Specific Acquisition Protocol Research Articles

Calibration phantom-based prediction of CT lung nodule volume measurement performance.

A calibration phantom-based method has been developed for predicting small lung nodule volume measurement bias and precision that is specific to a particular computed tomography (CT) scanner and acquisition protocol. The approach involves CT scanning a simple reference object with a specific acquisition protocol, analyzing the scan to estimate the fundamental imaging properties of the CT acquisition system, generating numerous simulated images of a target geometry using the fundamental imaging properties, measuring the simulated images with a standard nodule volume segmentation algorithm, and calculating bias and precision performance statistics from the resulting volume measurements. We evaluated the ability of this approach to predict volume measurement bias and precision of Teflon spheres (diameters =4.76, 6.36, and 7.94 mm) placed within an anthropomorphic chest phantom when using 3M Scotch Magic™ tape as the reference object. CT scanning of the spheres was performed with 0.625, 1.25, and 2.5 mm slice thickness and spacing. The study demonstrated good agreement between predicted volumetric performance and observed volume measurement performance for both volumetric measurement bias and precision. The predicted and observed volume mean for all slice thicknesses was found to be 28% and 13% lower on average than the manufactured sphere volume, respectively. When restricted to 0.625 and 1.25 mm slice thickness scans, which are recommended for small lung nodule volume measurement, we found that the difference between predicted and observed volume coefficient of variation was less than 1.0 %. The approach also showed a resilience to varying CT image acquisition protocols, a critical capability when deploying in a real-world clinical setting. This is the first report of a calibration phantom-based method's ability to predict both small lung nodule volume measurement bias and precision. Volume measurement bias and precision for small lung nodules can be predicted using simple low-cost reference objects to estimate fundamental CT image characteristics and modeling and simulation techniques. The approach demonstrates an improved method for predicting task specific, clinically relevant measurement performance using advanced and fully automated image analysis techniques and low-cost reference objects.

3-D Printing of Calibrated Germanium-68 PET Phantoms Without Inactive Walls

We aimed to print a solid-state germanium-68 phantom for positron emission tomography (PET) with excellent control of the activity concentration in the phantom material, with safe handling characteristics, and without inactive walls. To this end, we developed a novel method for incorporating germanium-68 into hydrophobic stereo-lithography monomers. The spontaneous phase transfer process provides complexation and induces phase transfer of aqueous ionic germanium-68 into the hydrophobic monomers, ensures homogenous isotope distribution in the rigid polymer matrix, and prevents leaching in aqueous environments. With this method, we additively manufactured radioactive spheres following the International Electrotechnical Commission design with the addition of a small sphere of 7.7 mm diameter. Extended leaching testing demonstrated near perfect source tightness. The same phantom can be repeatedly used in the quality management of multicenter clinical trials, without preparative errors. Long-lived solid-state phantoms produced with this method will provide consistent and reproducible validation of total body PET/CT systems. PET images with these phantoms acquired within a decaying gallium-68 background at different signal to background ratios showed homogenous activity distribution and no fill-related artefacts. The varying background allowed the assessment of trial specific acquisition protocols that match a given clinical question much better than todays predefined standard protocol.

Sources of residual autocorrelation in multiband task fMRI and strategies for effective mitigation.

