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OBJECTIVES The aim of the study is to investigate the performance of artificial intelligence (AI) convolutional neural networks (CNN) in detecting lung nodules on chest computed tomography of patients with complex lung disease, and demonstrate its noninferiority when compared against an experienced radiologist through clinically relevant assessments. METHODS A CNN prototype was used to retrospectively evaluate 103 complex lung disease cases and 40 control cases without reported nodules. Computed tomography scans were blindly evaluated by an expert thoracic radiologist; a month after initial analyses, 20 positive cases were re-evaluated with the assistance of AI. For clinically relevant applications: (1) AI was asked to classify each patient into nodules present or absent and (2) AI results were compared against standard radiology reports. Standard statistics were performed to determine detection performance. RESULTS AI was, on average, 27 seconds faster than the expert and detected 8.4% of nodules that would have been missed. AI had a sensitivity of 67.7%, similar to an accuracy reported for experienced radiologists. AI correctly classified each patient (nodules present/absent) with a sensitivity of 96.1%. When matched against radiology reports, AI performed with a sensitivity of 89.4%. Control group assessment demonstrated an overall specificity of 82.5%. When aided by AI, the expert decreased the average assessment time per case from 2:44 minutes to 35.7 seconds, while reporting an overall increase in confidence. CONCLUSION In a group of patients with complex lung disease, the sensitivity of AI is similar to an experienced radiologist and the tool helps detect previously missed nodules. AI also helps experts analyze for lung nodules faster and more confidently, a feature that is beneficial to patients and favorable to hospitals due to increased patient load and need for shorter turnaround times.

Background Radiology reporting of emergency whole-body computed tomography (CT) scans is time-critical and therefore involves a significant risk of pathology under-detection. We hypothesize a relevant number of initially missed secondary thoracic findings that would have been detected by an artificial intelligence (AI) software platform including several pathology-specific AI algorithms. Methods This retrospective proof-of-concept-study consecutively included 105 shock-room whole-body CT scans. Image data was analyzed by platform-bundled AI-algorithms, findings were reviewed by radiology experts and compared with the original radiologist's reports. We focused on secondary thoracic findings, such as cardiomegaly, coronary artery plaques, lung lesions, aortic aneurysms and vertebral fractures. Results We identified a relevant number of initially missed findings, with their quantification based on 105 analyzed CT scans as follows: up to 25 patients (23.8%) with cardiomegaly or borderline heart size, 17 patients (16.2%) with coronary plaques, 34 patients (32.4%) with aortic ectasia, 2 patients (1.9%) with lung lesions classified as "recommended to control" and 13 initially missed vertebral fractures (two with an acute traumatic origin). A high number of false positive or non-relevant AI-based findings remain problematic especially regarding lung lesions and vertebral fractures. Conclusions We consider AI to be a promising approach to reduce the number of missed findings in clinical settings with a necessary time-critical radiological reporting. Nevertheless, algorithm improvement is necessary focusing on a reduction of "false positive" findings and on algorithm features assessing the finding relevance, e.g., fracture age or lung lesion malignancy.

OBJECTIVE. The purpose of this study was to evaluate an artificial intelligence (AI)based prototype algorithm for fully automated quantification of emphysema on chest CT compared with pulmonary function testing (spirometry).MATERIALS AND METHODS. A total of 141 patients (72 women, mean age +/- SD of 66.46 +/- 9.7 years [range, 23-86 years]; 69 men, mean age of 66.72 +/- 11.4 years [range, 27-91 years]) who underwent both chest CT acquisition and spirometry within 6 months were retrospectively included. The spirometry-based Tiffeneau index (TI; calculated as the ratio of forced expiratory volume in the first second to forced vital capacity) was used to measure emphysema severity; a value less than 0.7 was considered to indicate airway obstruction. Segmentation of the lung based on two different reconstruction methods was carried out by using a deep convolution image-to-image network. This multilayer convolutional neural network was combined with multilevel feature chaining and depth monitoring. To discriminate the output of the network from ground truth, an adversarial network was used during training. Emphysema was quantified using spatial filtering and attenuation-based thresholds. Emphysema quantification and TI were compared using the Spearman correlation coefficient.RESULTS. The mean TI for all patients was 0.57 +/- 0.13. The mean percentages of emphysema using reconstruction methods 1 and 2 were 9.96% +/- 11.87% and 8.04% +/- 10.32%, respectively. AI-based emphysema quantification showed very strong correlation with TI (reconstruction method 1, rho = -0.86; reconstruction method 2, rho = -0.85; both p CONCLUSION. AI-based, fully automated emphysema quantification shows good correlation with TI, potentially contributing to an image-based diagnosis and quantification of emphysema severity.

OBJECTIVES The objective of this study was to evaluate an artificial intelligence (AI)-based prototype algorithm for the fully automated per lobe segmentation and emphysema quantification (EQ) on chest-computed tomography as it compares to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) severity classification of chronic obstructive pulmonary disease (COPD) patients. METHODS Patients (n=137) who underwent chest-computed tomography acquisition and spirometry within 6 months were retrospectively included in this Institutional Review Board-approved and Health Insurance Portability and Accountability Act-compliant study. Patient-specific spirometry data, which included forced expiratory volume in 1 second, forced vital capacity, and the forced expiratory volume in 1 second/forced vital capacity ratio (Tiffeneau-Index), were used to assign patients to their respective GOLD stage I to IV. Lung lobe segmentation was carried out using AI-RAD Companion software prototype (Siemens Healthineers), a deep convolution image-to-image network and emphysema was quantified in each lung lobe to detect the low attenuation volume. RESULTS A strong correlation between the whole-lung-EQ and the GOLD stages was found (ρ=0.88, P

