of view (UFOV), becomes critical for these 3-D imaging modalities. We investigate how the UFOV, defined as the eccentricity, in which detection performance degrades to a given probability, varies across imaging modalities and targets.

Approach: We measure the detectability of different targets at various distances from gaze locations for single slices of liver computed tomography (CT), 2-D digital mammograms (DM), and single slices of digital breast tomosynthesis (DBT) cases. Observers with varying expertise were instructed to maintain their gaze at a point while a short display of the image was flashed and an eye tracker verified observer’s steady fixation. Display times were 200 and 1000 ms for CT images and 500 ms for DM and DBT images.

Results: We find variations in the UFOV from 9 to 12 deg for liver CT to as small as 2.5 to 5 deg for calcification clusters in breast images (DM and DBT). We compare our results to those reported in the literature for lung nodules and discuss the differences across methods used to measure the UFOV, their dependence on case selection/task difficulty, viewing conditions, and observer expertise. We propose a complementary measure defined in terms of performance degradation relative to the peak foveal performance (relative-UFOV) to circumvent UFOV’s variations with case selection/task difficulty.

Conclusion: Our results highlight the variations in the UFOV across imaging modalities, target types, observer expertise, and measurement methods and suggest an additional relative-UFOV measure to more thoroughly characterize the detection performance away from point of fixation.

Purpose: Accurate detection of cancer lesions in positron emission tomography (PET) is fundamental to achieving favorable clinical outcomes. Therefore, image reconstruction, processing, visualization, and interpretation techniques must be optimized for this task. The objective of this work was to (1) develop and validate an efficient method to generate well-characterized synthetic lesions in real patient data and (2) to apply these lesions in a human perception experiment to establish baseline measurements of the limits of lesion detection as a function of lesion size and contrast using current imaging technologies.

Approach: A fully integrated software package for synthesizing well-characterized lesions in real patient PET was developed using a vendor provided PET image reconstruction toolbox (REGRECON5, General Electric Healthcare, Waukesha, Wisconsin). Lesion characteristics were validated experimentally for geometric accuracy, activity accuracy, and absence of artifacts. The Lesion Synthesis Toolbox was used to generate a library of 133 synthetic lesions of varying sizes (n  =  7) and contrast levels (n  =  19) in manually defined locations in the livers of 37 patient studies. A lesion-localization perception study was performed with seven observers to determine the limits of detection with regard to lesion size and contrast using our web-based perception study tool.

Results: The Lesion Synthesis Toolbox was validated for accurate lesion placement and size. Lesion intensities were deemed accurate with slightly elevated activities (5% at 2:1 lesion-to-background contrast) in small lesions (Ø  =  15  mm spheres), and no bias in large lesions (Ø  =  22.5  mm). Bed-stitching artifacts were not observed, and lesion attenuation correction bias was small (−1.6  ±  1.2  %  ). The 133 liver lesions were synthesized in ∼50  h, and readers were able to complete the perception study of these lesions in 12  ±  3  min with consistent limits of detection amongst all readers.

Conclusions: Our open-source utilities can be employed by nonexperts to generate well-characterized synthetic lesions in real patient PET images and for administering perception studies on clinical workstations without the need to install proprietary software.

Purpose: We present a phase-contrast x-ray tomography study of wild type C57BL/6 mouse hearts as a nondestructive approach to the microanatomy on the scale of the entire excised organ. Based on the partial coherence at a home-built phase-contrast μ-CT setup installed at a liquid metal jet source, we exploit phase retrieval and hence achieve superior image quality for heart tissue, almost comparable to previous synchrotron data on the whole organ scale.

Approach: In our work, different embedding methods and heavy metal-based stains have been explored. From the tomographic reconstructions, quantitative structural parameters describing the three-dimensional (3-D) architecture have been derived by two different fiber tracking algorithms. The first algorithm is based on the local gradient of the reconstructed electron density. By performing a principal component analysis on the local structure-tensor of small subvolumes, the dominant direction inside the volume can be determined. In addition to this approach, which is already well established for heart tissue, we have implemented and tested an algorithm that is based on a local 3-D Fourier transform.

Results: We showed that the choice of sample preparation influences the 3-D structure of the tissue, not only in terms of contrast but also with respect to the structural preservation. A heart prepared with the evaporation-of-solvent method was used to compare both algorithms. The results of structural orientation were very similar for both approaches. In addition to the determination of the fiber orientation, the degree of filament alignment and local thickness of single muscle fiber bundles were obtained using the Fourier-based approach.

