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Contains fulltext : 232884.pdf (Publisher’s version ) (Open Access), PURPOSE: To compare diffusion-weighted imaging of the breast performed with a conventional readout-segmented echo-planar imaging (rs-EPI) sequence to when using a prototype simultaneous multi-slice single-shot EPI (SMS-ss-EPI) acquisition. METHOD: From September 2017 to December 2018, 26 women with histologically proven breast cancer were scanned with the conventional rs-EPI and the SMS-ss-EPI at 3T during the same imaging examination. Four breast radiologists (4-13 years of experience) independently scored both acquired series of 25 women (one case was used for training) for overall image quality (1: extremely poor to 9: excellent) and artifacts (1: very disturbing to 5: not present). All lesions (n=52; 40 malignant, 12 benign) were also evaluated for visibility (1: not visible, 2: visible if location is given, 3: visible). In addition, lesion characteristics were rated, and a BI-RADS score was given. Results were analyzed using visual grading characteristics and the resulting area under the curve (AUCVGC), weighted kappa, McNemar test, and dependent-samples t-test when appropriate. RESULTS: Overall, radiologists significantly preferred the image quality in rs-EPI over that of SMS-ss-EPI (AUCVGC: 0.698, P=0.002). Infolding and ghosting, and distortion artifacts were significantly less apparent in the rs-EPI (AUCVGC: 0.660, P=0.022 and AUCVGC: 0.700 P=0.002, respectively). Lesions were, however, significantly better visible on the SMS-ss-EPI images (AUCVGC: 0.427, P=0.016). Malignant lesions had significantly higher visibility with SMS-ss-EPI (P=0.035). Sensitivity and specificity were comparable between both sequences (P=0.760 and P=0.549, respectively). CONCLUSIONS: Despite the perceived lower image quality and the increased presence of artifacts in the SMS-ss-EPI sequence, malignant lesions are better visualized using this sequence.

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Based on experimental results, this chapter describes applications of SAR polarimetry to extract relevant information on agriculture and wetland scenarios by exploiting differences in the polarimetric signature of different scatterers, crop types and their development stage depending on their physical properties. Concerning agriculture, crop type mapping, soil moisture estimation and phenology estimation are reviewed, as they are ones with a clear benefit of full polarimetry over dual or single polarimetry. For crop type mapping, supervised or partially unsupervised classification schemes are used. Phenology estimation is treated as a classification problem as well, by regarding the different stages as different classes. Soil moisture estimation makes intensive use of scattering models, in order to separate soil and vegetation scattering and to invert for soil moisture from the isolated ground component. Then, applications of SAR polarimetry to wetland monitoring are considered that include the delineation of their extent and their characterisation by means of polarimetric decompositions. In the last section of the chapter, the use of a SAR polarimetric decomposition is shown for the assessment of the damages consequential to earthquakes and tsunamis.

The coronavirus disease 2019 (COVID-19) pandemic is a global health care emergency. Although reverse-transcription polymerase chain reaction testing is the reference standard method to identify patients with COVID-19 infection, chest radiography and CT play a vital role in the detection and management of these patients. Prediction models for COVID-19 imaging are rapidly being developed to support medical decision making. However, inadequate availability of a diverse annotated data set has limited the performance and generalizability of existing models. To address this unmet need, the RSNA and Society of Thoracic Radiology collaborated to develop the RSNA International COVID-19 Open Radiology Database (RICORD). This database is the first multi-institutional, multinational, expert-annotated COVID-19 imaging data set. It is made freely available to the machine learning community as a research and educational resource for COVID-19 chest imaging. Pixel-level volumetric segmentation with clinical annotations was performed by thoracic radiology subspecialists for all COVID-19-positive thoracic CT scans. The labeling schema was coordinated with other international consensus panels and COVID-19 data annotation efforts, the European Society of Medical Imaging Informatics, the American College of Radiology, and the American Association of Physicists in Medicine. Study-level COVID-19 classification labels for chest radiographs were annotated by three radiologists, with majority vote adjudication by board-certified radiologists. RICORD consists of 240 thoracic CT scans and 1000 chest radiographs contributed from four international sites. It is anticipated that RICORD will ideally lead to prediction models that can demonstrate sustained performance across populations and health care systems., NA