Airway wall thickening is a consequence of chronic inflammatory processes and usually only qualitatively described in CT radiology reports. The purpose of this study is to automatically quantify airway wall thickness in multiple airway generations and assess the diagnostic potential of this parameter in a large cohort of patients with Chronic Obstructive Pulmonary Disease (COPD).This retrospective, single-center study included a series of unenhanced chest CTs. Inclusion criteria were the mentioning of an explicit COPD GOLD stage in the written radiology report and time period (01/2019-12/2021). A control group included chest CTs with completely unremarkable lungs according to the report. The DICOM images of all cases (axial orientation; slice-thickness: 1 mm; soft-tissue kernel) were processed by an AI algorithm pipeline consisting of (A) a 3D-U-Net for det detection and tracing of the bronchial tree centerlines (B) extraction of image patches perpendicular to the centerlines of the bronchi, and (C) a 2D U-Net for segmentation of airway walls on those patches. The performance of centerline detection and wall segmentation was assessed. The imaging parameter average wall thickness was calculated for bronchus generations 3-8 (AWTAirway wall thickness increases in patients suffering from COPD and is automatically quantifiable. AWTQuantitative measurements considering the complete visible bronchial tree instead of qualitative description could enhance radiology reports, allow for precise monitoring of disease progression and diagnosis of early stages of disease.

BACKGROUND: Manually performed diameter measurements on ECG-gated CT-angiography (CTA) represent the gold standard for diagnosis of thoracic aortic dilatation. However, they are time-consuming and show high inter-reader variability. Therefore, we aimed to evaluate the accuracy of measurements of a deep learning-(DL)-algorithm in comparison to those of radiologists and evaluated measurement times (MT). METHODS: We retrospectively analyzed 405 ECG-gated CTA exams of 371 consecutive patients with suspected aortic dilatation between May 2010 and June 2019. The DL-algorithm prototype detected aortic landmarks (deep reinforcement learning) and segmented the lumen of the thoracic aorta (multi-layer convolutional neural network). It performed measurements according to AHA-guidelines and created visual outputs. Manual measurements were performed by radiologists using centerline technique. Human performance variability (HPV), MT and DL-performance were analyzed in a research setting using a linear mixed model based on 21 randomly selected, repeatedly measured cases. DL-algorithm results were then evaluated in a clinical setting using matched differences. If the differences were within 5 mm for all locations, the cases was regarded as coherent; if there was a discrepancy >5 mm at least at one location (incl. missing values), the case was completely reviewed. RESULTS: HPV ranged up to ±3.4 mm in repeated measurements under research conditions. In the clinical setting, 2,778/3,192 (87.0%) of DL-algorithm’s measurements were coherent. Mean differences of paired measurements between DL-algorithm and radiologists at aortic sinus and ascending aorta were −0.45±5.52 and −0.02±3.36 mm. Detailed analysis revealed that measurements at the aortic root were over-/underestimated due to a tilted measurement plane. In total, calculated time saved by DL-algorithm was 3:10 minutes/case. CONCLUSIONS: The DL-algorithm provided coherent results to radiologists at almost 90% of measurement locations, while the majority of discrepent cases were located at the aortic root. In summary, the DL-algorithm assisted radiologists in performing AHA-compliant measurements by saving 50% of time per case.

PURPOSE The purpose of this study was to validate the accuracy of an artificial intelligence (AI) prototype application in determining bone mineral density (BMD) from chest computed tomography (CT), as compared with dual-energy x-ray absorptiometry (DEXA). MATERIALS AND METHODS In this Institutional Review Board-approved study, we analyzed the data of 65 patients (57 female, mean age: 67.4 y) who underwent both DEXA and chest CT (mean time between scans: 1.31 y). From the DEXA studies, T-scores for L1-L4 (lumbar vertebrae 1 to 4) were recorded. Patients were then divided on the basis of their T-scores into normal control, osteopenic, or osteoporotic groups. An AI algorithm based on wavelet features, AdaBoost, and local geometry constraints independently localized thoracic vertebrae from chest CT studies and automatically computed average Hounsfield Unit (HU) values with kVp-dependent spectral correction. The Pearson correlation evaluated the correlation between the T-scores and HU values. Mann-Whitney U test was implemented to compare the HU values of normal control versus osteoporotic patients. RESULTS Overall, the DEXA-determined T-scores and AI-derived HU values showed a moderate correlation (r=0.55; P

PURPOSE Diffusion MRI often suffers from low signal-to-noise ratio, especially for high b-values. This work proposes a model-based denoising technique to address this limitation. METHODS A generalization of the multi-shell spherical deconvolution model using a Richardson-Lucy algorithm is applied to noisy data. The reconstructed coefficients are then used in the forward model to compute denoised diffusion-weighted images (DWIs). The proposed method operates in the diffusion space and thus is complementary to image-based denoising methods. RESULTS We demonstrate improved image quality on the DWIs themselves, maps of neurite orientation dispersion and density imaging, and diffusional kurtosis imaging (DKI), as well as reduced spurious peaks in deterministic tractography. For DKI in particular, we observe up to 50% error reduction and demonstrate high image quality using just 30 DWIs. This corresponds to greater than fourfold reduction in scan time if compared to the widely used 140-DWI acquisitions. We also confirm consistent performance in pathological data sets, namely in white matter lesions of a multiple sclerosis patient. CONCLUSION The proposed denoising technique termed generalized spherical deconvolution has the potential of significantly improving image quality in diffusion MRI. Magn Reson Med 78:2428-2438, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