Conclusions: Phase-contrast x-ray tomography allows for investigating the structure of heart tissue with an isotropic resolution below 10  μm. The fact that this is possible with compact laboratory instrumentation opens up new opportunities for screening samples and optimizing sample preparation, also prior to synchrotron beamtimes. Further, results from the structural analysis can help in understanding cardiovascular diseases or can be used to improve computational models of the heart.

Purpose: For the focal spot measurement of x-ray tubes, we propose a practical method in which only a metal edge and a digital detector are used, together with a process of removing detector blur inherently associated.

Approach: The evaluation was made through the optical transfer function (OTF) measurements using the edge response of a 1-mm-thick tungsten plate. First, we made the acquisition of a geometrically magnified edge response, which consists of focal spot penumbra and detector blur, followed by the acquisition of nonmagnified edge response, which includes only detector blur. Then the detector blur was removed by taking the ratio of the two OTFs. Finally, the focal spot profile was obtained by the inverse Fourier transform of this ratio.

Results: Resultant full widths at the half-maximum of a small focus profile were 0.529  ±  0.005  mm for the proposed method and 0.527  ±  0.020  mm for the conventional slit method with film, indicating excellent agreement between both methods. Comparing between results obtained using two flat panel detectors with different pixel pitches (0.143 and 0.175 mm) confirmed no differences with these variations.

Conclusion: Through the whole study, the accuracy and the practicality of the proposed method were demonstrated, indicating a possibility of the method to be widely used to evaluate the effective focal spot size and profile of x-ray systems.

Purpose: We investigate an analyzer-less x-ray interferometer with a spatially modulated phase grating (MPG) that can deliver three modalities (attenuation image, phase image, and scatter images) in breast computed tomography (BCT). The system can provide three x-ray modalities while preserving the dose to the object and can achieve attenuation image sensitivity similar to that of a standard absorption-only BCT. The MPG system works with a source, a source-grating, a single phase grating, and a detector. No analyzer is necessary. Thus, there is an approximately 2x improvement in fluence at the detector for our system compared with the same source–detector distance Talbot–Lau x-ray interferometry (TLXI) because the TLXI has an analyzer after the object, which is not required for the MPG.

Approach: We investigate the MPG BCT system in simulations and find a clinically feasible system geometry. First, the mechanism of MPG interferometry is conceptually shown via Sommerfeld–Rayleigh diffraction integral simulations. Next, we investigate source coherence requirements, fringe visibility, and phase sensitivity dependence on different system parameters and find clinically feasible system geometry.

Results: The phase sensitivity of MPG interferometry is proportional to object–detector distance and inversely proportional to a period of broad fringes at the detector, which is determined by the grating spatial modulation period. In our simulations, the MPG interferometry can achieve about 27% fringe visibility with clinically realistic BCT geometry of a total source–detector distance of 950 mm and source–object distance of 500 mm.

Conclusions: We simulated a promising analyzer-less x-ray interferometer, with a spatially sinusoidal MPG. Our system is expected to deliver the attenuation, phase and scatter image in a single acquisition without dose or fluence detriment, compared with conventional BCT.

Purpose: About one third of all deaths worldwide can be traced to some form of cardiovascular disease. The gold standard for the diagnosis and interventional treatment of blood vessels is digital subtraction angiography (DSA). An alternative to DSA is K-edge subtraction (KES) imaging, which has been shown to be advantageous for moving organs and for eliminating image artifacts caused by patient movement. As highly brilliant, monochromatic x-rays are required for this method, it has been limited to synchrotron facilities so far, restraining the applicability in the clinical routine. Over the past decades, compact synchrotron x-ray sources based on inverse Compton scattering have been evolving; these provide x-rays with sufficient brilliance and meet spatial and financial requirements for laboratory settings or university hospitals.

Approach: We demonstrate a proof-of-principle KES imaging experiment using the Munich Compact Light Source (MuCLS), the first user-dedicated installation of a compact synchrotron x-ray source worldwide. A series of experiments were performed both on a phantom and an excised human carotid to demonstrate the ability of the proposed KES technique to separate the iodine contrast agent and calcifications.

Results: It is shown that the proposed filter-based KES method allows for the iodine-contrast agent and calcium to be clearly separated, thereby providing x-ray images only showing one of the two materials.

Conclusions: The results show that the quasimonochromatic spectrum of the MuCLS enables filter-based KES imaging and can become an important tool in preclinical research and possible future clinical diagnostics.