Item does not contain fulltext, Screening for breast cancer reduces breast cancer-related mortality and earlier detection facilitates less aggressive treatment. Unfortunately, current screening modalities are imperfect, suffering from limited sensitivity and high false-positive rates. Novel techniques in the field of breast imaging may soon play a role in breast cancer screening: digital breast tomosynthesis, contrast material-enhanced spectral mammography, US (automated three-dimensional breast US, transmission tomography, elastography, optoacoustic imaging), MRI (abbreviated and ultrafast, diffusion-weighted imaging), and molecular breast imaging. Artificial intelligence and radiomics have the potential to further improve screening strategies. Furthermore, nonimaging-based screening tests such as liquid biopsy and breathing tests may transform the screening landscape. (c) RSNA, 2020 Online supplemental material is available for this article.

Item does not contain fulltext, Although computer-aided diagnosis (CAD) is widely used in mammography, conventional CAD programs that use prompts to indicate potential cancers on the mammograms have not led to an improvement in diagnostic accuracy. Because of the advances in machine learning, especially with use of deep (multilayered) convolutional neural networks, artificial intelligence has undergone a transformation that has improved the quality of the predictions of the models. Recently, such deep learning algorithms have been applied to mammography and digital breast tomosynthesis (DBT). In this review, the authors explain how deep learning works in the context of mammography and DBT and define the important technical challenges. Subsequently, they discuss the current status and future perspectives of artificial intelligence-based clinical applications for mammography, DBT, and radiomics. Available algorithms are advanced and approach the performance of radiologists-especially for cancer detection and risk prediction at mammography. However, clinical validation is largely lacking, and it is not clear how the power of deep learning should be used to optimize practice. Further development of deep learning models is necessary for DBT, and this requires collection of larger databases. It is expected that deep learning will eventually have an important role in DBT, including the generation of synthetic images.

Contains fulltext : 208984.pdf (publisher's version ) (Open Access), Multiple studies in the first decade of the 21(st) century have established contrast-enhanced breast MRI as a screening modality for women with a hereditary or familial increased risk for the development of breast cancer. In recent studies, in women with various risk profiles, the sensitivity ranges between 81% and 100%, which is approximately twice as high as the sensitivity of mammography. The specificity increases in follow-up rounds to around 97%, with positive predictive values for biopsy in the same range as for mammography. MRI preferentially detects the more aggressive/invasive types of breast cancer, but has a higher sensitivity than mammography for any type of cancer. This performance implies that in women screened with breast MRI, all other examinations must be regarded as supplemental. Mammography may yield ~5% additional cancers, mostly ductal carcinoma in situ, while slightly decreasing specificity and increasing the costs. Ultrasound has no supplemental value when MRI is used. Evidence is mounting that in other groups of women the performance of MRI is likewise superior to more conventional screening techniques. Particularly in women with a personal history of breast cancer, the gain seems to be high, but also in women with a biopsy history of lobular carcinoma in situ and even women at average risk, similar results are reported. Initial outcome studies show that breast MRI detects cancer earlier, which induces a stage-shift increasing the survival benefit of screening. Cost-effectiveness is still an issue, particularly for women at lower risk. Since costs of the MRI scan itself are a driving factor, efforts to reduce these costs are essential. The use of abbreviated MRI protocols may enable more widespread use of breast MRI for screening. Level of Evidence: 1 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2019;50:377-390.

Contains fulltext : 208973.pdf (publisher's version ) (Open Access), MRI of the breast has the highest sensitivity for breast cancer detection among current clinical imaging modalities and is indispensable for breast imaging practice. While the basis of breast MRI consists of T1-weighted contrast-enhanced imaging, T2-weighted, ultrafast, and diffusion-weighted imaging may be used to improve lesion characterization. Such multiparametric assessment of breast lesions allows for excellent discrimination between benign and malignant breast lesions. Indications for breast MRI are expanding. In preoperative staging, multiple studies confirm the superiority of MRI to other imaging modalities for tumor size estimation and detection of additional tumor foci in the ipsilateral and contralateral breast. Ongoing studies show that in experienced hands this can be used to improve breast cancer surgery, although there is no evidence of improved long-term outcomes. Screening indications are likewise growing as evidence is accumulating that OncologicRI depicts cancers at an earlier stage than mammography in all women. To manage the associated costs for screening, the use of abbreviated protocols may be beneficial. In patients treated with neoadjuvant chemotherapy, MRI is used to document response. It is essential to realize that oncologic and surgical response are different, and evaluation should be adapted to the underlying question.