PURPOSE To compare the effectiveness of prospective, retrospective, and combined (prospective + retrospective) EPI distortion correction methods in bilateral breast diffusion-weighted imaging (DWI) scans. METHODS Five healthy female subjects underwent an axial bilateral breast DWI exam with and without prospective B0 inhomogeneity correction using slice-by-slice linear shimming. In each case, an additional b=0 DWI scan was performed with the polarity of the phase-encoding gradient reversed, to generate an estimated B0 map; this map or a separately acquired B0 map was used for retrospective correction, either alone or in combination with the prospective correction. The alignment between an undistorted, anatomical reference scan with similar contrast and the corrected b=0 DWI images with different correction schemes was assessed. RESULTS The average cross-correlation coefficient between the DWI images and the anatomical reference scan was increased from 0.82 to 0.92 over the five volunteers when combined prospective and retrospective distortion correction was applied. Furthermore, such correction substantially reduced patient-to-patient variation of the image alignment and the variability of the average apparent diffusion coefficient in normal glandular tissue. CONCLUSION Combined prospective and retrospective distortion correction can provide an efficient way to reduce susceptibility-induced image distortions and enhance the reliability of breast DWI exams. Magn Reson Med 78:247-253, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Objective Follow-up of aortic aneurysms by computed tomography (CT) is crucial to balance the risks of treatment and rupture. Artificial intelligence (AI)-assisted radiology reporting promises time savings and reduced inter-reader variabilities. Methods The influence of AI assistance on the efficiency and accuracy of aortic aneurysm reporting according to the AHA / ESC guidelines was quantified based on 324 AI measurements and 1944 radiological measurements: 18 aortic aneurysm patients, each with two CT scans (arterial contrast phase, electrocardiogram-gated) with an interval of at least six months have been included. One board-certified radiologist and two residents (8/4/2 years of experience in vascular imaging) independently assessed aortic diameters at nine landmark positions. Aneurysm extensions were compared with original CT reports. After three weeks washout period, CTs were re-assessed, based on graphically illustrated AI measurements. Results Time-consuming guideline-compliant aortic measurements revealed additional affections of the root / arch for 80 % of aneurysms that had initially been reported to be limited to the ascending aorta. AI assistance reduced mean reporting time by 63 % from 13:01 to 04:46 min including manual corrections of AI measurements (performed for 33.6 % of all measurements with predominance at the sinuses of Vasalva). AI assistance reduced total diameter inter-reader variability by 42.5 % (0.42 / 1.16 mm with / without AI assistance, mean of all patients and landmark positions, significant reduction for 6 out of 9 measuring positions). Conventional and AI-assisted quantification aneurysm progress varied to small extent (mean of 0.75 mm over all patients / landmark positions) not significantly exceeding radiologist’s inter-reader variabilities. Conclusions Guideline-compliant aorta measurement is crucial to report detailed aneurysm extension which might affect the strategy of interventional repair. AI assistance promises improved reporting efficiency and has high potential to reduce radiologist’s inter-reader variabilities that can hamper diagnostic follow-up accuracy. Key point The time-consuming guideline-compliant aorta aneurysm assessment is crucial to report aneurysm extension in detail; AI-assisted measurement reduces reporting time, improves extension evaluation and reduces inter-reader variability.

PURPOSE Because of the intrinsic low signal-to-noise ratio in diffusion-weighted imaging (DWI), magnitude processing often causes an overestimation of the signal's amplitude. This results in low-estimation accuracy of diffusion models and reduced contrast because of a superposition of the image signal and the noise floor. We adopt a new phase correction (PC) technique that yields real valued diffusion data while maintaining a Gaussian noise distribution. METHODS We conduct simulations of the noise propagation in the echo-planar imaging reconstruction chain to determine the spatial noise correlation in the image. Using the correlation pattern, optimized filter kernels are derived to estimate the true phase of the signal in each voxel. Furthermore, we adopt an outlier detection technique to replace the real value by the magnitude in case of substantial signal loss resulting from incorrect PC. RESULTS The benefits of our method are demonstrated on Monte Carlo simulations, DWI data acquired from healthy volunteer experiments, estimated parameters of the diffusion kurtosis imaging model, and the model-free diffusion spectrum imaging technique. The improved PC approach significantly reduces the noise bias and only slightly increases the sensitivity to local phase variations. CONCLUSION PC can enhance the usefulness of higher b-values, allowing deeper insights into tissue microstructure. Magn Reson Med 77:559-570, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Purpose Diffusional kurtosis imaging (DKI) is an approach to characterizing the non-Gaussian fraction of water diffusion in biological tissue. However, DKI is highly susceptible to the low signal-to-noise ratio of diffusion-weighted images, causing low precision and a significant bias due to Rician noise distribution. Here, we evaluate precision and bias using weighted linear least squares fitting of different acquisition schemes including several multishell schemes, a diffusion spectrum imaging (DSI) scheme, as well as a compressed sensing reconstruction of undersampled DSI scheme. Methods Monte Carlo simulations were performed to study the three-dimensional distribution of the apparent kurtosis coefficient (AKC). Experimental data were acquired from one healthy volunteer with multiple repetitions, using the same acquisition schemes as for the simulations. Results The angular distribution of the bias and precision were very inhomogeneous. While axial kurtosis was significantly overestimated, radial kurtosis was underestimated. The precision of radial kurtosis was up to 10-fold lower than axial kurtosis. Conclusion The noise bias behavior of DKI is highly complex and can cause overestimation as well as underestimation of the AKC even within one voxel. The acquisition scheme with three shells, suggested by Poot et al, provided overall the best performance. Magn Reson Med 76:1684–1696, 2016. © 2016 International Society for Magnetic Resonance in Medicine