Purpose: Although the mortality rate of breast cancer was reduced with the introduction of screening mammography, many women undergo unnecessary subsequent examinations due to inconclusive diagnoses. Superposition of anatomical structures especially within dense breasts in conjunction with the inherently low soft tissue contrast of absorption images compromises image quality. This can be overcome by phase-contrast imaging.

Approach: We analyze the spatial resolution of grating-based multimodal mammography using a mammographic phantom and one freshly dissected mastectomy specimen at an inverse Compton x-ray source. Here, the focus was on estimating the spatial resolution with the sample in the beam path and discussing benefits and drawbacks of the method used and the estimation of the mean glandular dose. Finally, the possibility of improving the spatial resolution is investigated by comparing monochromatic grating-based mammography with the standard one.

Results: The spatial resolution is constant or also higher for the image acquired with monochromatic radiation and the contrast-to-noise ratio (CNR) is higher in our approach while the dose can be reduced by up to 20%.

Conclusions: In summary, phase-contrast imaging helps to improve tumor detection by advanced diagnostic image quality. We demonstrate a higher spatial resolution for one mastectomy specimen and increased CNR at an equal or lower dose for the monochromatic measurements.

New increasingly complex in vitro cancer cell models are being developed. These new models seem to represent the cell behavior in vivo more accurately and have better physiological relevance than prior models. An efficient testing method for selecting the most optimal drug treatment does not exist to date. One proposed solution to the problem involves isolation of cancer cells from the patients’ cancer tissue, after which they are exposed to potential drugs alone or in combinations to find the most optimal medication. To achieve this goal, methods that can efficiently quantify and analyze changes in tested cell are needed. Our study aimed to detect and segment cells and structures from cancer cell cultures grown on vascular structures in phase-contrast microscope images using U-Net neural networks to enable future drug efficacy assessments. We cultivated prostate carcinoma cell lines PC3 and LNCaP on the top of a matrix containing vascular structures. The cells were imaged with a Cell-IQ phase-contrast microscope. Automatic analysis of microscope images could assess the efficacy of tested drugs. The dataset included 36 RGB images and ground-truth segmentations with mutually not exclusive classes. The used method could distinguish vascular structures, cells, spheroids, and cell matter around spheroids in the test images. Some invasive spikes were also detected, but the method could not distinguish the invasive cells in the test images.

When mining image data from PACs or clinical trials or processing large volumes of data without curation, the relevant scans must be identified among irrelevant or redundant data. Only images acquired with appropriate technical factors, patient positioning, and physiological conditions may be applicable to a particular image processing or machine learning task. Automatic labeling is important to make big data mining practical by replacing conventional manual review of every single-image series. Digital imaging and communications in medicine headers usually do not provide all the necessary labels and are sometimes incorrect. We propose an image-based high throughput labeling pipeline using deep learning, aimed at identifying scan direction, scan posture, lung coverage, contrast usage, and breath-hold types. They were posed as different classification problems and some of them involved further segmentation and identification of anatomic landmarks. Images of different view planes were used depending on the specific classification problem. All of our models achieved accuracy >99  %   on test set across different tasks using a research database from multicenter clinical trials.

Purpose: Due to the high incidence and mortality rates of lung cancer worldwide, early detection of a precancerous lesion is essential. Low-dose computed tomography is a commonly used technique for screening, diagnosis, and prognosis of non-small-cell lung cancer. Recently, convolutional neural networks (CNN) had shown great potential in lung nodule classification. Clinical information (family history, gender, and smoking history) together with nodule size provide information about lung cancer risk. Large nodules have greater risk than small nodules.

Approach: A subset of cases from the National Lung Screening Trial was chosen as a dataset in our study. We divided the nodules into large and small nodules based on different clinical guideline thresholds and then analyzed the groups individually. Similarly, we also analyzed clinical features by dividing them into groups. CNNs were designed and trained over each of these groups individually. To our knowledge, this is the first study to incorporate nodule size and clinical features for classification using CNN. We further made a hybrid model using an ensemble with the CNN models of clinical and size information to enhance malignancy prediction.

Results: From our study, we obtained 0.9 AUC and 83.12% accuracy, which was a significant improvement over our previous best results.

Conclusions: In conclusion, we found that dividing the nodules by size and clinical information for building predictive models resulted in improved malignancy predictions. Our analysis also showed that appropriately integrating clinical information and size groups could further improve risk prediction.

