Purpose: We investigate how an intrinsic speckle tracking approach to speckle-based x-ray imaging is used to extract an object’s effective dark-field (DF) signal, which is capable of providing object information in three dimensions.

Approach: The effective DF signal was extracted using a Fokker–Planck type formalism, which models the deformations of illuminating reference beam speckles due to both coherent and diffusive scatter from the sample. Here, we assumed that (a) small-angle scattering fans at the exit surface of the sample are rotationally symmetric and (b) the object has both attenuating and refractive properties. The associated inverse problem of extracting the effective DF signal was numerically stabilized using a “weighted determinants” approach.

Results: Effective DF projection images, as well as the DF tomographic reconstructions of the wood sample, are presented. DF tomography was performed using a filtered back projection reconstruction algorithm. The DF tomographic reconstructions of the wood sample provided complementary, and otherwise inaccessible, information to augment the phase contrast reconstructions, which were also computed.

Conclusions: An intrinsic speckle tracking approach to speckle-based imaging can tomographically reconstruct an object’s DF signal at a low sample exposure and with a simple experimental setup. The obtained DF reconstructions have an image quality comparable to alternative x-ray DF techniques.

Purpose: Surgery is an essential part of the curative plan for most patients affected with solid tumors. The outcome of such surgery, e.g., recurrence rates and ultimately patient survival, depends on several factors where the resection margin is of key importance. Presently, the resection margin is assessed by classical histology, which is time-consuming (several days), destructive, and basically only gives two-dimensional information. Clearly, it would be advantageous if immediate feedback on tumor extension in all three dimensions were available to the surgeon intraoperatively.

Approach: We investigate a laboratory propagation-based phase-contrast x-ray computed tomography system that provides the resolution, the contrast, and, potentially, the speed for this purpose. The system relies on a liquid-metal jet microfocus source and a scintillator-coated CMOS detector. Our study is performed on paraffin-embedded non-stained samples of human pancreatic neuroendocrine tumors, liver intrahepatic cholangiocarcinoma, and pancreatic serous cystic neoplasm (benign).

Results: We observe tumors with distinct and sharp edges having cellular resolution (∼10  μm) as well as many assisting histological landmarks, allowing for resection margin assessment. All x-ray data are compared with classical histology. The agreement is excellent.

Conclusion: We conclude that the method has potential for intraoperative three-dimensional virtual histology.

Purpose: Tomography using diffracted x-rays produces reconstructions mapping quantities such as crystal lattice parameter(s), crystallite size, and crystallographic texture, information quite different from that obtained with absorption or phase contrast. Diffraction tomography is used to map an entire blue shark centrum with its double cone structure (corpora calcerea) and intermedialia (four wedges).

Approach: Energy dispersive diffraction (EDD) and polychromatic synchrotron x-radiation at 6-BM-B, the Advanced Photon Source, were used. Different, properly oriented Bragg planes diffract different x-ray energies; these intensities are measured by one of ten energy-sensitive detectors. A pencil beam defines the irradiated volume, and a collimator before each energy-sensitive detector selects which portion of the irradiated column is sampled at any one time. Translating the specimen along X  ,  Y, and Z axes produces a 3D map.

Results: We report 3D maps of the integrated intensity of several bioapatite reflections from the mineralized cartilage centrum of a blue shark. The c axis reflection’s integrated intensities and those of a reflection with no c axis component reveal that the cone wall’s bioapatite is oriented with its c axes lateral, i.e., perpendicular to the backbone’s axis, and that the wedges’ bioapatite is oriented with its c axes axial. Absorption microcomputed tomography (laboratory and synchrotron) and x-ray excited x-ray fluorescence maps provide higher resolution views.

Conclusion: The bioapatite in the cone walls and wedges is oriented to resist lateral and axial deflections, respectively. Mineralized tissue samples can be mapped in 3D with EDD tomography and subsequently studied by destructive methods.

Purpose: Dental calculus forms on teeth during the life of an individual and its investigation can yield information about diet, health status, and environmental pollution. Currently, the analytical techniques used to visualize the internal structure of human dental calculus and entrapped inclusions are limited and require destructive sampling, which cannot always be justified.

Approach: We used propagation phase-contrast synchrotron radiation micro-computed tomography (PPC-SR-μCT) to non-destructively examine the internal organization of dental calculus, including its microstructure and entrapped inclusions, on both modern and archeological samples.

Results: The virtual histological exploration of the samples shows that PPC-SR-μCT is a powerful approach to visualize the internal organization of dental calculus. We identified several important features, including previously undetected negative imprints of enamel and dentine growth markers (perikymata and periradicular bands, respectively), the non-contiguous structure of calculus layers with multiple voids, and entrapped plant remains.

Conclusions: PPC-SR-μCT is an effective technique to explore dental calculus structural organization, and is especially powerful for enabling the identification of inclusions. The non-destructive nature of synchrotron tomography helps protect samples for future research. However, the irregular layers and frequent voids reveal a high heterogeneity and variability within calculus, with implications for research focusing on inclusions.