PURPOSE To evaluate a model-independent, multi-directional anisotropy (MDA) metric that is analytically and experimentally equivalent to fractional anisotropy (FA) in single-direction diffusivity, but potentially superior to FA in its sensitivity to the underlying anisotropy of multi-directional diffusivity. MATERIALS AND METHODS An expression for MDA was defined from the orientation distribution function (ODF) and its analytical relation to FA was derived. Simulations of single and crossed double-fibers were performed using a compressed-sensing-accelerated diffusion-spectrum-imaging (CS-DSI) scheme. In vivo brain imaging using CS-DSI was performed on eight healthy subjects. MDA was compared with FA and with another ODF-based metric known as generalized FA (GFA). RESULTS In simulated single-direction fibers, MDA was shown to be equivalent to FA (from FA = 0.2 to 0.8). In crossed fibers, MDA provided superior differentiation of the underlying anisotropy as compared to FA and GFA. In vivo analysis shows that the MDA was superior to both FA (P = 0.015) and GFA (P = 0.021) in terms of its relative accuracy in crossed fiber regions. CONCLUSION MDA provides a potentially superior measure of fiber anisotropy relative to conventional FA or GFA, and may be used to improve the assessment of disease in regions with multi-directional brain fibers.

To compare compressed diffusion spectrum imaging (CS-DSI) with diffusion tensor imaging (DTI) in patients with intracranial masses. We hypothesized that CS-DSI would provide superior visualization of the motor and language tracts.We retrospectively analyzed 25 consecutive patients with intracranial masses who underwent DTI and CS-DSI for preoperative planning. Directionally-encoded anisotropy maps, and streamline hand corticospinal motor tracts and arcuate fasciculus language tracts were graded according to a 3-point scale. Tract counts, anisotropy, and lengths were also calculated. Comparisons were made using exact marginal homogeneity, McNemar's and Wilcoxon signed-rank tests.Readers preferred the CS-DSI over DTI anisotropy maps in 92% of the cases, and the CS-DSI over DTI tracts in 84%. The motor tracts were graded as excellent in 80% of cases for CS-DSI versus 52% for DTI; 58% of the motor tracts graded as acceptable in DTI were graded as excellent in CS-DSI (p=0.02). The language tracts were graded as excellent in 68% for CS-DSI versus none for DTI; 78% of the language tracts graded as acceptable by DTI were graded as excellent by CS-DSI (p0.001). CS-DSI demonstrated smaller normalized mean differences than DTI for motor tract counts, anisotropy and language tract counts (p≤0.01).CS-DSI was preferred over DTI for the evaluation of motor and language white matter tracts in patients with intracranial masses. Results suggest that CS-DSI may be more useful than DTI for preoperative planning purposes.

Diffusion MRI often suffers from low signal-to-noise ratio, especially for high b-values. This work proposes a model-based denoising technique to address this limitation.A generalization of the multi-shell spherical deconvolution model using a Richardson-Lucy algorithm is applied to noisy data. The reconstructed coefficients are then used in the forward model to compute denoised diffusion-weighted images (DWIs). The proposed method operates in the diffusion space and thus is complementary to image-based denoising methods.We demonstrate improved image quality on the DWIs themselves, maps of neurite orientation dispersion and density imaging, and diffusional kurtosis imaging (DKI), as well as reduced spurious peaks in deterministic tractography. For DKI in particular, we observe up to 50% error reduction and demonstrate high image quality using just 30 DWIs. This corresponds to greater than fourfold reduction in scan time if compared to the widely used 140-DWI acquisitions. We also confirm consistent performance in pathological data sets, namely in white matter lesions of a multiple sclerosis patient.The proposed denoising technique termed generalized spherical deconvolution has the potential of significantly improving image quality in diffusion MRI. Magn Reson Med 78:2428-2438, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

X-Ray Phase-Contrast (XPC) imaging is a novel technology with a great potential for applications in clinical practice, with breast imaging being of special interest. This work introduces an intuitive methodology to combine and visualize relevant diagnostic features, present in the X-ray attenuation, phase shift and scattering information retrieved in XPC imaging, using a Fourier domain fusion algorithm. The method allows to present complementary information from the three acquired signals in one single image, minimizing the noise component and maintaining visual similarity to a conventional X-ray image, but with noticeable enhancement in diagnostic features, details and resolution. Radiologists experienced in mammography applied the image fusion method to XPC measurements of mastectomy samples and evaluated the feature content of each input and the fused image. This assessment validated that the combination of all the relevant diagnostic features, contained in the XPC images, was present in the fused image as well.

Many limitations of diffusion MRI are due to the instability of the model fitting procedure. Major shortcomings of the model-based approach are a partial information loss due to model simplicity, long scan time requirements due to fitting instability, and the lack of knowledge about how the parameters of a given model would respond to previously unseen microstructural changes, possibly failing to detect certain previously unseen pathologies. Here we show that diffusion MRI pathology detection is feasible without any models and without any prior knowledge of specific pathological changes whatsoever. Instead, raw q-space measurements are used directly without a model, only healthy population data is used for reference, and any deviations in a patient dataset from the healthy reference database are detected using novelty detection methods. This is done in each voxel independently, i.e. without spatial bias.