Purpose: High-resolution cardiac imaging and fiber analysis methods are required to understand cardiac anatomy. Although refraction-contrast x-ray CT (RCT) has high soft tissue contrast, it cannot be commonly used because it requires a synchrotron system. Microfocus x-ray CT (μCT) is another commercially available imaging modality.

Approach: We evaluate the usefulness of μCT for analyzing fibers by quantitatively and objectively comparing the results with RCT. To do so, we scanned a rabbit heart by both modalities with our original protocol of prepared materials and compared their image-based analysis results, including fiber orientation estimation and fiber tracking.

Results: Fiber orientations estimated by two modalities were closely resembled under the correlation coefficient of 0.63. Tracked fibers from both modalities matched well the anatomical knowledge that fiber orientations are different inside and outside of the left ventricle. However, the μCT volume caused incorrect tracking around the boundaries caused by stitching scanning.

Conclusions: Our experimental results demonstrated that μCT scanning can be used for cardiac fiber analysis, although further investigation is required in the differences of fiber analysis results on RCT and μCT.

Purpose: Preclinical studies often compare micro-computed tomography (micro-CT) imaging with histology using optical microscopy of fluorescently labeled slides. However, correlating the images is difficult because the tissues appear differently in the two modalities. It would be valuable to have a single contrast medium visible on both radiographic and optical imaging.

Approach: We have explored the detectability of fluorescently labeled gold nanoparticles under micro-CT and optical projection tomography (OPT) in agarose phantoms and a murine melanoma tumor model. Murine melanoma cells were used to induce tumor growth in the right hind legs of 12 C57Bl6 mice, with the maximal tumor size of 1  cm³. We injected Cy3 fluorescently coated gold nanorods directly into the tumors. The mice were scanned with in vivo micro-CT (for pre- and post-contrast scans). Once euthanized, the hind leg was dissected and scanned with a higher resolution specimen micro-CT and OPT.

Results: The distribution of the gold nanoparticles appeared to be contained and isolated to the tumor. Alignment of micro-CT specimen scans with the OPT scans was possible, although there was also autofluorescence of the surrounding muscle tissue.

Conclusions: This study highlights the potential use of fluorescently labeled gold nanoparticles for imaging murine melanoma tumors using micro-CT and OPT.

Purpose: Recent theories examine the role of the fractal branching vasculature as a primary site of Born scattering from soft normal tissues. These derivations postulate that the first-order statistics of speckle from soft tissue, such as the liver, thyroid, and prostate, will follow a Burr distribution with a power law parameter that can be related back to the underlying power law, which governs the branching network. However, the issue of scatterer spacing, or the number of cylindrical vessels per sample volume of the interrogating pulse, has not been directly addressed.

Approach: Speckle statistics are examined with a 3D simulation that varies the number density and the governing power law parameter of an ensemble of different sized cylinders. Several in vivo liver scans are also analyzed for confirmation across different conditions.

Results: The Burr distribution is found to be an appropriate model for the histogram of amplitudes from speckle regions, where the parameters track the underlying power law and scatterer density conditions. These results are also tested in a more general model of rat liver scans in normal versus abnormal conditions, and the resulting Burr parameters are also found to be appropriate and sensitive to underlying scatterer distributions.

Conclusions: These preliminary results suggest that the classical Burr distribution may be useful in the quantification of scattering of ultrasound from soft vascularized tissues and as a tool in tissue characterization.

Purpose: As pathology departments around the world contemplate digital microscopy for primary diagnosis, making an informed choice regarding display procurement is very challenging in the absence of defined minimum standards. In order to help inform the decision, we aimed to conduct an evaluation of displays with a range of technical specifications and sizes.

Approach: We invited histopathologists within our institution to take part in a survey evaluation of eight short-listed displays. Pathologists reviewed a single haematoxylin and eosin whole slide image of a benign nevus on each display and gave a single score to indicate their preference in terms of image quality and size of the display.

Results: Thirty-four pathologists took part in the display evaluation experiment. The preferred display was the largest and had the highest technical specifications (11.8-MP resolution, 2100  cd  /  m² maximum luminance). The least preferred display had the lowest technical specifications (2.3-MP resolution, 300  cd  /  m² maximum luminance). A trend was observed toward an increased preference for displays with increased luminance and resolution.

Conclusions: This experiment demonstrates a preference for large medical-grade displays with the high luminance and high resolution. As cost becomes implicated in procurement, significantly less expensive medical-grade displays with slightly lower technical specifications may be the most cost-effective option.

Corrections to the article, “Multilabel segmentation of cancer cell culture on vascular structures with deep neural networks,” by S. Rahkonen et al.