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Zusammenfassung: Ber Hoizgeruch wurde bisiang bei der Bestimmung der Holzart und Beschreibung des Holzzustandes nur marginal berücksichtigt. Mit Hilfe einer Anlage mit einer neuen Kombination von Gassensorarrays (Serien von gasempfindlichen Metallhalbleitern), verbunden mit neuronalen Netzen, wurden einheimische Nadel- und Laubhölzer sowie tropische Laubhölzer auf ihre Unterscheidbarkeit getestet. Eine gezielte Datenbearbeitung ermöglicht es, auch bisher schwer zu unterscheidende Arten über den Geruch zu trennen. Neben der Abhängigkeit des Geruches von der Holzart und weiterer Faktoren (mit dem Ort der Probenentnahme im Baum zusammenhängend) werden auch allgemeine Anwendungsmöglichkeiten des Systems kurz diskutiert

Contains fulltext : 178221.pdf (publisher's version ) (Closed access), OBJECTIVE: Ultrafast dynamic contrast-enhanced magnetic resonance imaging of the breast enables assessment of the contrast inflow dynamics while providing images with diagnostic spatial resolution. However, the slice thickness of common ultrafast techniques still prevents multiplanar reconstruction. In addition, some temporal blurring of the enhancement characteristics occurs in case view-sharing is used. We evaluate a prototype compressed-sensing volume-interpolated breath-hold examination (CS-VIBE) sequence for ultrafast breast MRI that improves through plane spatial resolution and avoids temporal blurring while maintaining an ultrafast temporal resolution (less than 5 seconds per volume). Image quality (IQ) of the new sequence is compared with an ultrafast view-sharing sequence (time-resolved angiography with interleaved stochastic trajectories [TWIST]), and assessment of lesion morphology is compared with a regular T1-weighted 3D Dixon sequence (VIBE-DIXON) with an acquisition time of 91 seconds. MATERIALS AND METHODS: From April 2016 to October 2016, 30 women were scanned with the CS-VIBE sequence, replacing the routine ultrafast TWIST sequence in a hybrid breast MRI protocol. The need for informed consent was waived. All MRI scans were performed on a 3T MAGNETOM Skyra system (Siemens Healthcare, Erlangen, Germany) using a 16-channel bilateral breast coil. Two reader studies were conducted involving 5 readers. In the first study, overall IQ of CS-VIBE and TWIST in the axial plane was independently rated for 23 women for whom prior MRI examinations with TWIST were available. In addition, the presence of several types of artifacts was rated on a 5-point scale. The second study was conducted in women (n = 16) with lesions. In total, characteristics of 31 lesions (5 malignant and 26 benign) were described independently for CS-VIBE and VIBE-DIXON, according to the BI-RADS MRI-lexicon. In addition, a lesion conspicuity score was given. RESULTS: Using CS-VIBE, a much

Denitrification is the main process removing nitrate in river drainage basins and buffer input from agricultural land and limits aquatic ecosystem pollution. However, the identification of denitrification hotspots (for example, riparian zones), their role in a landscape context and the evolution of their overall removal capacity at the drainage basin scale are still challenging. The main approaches used (that is, mass balance method, denitrification proxies, and potential wetted areas) suffer from methodological drawbacks. We review these approaches and the key frameworks that have been proposed to date to formalize the understanding of the mechanisms driving denitrification: (i) Diffusion versus advection pathways of nitrate transfer, (ii) the biogeochemical hotspot, and (iii) the Damköhler ratio. Based on these frameworks, we propose to use high-resolution mapping of catchment topography and landscape pattern to define both potential denitrification sites and the dynamic hydrologic modeling at a similar spatial scale (<10 km2). It would allow the quantification of cumulative denitrification activity at the small catchment scale, using spatially distributed Damköhler and Peclet numbers and biogeochemical proxies. Integration of existing frameworks with new tools and methods offers the potential for significant breakthroughs in the quantification and modeling of denitrification in small drainage basins. This can provide a basis for improved protection and restoration of surface water and groundwater quality.