To compare the effectiveness of prospective, retrospective, and combined (prospective + retrospective) EPI distortion correction methods in bilateral breast diffusion-weighted imaging (DWI) scans.Five healthy female subjects underwent an axial bilateral breast DWI exam with and without prospective BThe average cross-correlation coefficient between the DWI images and the anatomical reference scan was increased from 0.82 to 0.92 over the five volunteers when combined prospective and retrospective distortion correction was applied. Furthermore, such correction substantially reduced patient-to-patient variation of the image alignment and the variability of the average apparent diffusion coefficient in normal glandular tissue.Combined prospective and retrospective distortion correction can provide an efficient way to reduce susceptibility-induced image distortions and enhance the reliability of breast DWI exams. Magn Reson Med 78:247-253, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Within the last decade hyperpolarized [1-13C] pyruvate chemical-shift imaging has demonstrated impressive potential for metabolic MR imaging for a wide range of applications in oncology, cardiology, and neurology. In this work, a highly efficient pulse sequence is described for time-resolved, multislice chemical shift imaging of the injected substrate and obtained downstream metabolites. Using spectral-spatial excitation in combination with single-shot spiral data acquisition, the overall encoding is evenly distributed between excitation and signal reception, allowing the encoding of one full two-dimensional metabolite image per excitation. The signal-to-noise ratio can be flexibly adjusted and optimized using lower flip angles for the pyruvate substrate and larger ones for the downstream metabolites. Selectively adjusting the excitation of the down-stream metabolites to 90° leads to a so-called "saturation-recovery" scheme with the detected signal content being determined by forward conversion of the available pyruvate. In case of repetitive excitations, the polarization is preserved using smaller flip angles for pyruvate. Metabolic exchange rates are determined spatially resolved from the metabolite images using a simplified two-site exchange model. This novel contrast is an important step toward more quantitative metabolic imaging. Goal of this work was to derive, analyze, and implement this "saturation-recovery metabolic exchange rate imaging" and demonstrate its capabilities in four rats bearing subcutaneous tumors.

We developed a novel method to accelerate diffusion spectrum imaging using compressed sensing. The method can be applied to either reduce acquisition time of diffusion spectrum imaging acquisition without losing critical information or to improve the resolution in diffusion space without increasing scan time. Unlike parallel imaging, compressed sensing can be applied to reconstruct a sub-Nyquist sampled dataset in domains other than the spatial one. Simulations of fiber crossings in 2D and 3D were performed to systematically evaluate the effect of compressed sensing reconstruction with different types of undersampling patterns (random, gaussian, Poisson disk) and different acceleration factors on radial and axial diffusion information. Experiments in brains of healthy volunteers were performed, where diffusion space was undersampled with different sampling patterns and reconstructed using compressed sensing. Essential information on diffusion properties, such as orientation distribution function, diffusion coefficient, and kurtosis is preserved up to an acceleration factor of R = 4. Magn Reson Med, 2011. © 2011 Wiley Periodicals, Inc.

Differential phase-contrast X-ray imaging using a Talbot-Lau interferometer has recently shown promising results for applications in medical imaging. However, reducing the applied radiation dose remains a major challenge. In this study, we consider the realization of a Talbot-Lau interferometer in a high Talbot order to increase the signal-to-noise ratio for low-dose applications. The quantitative performance of π and π/2 systems at high Talbot orders is analyzed through simulations, and the design energy and X-ray spectrum are optimized for mammography. It is found that operation even at very high Talbot orders is feasible and beneficial for image quality. As long as the X-ray spectrum is matched to the visibility spectrum, the SNR continuously increases with the Talbot order for π-systems. We find that the optimal X-ray spectra and design energies are almost independent of the Talbot order and that the overall imaging performance is robust against small variations in these parameters. Discontinuous spectra, such as that from molybdenum, are less robust because the characteristic lines may coincide with minima in the visibility spectra; however, they may offer slightly better performance. We verify this hypothesis by realizing a prototype system with a mean fringe visibility of above 40% at the seventh Talbot order. With this prototype, a proof-of-principle measurement of a freshly dissected breast at reasonable compression to 4 cm is conducted with a mean glandular dose of only 3 mGy but with a high SNR.

This note summarizes the characterization of the acoustic properties of four materials intended for the development of tissue, and especially breast tissue, phantoms for the use in photoacoustic and ultrasound imaging. The materials are agar, silicone, polyvinyl alcohol gel (PVA) and polyacrylamide gel (PAA). The acoustical properties, i.e., the speed of sound, impedance and acoustic attenuation, are determined by transmission measurements of sound waves at room temperature under controlled conditions. Although the materials are tested for application such as photoacoustic phantoms, we focus here on the acoustic properties, while the optical properties will be discussed elsewhere. To obtain the acoustic attenuation in a frequency range from 4 MHz to 14 MHz, two ultrasound sources of 5 MHz and 10 MHz core frequencies are used. For preparation, each sample is cast into blocks of three different thicknesses. Agar, PVA and PAA show similar acoustic properties as water. Within silicone polymer, a significantly lower speed of sound and higher acoustical attenuation than in water and human tissue were found. All materials can be cast into arbitrary shapes and are suitable for tissue-mimicking phantoms. Due to its lower speed of sound, silicone is generally less suitable than the other presented materials.

X-ray grating interferometry requires gratings with periods in the micrometer range and allows the acquisition of the dark-field contrast. The analyzer grating is designed to match the period of the interference pattern in order to translate it into a measurable intensity modulation. In this study, we explore the influence of a sample-induced mismatch between the interference pattern and the analyzer grating on the dark-field contrast. We propose a formula for the calculation of the signal due to a period mismatch and present estimations varying periods and detector pixel size. Furthermore, numerical simulations of the X-ray wave-front demonstrate that the wave-front curvature, described by the lens-term, e.g. behind a parabolic lens or edges of a sample can change the period of the interference pattern. Our results give a concrete explanation for the formation of a dark-field contrast from object edges and thus allow a better understanding of the dark-field signal obtained with a grating interferometer.