Purpose: Propagation-based x-ray imaging (PBI) is a phase-contrast technique that is employed in high-resolution imaging by introducing some distance between sample and detector. PBI causes characteristic intensity fringes that have to be processed with appropriate phase-retrieval algorithms, which has historically been a difficult task for objects composed of several different materials. Spectral x-ray imaging has been introduced to PBI to overcome this issue and to potentially utilize the spectral nature of the data for material-specific imaging. We aim to explore the potential of spectral PBI in three-dimensional computed tomography (CT) imaging in this work.

Approach: We demonstrate phase-retrieval for experimental high-resolution spectral propagation-based CT data of a simple two-component sample, as well as a multimaterial capacitor test sample. Phase-retrieval was performed using an algorithm based on the Alvarez–Macovski model. Virtual monochromatic (VMI) and effective atomic number images were calculated after phase-retrieval.

Results: Phase-retrieval results from the spectral data set show a distinct gray-level for each material with no residual phase-contrast fringes. Several representations of the phase-retrieved data are provided. The VMI is used to display an attenuation-equivalent image at a chosen display energy of 80 keV, to provide good separation of materials with minimal noise. The effective atomic number image shows the material composition of the sample.

Conclusions: Spectral photon-counting detector technology has already been shown to be compatible with spectral PBI, and there is a foreseeable need for robust phase-retrieval in high-resolution, spectral x-ray CT in the future. Our results demonstrate the feasibility of phase-retrieval for spectral PBI CT.

Purpose: Synchrotron radiation-based tomography yields microanatomical features in human and animal tissues without physical slicing. Recent advances in instrumentation have made laboratory-based phase tomography feasible. We compared the performance of three cutting-edge laboratory systems benchmarked by synchrotron radiation-based tomography for three specimens. As an additional criterion, the user-friendliness of the three microtomography systems was considered.

Approach: The three tomography systems—SkyScan 2214 (Bruker-microCT, Kontich, Belgium), Exciscope prototype (Stockholm, Sweden), and Xradia 620 Versa (Zeiss, Oberkochen, Germany)—were given 36 h to measure three medically relevant specimens, namely, zebrafish larva, archaeological human tooth, and porcine nerve. The obtained datasets were registered to the benchmark synchrotron radiation-based tomography from the same specimens and selected ones to the SkyScan 1275 and phoenix nanotom m^® laboratory systems to characterize development over the last decade.

Results: Next-generation laboratory-based microtomography almost reached the quality achieved by synchrotron-radiation facilities with respect to spatial and density resolution, as indicated by the visualization of the medically relevant microanatomical features. The SkyScan 2214 system and the Exciscope prototype demonstrated the complementarity of phase information by imaging the eyes of the zebrafish larva. The 3-μm thin annual layers in the tooth cementum were identified using Xradia 620 Versa.

Conclusions: SkyScan 2214 was the simplest system and was well-suited to visualizing the wealth of anatomical features in the zebrafish larva. Data from the Exciscope prototype with the high photon flux from the liquid metal source showed the spiral nature of the myelin sheaths in the porcine nerve. Xradia 620 Versa, with detector optics as typically installed for synchrotron tomography beamlines, enabled the three-dimensional visualization of the zebrafish larva with comparable quality to the synchrotron data and the annual layers in the tooth cementum.

Purpose: Existing maximum-likelihood (ML) methods in computed tomography usually require significant computing resources to implement, and/or are limited to particular measurement noise models that are representative of the simplest theoretical archetypes. There is an absence of general procedures to produce rapid ML methods that account precisely for the noise model of a given experiment. We investigate a mathematical-computational procedure of producing constrained quadratic optimization reconstruction algorithms that fill this niche, requiring less computing resources than the exact (expectation-maximization) procedures and having comparable performance with least-squares iterative methods. This allows high-fidelity reconstructions to be practically achievable for largely arbitrary noise models.

Approach: We identify a systematic mathematical procedure to produce constrained quadratic optimization methods that maximize tomogram likelihood under arbitrary noise models, which are tunable to specific characteristics of the experiment. This procedure is applied to a general theory of mixed Poisson–Gaussian noise in transmission tomography, and to a theory of invertible linear transformations of measurement intensity subject to Poisson noise. We perform tomographic reconstructions of a very highly attenuating two-dimensional object phantom and compare the speed and fidelity of reconstruction with alternative quadratic metrics (ℓ²—minimization among others).

Results: Quantitative metrics reveal that reconstructions under our systematically produced quadratic methods achieved significantly greater reconstruction fidelity with less computation than the optimized conventional, untuned quadratic metrics with a comparable procedure.

Conclusion: Constrained quadratic optimization methods appear to apply sufficiently good approximations to achieve a high reconstruction fidelity with a simple quadratic metric amenable to a broad class of minimization methods. These preliminary simulation-based results are very promising and suggest that such methods may be used to produce high-fidelity reconstructions with less computation than many other statistical methods. By design, these quadratic methods are also explicit and quantitative in their description, allowing fine-tuning according to the specific uncertainties and noise model of the experiment. Further research is required to ascertain the full practical potential of these methods.