Purpose Diffusion spectrum imaging (DSI) is an imaging technique that has been successfully applied to resolve white matter crossings in the human brain. However, its accuracy in complex microstructure environments has not been well characterized. Theory and Methods Here we have simulated different tissue configurations, sampling schemes, and processing steps to evaluate DSI performances' under realistic biophysical conditions. A novel approach to compute the orientation distribution function (ODF) has also been developed to include biophysical constraints, namely integration ranges compatible with axial fiber diffusivities. Results Performed simulations identified several DSI configurations that consistently show aliasing artifacts caused by fast diffusion components for both isotropic diffusion and fiber configurations. The proposed method for ODF computation showed some improvement in reducing such artifacts and improving the ability to resolve crossings, while keeping the quantitative nature of the ODF. Conclusion In this study, we identified an important limitation of current DSI implementations, specifically the presence of aliasing due to fast diffusion components like those from pathological tissues, which are not well characterized, and can lead to artifactual fiber reconstructions. To minimize this issue, a new way of computing the ODF was introduced, which removes most of these artifacts and offers improved angular resolution. Magn Reson Med 76:1837–1847, 2016. © 2015 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Diffusional kurtosis imaging (DKI) is an approach to characterizing the non-Gaussian fraction of water diffusion in biological tissue. However, DKI is highly susceptible to the low signal-to-noise ratio of diffusion-weighted images, causing low precision and a significant bias due to Rician noise distribution. Here, we evaluate precision and bias using weighted linear least squares fitting of different acquisition schemes including several multishell schemes, a diffusion spectrum imaging (DSI) scheme, as well as a compressed sensing reconstruction of undersampled DSI scheme.Monte Carlo simulations were performed to study the three-dimensional distribution of the apparent kurtosis coefficient (AKC). Experimental data were acquired from one healthy volunteer with multiple repetitions, using the same acquisition schemes as for the simulations.The angular distribution of the bias and precision were very inhomogeneous. While axial kurtosis was significantly overestimated, radial kurtosis was underestimated. The precision of radial kurtosis was up to 10-fold lower than axial kurtosis.The noise bias behavior of DKI is highly complex and can cause overestimation as well as underestimation of the AKC even within one voxel. The acquisition scheme with three shells, suggested by Poot et al, provided overall the best performance. Magn Reson Med 76:1684-1696, 2016. © 2016 International Society for Magnetic Resonance in Medicine.

Numerical wave-optical simulations of X-ray differential phase-contrast imaging using grating interferometry require the oversampling of gratings and object structures in the range of few micrometers. Consequently, fields of view of few millimeters already use large amounts of a computer’s main memory to store the propagating wave front, limiting the scope of the investigations to only small-scale problems. In this study, we apply an approximation to the Fresnel-Kirchhoff diffraction theory to overcome these restrictions by dividing the two-dimensional wave front up into 1D lines, which are processed separately. The approach enables simulations with samples of clinically relevant dimensions by significantly reducing the memory footprint and the execution time and, thus, allows the qualitative comparison of different setup configurations. We analyze advantages as well as limitations and present the simulation of a virtual mammography phantom of several centimeters of size.

Hyperpolarized13C imaging allows real-timein vivomeasurements of metabolite levels. Quantification of metabolite conversion between [1-13C]pyruvate and downstream metabolites [1-13C]alanine, [1-13C]lactate, and [13C]bicarbonate can be achieved through kinetic modeling. Since pyruvate interacts dynamically and simultaneously with its downstream metabolites, the purpose of this work is the determination of parameter values through a multisite, dynamic model involving possible biochemical pathways present in MR spectroscopy. Kinetic modeling parameters were determined by fitting the multisite model to time-domain dynamic metabolite data. The results for different pyruvate doses were compared with those of different two-site models to evaluate the hypothesis that for identical data the uncertainty of a model and the signal-to-noise ratio determine the sensitivity in detecting small physiological differences in the target metabolism. In comparison to the two-site exchange models, the multisite model yielded metabolic conversion rates with smaller bias and smaller standard deviation, as demonstrated in simulations with different signal-to-noise ratio. Pyruvate dose effects observed previously were confirmed and quantified through metabolic conversion rate values. Parameter interdependency allowed an accurate quantification and can therefore be useful for monitoring metabolic activity in different tissues.

X-ray phase-contrast and dark-field imaging are two new modalities that have great potential for applications in different fields like medical diagnostics or materials science. The use of grating interferometers allows the detection of both differential phase shift and dark-field signal together with the absorption signal in a single acquisition. We present wave-optical simulations to quantitatively analyze the response of a grating-based X-ray phase-contrast and dark-field imaging setup to variations of the sample relative to the system. Specifically, we investigated changes in the size and the position of the object. Furthermore, we examined the influence of different detector pixel sizes while sample and interferometer remained unchanged. The results of this study contribute to a better understanding of the signal formation and represent a step towards the full characterization of the response of grating interferometry setups to specific sample geometries.

Phase retrieval in differential X-ray phase contrast imaging involves a one dimensional integration step. In the presence of noise, standard integration methods result in image blurring and streak artifacts. This work proposes a regularized integration method which takes the availability of two dimensional data as well as the integration-specific frequency-dependent noise amplification into account. In more detail, a Fourier-domain algorithm is developed comprising a frequency-dependent minimization of the total variation orthogonal to the direction of integration. For both simulated and experimental data, the novel method yielded strong artefact reduction without increased blurring superior to the results obtained by standard integration methods or regularization techniques in the image domain.

Recently, super-resolution methods for diffusion MRI capable of retrieving high-resolution diffusion-weighted images were proposed, yielding a resolution beyond the scanner hardware limitations. These techniques rely on acquiring either one isotropic or several anisotropic low-resolution versions of each diffusion-weighted image. In the present work, a variational formulation of joint super-resolution of all diffusion-weighted images is presented which takes advantage of interrelations between similar diffusion-weighted images. These interrelations allow to use only one anisotropic low-resolution version of each diffusion-weighted image and to retrieve its missing high-frequency components from other images which have a similar q-space coordinate but a different resolution-anisotropy orientation. An acquisition scheme that entails complementary resolution-anisotropy among neighboring q-space points is introduced. High-resolution images are recovered at reduced scan time requirements compared to state-of-the-art anisotropic super-resolution methods. The introduced principles of joint super-resolution thus have the potential to further improve the performance of super-resolution methods.

To evaluate a model-independent, multi-directional anisotropy (MDA) metric that is analytically and experimentally equivalent to fractional anisotropy (FA) in single-direction diffusivity, but potentially superior to FA in its sensitivity to the underlying anisotropy of multi-directional diffusivity.An expression for MDA was defined from the orientation distribution function (ODF) and its analytical relation to FA was derived. Simulations of single and crossed double-fibers were performed using a compressed-sensing-accelerated diffusion-spectrum-imaging (CS-DSI) scheme. In vivo brain imaging using CS-DSI was performed on eight healthy subjects. MDA was compared with FA and with another ODF-based metric known as generalized FA (GFA).In simulated single-direction fibers, MDA was shown to be equivalent to FA (from FA = 0.2 to 0.8). In crossed fibers, MDA provided superior differentiation of the underlying anisotropy as compared to FA and GFA. In vivo analysis shows that the MDA was superior to both FA (P = 0.015) and GFA (P = 0.021) in terms of its relative accuracy in crossed fiber regions.MDA provides a potentially superior measure of fiber anisotropy relative to conventional FA or GFA, and may be used to improve the assessment of disease in regions with multi-directional brain fibers.

An X-ray phase contrast grating interferometer consists of three gratings in the beam line to generate interference patterns and acquire phase shift information if an object is placed into the beam. These gratings have a pitch of a few micrometers and therefore very accurate alignment is needed to acquire phase information reliably. In this work we have investigated different setup parameters and environmental influences on the detected signal quality by Six Sigma methods. We have used this information to optimize our setup and established a semi-automatic alignment procedure to reproduce a stable signal quality.

Within the last decade hyperpolarized [1-13C] pyruvate chemical-shift imaging has demonstrated impressive potential for metabolic MR imaging for a wide range of applications in oncology, cardiology, and neurology. In this work, a highly efficient pulse sequence is described for time-resolved, multislice chemical shift imaging of the injected substrate and obtained downstream metabolites. Using spectral-spatial excitation in combination with single-shot spiral data acquisition, the overall encoding is evenly distributed between excitation and signal reception, allowing the encoding of one full two-dimensional metabolite image per excitation. The signal-to-noise ratio can be flexibly adjusted and optimized using lower flip angles for the pyruvate substrate and larger ones for the downstream metabolites. Selectively adjusting the excitation of the down-stream metabolites to 90° leads to a so-called "saturation-recovery" scheme with the detected signal content being determined by forward conversion of the available pyruvate. In case of repetitive excitations, the polarization is preserved using smaller flip angles for pyruvate. Metabolic exchange rates are determined spatially resolved from the metabolite images using a simplified two-site exchange model. This novel contrast is an important step toward more quantitative metabolic imaging. Goal of this work was to derive, analyze, and implement this "saturation-recovery metabolic exchange rate imaging" and demonstrate its capabilities in four rats bearing subcutaneous tumors.

We developed a novel method to accelerate diffusion spectrum imaging using compressed sensing. The method can be applied to either reduce acquisition time of diffusion spectrum imaging acquisition without losing critical information or to improve the resolution in diffusion space without increasing scan time. Unlike parallel imaging, compressed sensing can be applied to reconstruct a sub-Nyquist sampled dataset in domains other than the spatial one. Simulations of fiber crossings in 2D and 3D were performed to systematically evaluate the effect of compressed sensing reconstruction with different types of undersampling patterns (random, gaussian, Poisson disk) and different acceleration factors on radial and axial diffusion information. Experiments in brains of healthy volunteers were performed, where diffusion space was undersampled with different sampling patterns and reconstructed using compressed sensing. Essential information on diffusion properties, such as orientation distribution function, diffusion coefficient, and kurtosis is preserved up to an acceleration factor of R = 4.

X-ray computed tomography is a powerful medical imaging device. It allows high-resolution 3-D visualization of the human body. However, one drawback is the health risk associated with ionizing radiation. Simply downscaling the radiation intensities over the entire scan results in increased quantum noise. This paper proposes the concept of computer-assisted scan protocol and reconstruction. More specifically, we propose a method to compute patient and task-specific intensity profiles that achieve an optimal tradeoff between radiation dose and image quality. Therefore, reasonable image variance and dose metrics are derived. Conventional third-generation systems as well as inverted geometry concepts are considered. Two dose/noise minimization problems are formulated and solved by an efficient algorithm providing optimized milliampere (mA)-profiles. Thorax phantom simulations demonstrate the promising advantage of this technique: in this particular example, the dose is reduced by 53% for third-generation systems and by 86% for an inverted geometry in comparison to a sinusoidal mA-profile at a constant upper noise limit.