Purpose: Quantitative measures derived from positron emission tomography (PET) images are subject to statistical uncertainty, depending critically on system parameters, including the spatial resolution of the scanner. Predictions of statistical uncertainty of quantitative measures were compared with measurements.

Approach: Measurements were performed on the dual-ring PET prototype setup at the University of Michigan. The setup consisted of multiple detectors that, in combination, span a system resolution ranging between 1 and 5.5 mm full-width-at-half-maximum (FWHM). A Micro Jaszczak hot-spot phantom with rod diameters between 1.2 and 4.8 mm was imaged and independently reconstructed for different detector combinations. Statistical properties of quantitative measures were evaluated for different reconstructions.

Results: Measured signal-to-noise ratios (SNR) of 108  ±  14, 85  ±  11, and 40  ±  5 for high (0.92 to 0.98 mm FWHM), medium (1.3 to 1.5 mm FWHM), and low (5.5 mm FWHM) resolution detector configurations and 1 million events in general terms followed predications based on the detector resolution.

Conclusions: The unique tomograph configuration allowed for experimental comparison of the impact of spatial resolution on statistical properties of reconstructions in the same setup. An SNR advantage in systems with high resolutions was predicted and confirmed even for object features significantly larger than the detector resolution.

Purpose: To assess the result in orthopedic trauma surgery, usually three-dimensional volume data of the treated region is acquired. With mobile C-arm systems, these acquisitions can be performed intraoperatively, reducing the number of required revision surgeries. However, the acquired volumes are typically not aligned to the anatomical regions. Thus, the multiplanar reconstructed (MPR) planes need to be adjusted manually during the review of the volume. To speed up and ease the workflow, an automatic parameterization of these planes is needed.

Approach: We present a detailed study of multitask learning (MTL) regression networks to estimate the parameters of the MPR planes. First, various mathematical descriptions for rotation, including Euler angle, quaternion, and matrix representation, are revised. Then, two different MTL network architectures based on the PoseNet are compared with a single task learning network.

Results: Using a matrix description rather than the Euler angle description, the accuracy of the regressed normals improves from 7.7 deg to 7.3 deg in the mean value for single anatomies. The multihead approach improves the regression of the plane position from 7.4 to 6.1 mm, whereas the orientation does not benefit from this approach. Thus, the achieved accuracy meets the reported interrater variance in similarly complex body regions of up to 6.3 deg for the normals and up to 9.3 mm for the plane position.

Conclusions: The use of a multihead approach with shared features leads to more accurate plane regression compared with the use of individual networks for each task. It also improves the angle estimation for the ankle region. The reported results are in the same range as manual plane adjustments. The use of a combined network with shared parameters requires less memory, which is a great benefit for the implementation of an application for the surgical environment.

Purpose: The introduction of whole slide imaging and digital pathology has enabled greater scrutiny of visual search behaviors among pathologists. We aim to investigate zooming and panning behaviors, external markers of visual processing capabilities, and the changes with experience.

Approaches: Twenty digitized breast core needle biopsy histopathology slides were obtained from the circulating slides from the main digital pathology trial (IRAS number: 258799). These were presented to five pathologists with varying experience (1.5 to 40 years) whose examinations were recorded. Data of visual fixations were collected using eye-tracking cameras, and the magnification data and zooming behaviors were extracted in an objective fashion by an automated algorithm. The relationship between experience and metrics was analyzed using mixed-effects regression analyses.

Results: There was a significant association between experience and both reading times (p  <  0.001) and a number of fixations (p  <  0.001), with these relationships being inversely proportional. The greater experience was also associated with greater diagnostic accuracy (p  =  0.033). We found that experience was significantly associated with greater use of magnification changes (p  <  0.001). Conversely, less experience showed a near significant association with the increased proportion of time spent panning (p  =  0.070).

Conclusions: Fewer fixations needed to reach a diagnosis and quicker reading times are indicative of greater cognitive and visual processing capabilities with greater experience. These cognitive capabilities may be a prerequisite for the more frequent zooming changes that are more prevalent with increasing experience.

Purpose: In ultrasound imaging, interpolation is a key step in converting scanline data to brightness-mode (B-mode) images. Conventional methods, such as bilinear interpolation, do not fully capture the spatial dependence between data points, which leads to deviations from the underlying probability distribution at the interpolation points.

Approach: We propose Gaussian process (GP) regression as an improved method for ultrasound scanline interpolation. Using ultrasound scanlines acquired from two different ultrasound scanners during in vivo trials, we compare the scanline conversion accuracy of three standard interpolation methods with that of GP regression, measuring the peak signal-to-noise ratio (PSNR) and mean absolute error (MAE) for each method.

Results: The PSNR and MAE scores show that GP regression leads to more accurate scanline conversion compared to the nearest neighbor, bilinear, and cubic spline interpolation methods, for both datasets. Furthermore, limiting the interpolation window size of GP regression to 15 reduces computation time with minimal to no reduction in PSNR.

Conclusions: GP regression quantitatively leads to more accurate scanline conversion and provides uncertainty estimates at each of the interpolation points. Our windowing method reduces the computational cost of using GP regression for scanline conversion.