The OPUS (Optoacoustic plus Ultrasound) system is a combination of a medical ultrasound scanner with a highrepetition rate, wavelength-tunable laser system and a suitable triggering interface to synchronize the laser and the ultrasound system. The pulsed laser generates an optoacoustic (OA), or photoacoustic (PA), signal which is detected by the ultrasound system. Alternatively, imaging in conventional ultrasound mode can be performed. Both imaging modes can be superimposed. The laser light is coupled into the tissue laterally, parallel to the ultrasound transducer, which does not require for any major modification to the transducer or the ultrasound beam forming. This was a basic requirement on the instrument, as the intention of the project was to establish the optoacoustic imaging modality as add-on to a conventional standard ultrasound instrument. We believe that this approach may foster the introduction of OA imaging as routine tool in medical diagnosis. Another key aspect of the project was to exploit the capabilities of OA imaging for quantitative analysis. The intention of the presented work is to summarize all steps necessary to extract the significant information from the PA raw data, which are required for the quantification of local absorber distributions. We show results of spatially resolved absorption measurements in scattering samples and a comparison of four different image reconstruction algorithms, regarding their influence on lateral resolution as well as on the signal to noise ratio for different sample depths and absorption values. The reconstruction algorithms are based on Fourier transformation, on a generalized 2D Hough transformation, on circular back-projection and the classical delay-and-sum approach which is implemented in most ultrasound scanners. Furthermore, we discuss the influence of a newly developed laser source, combining diode and flash lamp pumping. Compared to all-flash-lamp pumped systems it features a significantly higher pulse-to-pulse stability, which is required for sensitive and precise quantitative analyses.

Photoacoustic imaging is a promising new way to generate unprecedented contrast in ultrasound diagnostic imaging. It differs from other medical imaging approaches, in that it provides spatially resolved information about optical absorption of targeted tissue structures. Because the data acquisition process deviates from standard clinical ultrasound, choice of the proper image reconstruction method is crucial for successful application of the technique. In the literature, multiple approaches have been advocated, and the purpose of this paper is to compare four reconstruction techniques. Thereby, we focused on resolution limits, stability, reconstruction speed, and SNR. We generated experimental and simulated data and reconstructed images of the pressure distribution using four different methods: delay-and-sum (DnS), circular backprojection (CBP), generalized 2D Hough transform (HTA), and Fourier transform (FTA). All methods were able to depict the point sources properly. DnS and CBP produce blurred images containing typical superposition artifacts. The HTA provides excellent SNR and allows a good point source separation. The FTA is the fastest and shows the best FWHM. In our study, we found the FTA to show the best overall performance. It allows a very fast and theoretically exact reconstruction. Only a hardware-implemented DnS might be faster and enable real-time imaging. A commercial system may also perform several methods to fully utilize the new contrast mechanism and guarantee optimal resolution and fidelity.

Many scientific research areas rely on accurate electron density characterization of various materials. For instance in X-ray optics and radiation therapy, there is a need for a fast and reliable technique to quantitatively characterize samples for electron density. We present how a precise measurement of electron density can be performed using an X-ray phase-contrast grating interferometer in a radiographic mode of a homogenous sample in a controlled geometry. A batch of various plastic materials was characterized quantitatively and compared with calculated results. We found that the measured electron densities closely match theoretical values. The technique yields comparable results between a monochromatic and a polychromatic X-ray source. Measured electron densities can be further used to design dedicated X-ray phase contrast phantoms and the additional information on small angle scattering should be taken into account in order to exclude unsuitable materials.

In this paper, we propose a new spatio-temporal reconstruction scheme for the fast reconstruction of dynamic magnetic resonance imaging (dMRI) data from undersampled k-space measurements. To utilize both spatial and temporal redundancy in dMRI sequences, our method investigates the potential benefits of enforcing local spatial sparsity constraints on the difference to a reference image for each frame and additionally exploiting the low-rank property of global spatiotemporal signal via nuclear norm (NN) minimization. We present here an iterative algorithm that solves the convex optimization problem in an alternating fashion. The proposed method is tested on in-vivo 3D cardiac MRI and dynamic susceptibility contrast (DSC)-MRI brain perfusion datasets. In comparison to two state-of-the-art methods, numerical experiments demonstrate the superior performance of our method in terms of reconstruction accuracy.

Besides x-ray imaging, sonography is the most common method for breast cancer screening. The intention of our work is to develop optoacoustical imaging as an add-on to a conventional system. While ultrasound imaging reveals acoustical properties of tissue, optoacoustics generates an image of the distribution of optical absorption. Hence, it can be a valuable addition to sonography, because acoustical properties of different tissues show only a slight variation whereas the optical properties may differ strongly. Additionally, optoacoustics gives access to physiological parameters, like oxygen saturation of blood. For the presented work, we combine a conventional ultrasound system to a 100 Hz laser. The laser system consists of a Nd:YAG-laser at a wavelength of 532 nm with 7 ns pulse duration, coupled to a tunable Optical Parametric Oscillator (OPO) with a tuning rage from 680 nm to 2500 nm. The tunable laser source allows the selection of wavelengths which compromising high spectral information content with high skin transmission. The laser pulse is delivered fiber-optically to the ultrasound transducer and coupled into the acoustical field of view. Homogeneous illumination is crucial in order to achieve unblurred images. Furthermore the maximum allowed pulse intensities in accordance with standards for medical equipment have to be met to achieve a high signal to noise ration. The ultrasound instrument generates the trigger signal which controls the laser pulsing in order to apply ultrasound instrument's imaging procedures without major modifications to generate an optoacoustic image. Detection of the optoacoustic signal as well as of the classical ultrasound signal is carried out by the standard medical ultrasound transducer. The characterization of the system, including quantitative measurements, performed on tissue phantoms, is presented. These phantoms have been specially designed regarding their acoustical as well as their optical properties.