Analysis of task fMRI studies is typically based on using ordinary least squares within a voxel- or vertex-wise linear regression framework known as the general linear model. This use produces estimates and standard errors of the regression coefficients representing amplitudes of task-induced activations. To produce valid statistical inferences, several key statistical assumptions must be met, including that of independent residuals. Since task fMRI residuals often exhibit temporal autocorrelation, it is common practice to perform "prewhitening" to mitigate that dependence. Prewhitening involves estimating the residual correlation structure and then applying a filter to induce residual temporal independence. While theoretically straightforward, a major challenge in prewhitening for fMRI data is accurately estimating the residual autocorrelation at each voxel or vertex of the brain. Assuming a global model for autocorrelation, which is the default in several standard fMRI software tools, may under- or over-whiten in certain areas and produce differential false positive control across the brain. The increasing popularity of multiband acquisitions with faster temporal resolution increases the challenge of effective prewhitening because more complex models are required to accurately capture the strength and structure of autocorrelation. These issues are becoming more critical now because of a trend toward subject-level analysis and inference. In group-average or group-difference analyses, the within-subject residual correlation structure is accounted for implicitly, so inadequate prewhitening is of little real consequence. For individual subject inference, however, accurate prewhitening is crucial to avoid inflated or spatially variable false positive rates. In this paper, we first thoroughly examine the patterns, sources and strength of residual autocorrelation in multiband task fMRI data. Second, we evaluate the ability of different autoregressive (AR) model-based prewhitening strategies to effectively mitigate autocorrelation and control false positives. We consider two main factors: the choice of AR model order and the level of spatial regularization of AR model coefficients, ranging from local smoothing to global averaging. We also consider determining the AR model order optimally at every vertex, but we do not observe an additional benefit of this over the use of higher-order AR models (e.g. (AR(6)). To overcome the computational challenge associated with spatially variable prewhitening, we developed a computationally efficient R implementation using parallelization and fast C++ backend code. This implementation is included in the open source R package BayesfMRI. We find that residual autocorrelation exhibits marked spatial variance across the cortex and is influenced by many factors including the task being performed, the specific acquisition protocol, mis-modeling of the hemodynamic response function, unmodeled noise due to subject head motion, and systematic individual differences. We also find that local regularization is much more effective than global averaging at mitigating autocorrelation. While increasing the AR model order is also helpful, it has a lesser effect than allowing AR coefficients to vary spatially. We find that prewhitening with an AR(6) model with local regularization is effective at reducing or even eliminating autocorrelation and controlling false positives. Our analysis revealed dramatic spatial differences in autocorrelation across the cortex. This spatial topology is unique to each session, being influenced by the task being performed, the acquisition technique, various modeling choices, and individual differences. If not accounted for, these differences will result in differential false positive control and power across the cortex and across subjects.

Repeated lidar acquisitions in an underground salt gallery in the Alsatian potassic basin (France): Monitoring and geomechanical modelling

In the Alsatian potassic basin, located northwest of Mulhouse (eastern France), the deformation of an underground salt gallery is monitored from April 2017 to April 2019 using repeated static lidar acquisitions. This monitoring aims to characterize the rheology of the rock salt. The gallery is situated at 550 m deep in a 20 m thick rock salt layer. At this depth traditional referencing tools such as GPS are not available; and the salt is creeping, which prevents the referencing of the measurements with respect to a stable area. First, we develop a specific acquisition protocol to precisely monitor the deformation affecting the gallery walls over time. After two years, we measure a horizontal deformation between 0.10 and 0.30% all along the gallery with a mean uncertainty of 0.08%. From the low horizontal deformation, a new methodology is developed to set the point clouds in the same coordinate system. By doing so, the gallery closure, or volumetric deformation, is calculated. Then, the horizontal deformation time series of the gallery is used to parametrize the mechanical behaviour law of the rock salt, consistent with parameters from laboratory work on this rock salt. Through our work, in addition to the new methodology, the lidar device is shown to be a useful and polyvalent tool to monitor underground cavities and to mechanically characterize the surrounding medium.

Combined Estimation of Shape and Pose for Statistical Analysis of Articulating Joints.

Quantifying shape variations in articulated joints is of utmost interest to understand the underlying joint biomechanics and associated clinical symptoms. For joint comparisons and analysis, the relative positions of the bones can confound subsequent analysis. Clinicians design specific image acquisition protocols to neutralize the individual pose variations. However, recent studies have shown that even specific acquisition protocols fail to achieve consistent pose. The individual pose variations are largely attributed to the day-to-day functioning of the patient, such as gait during walk, as well as interactions between specific morphologies and joint alignment. This paper presents a novel two-step method to neutralize such patient-specific variations while simultaneously preserving the inherent relationship of the articulated joint. The resulting shape models are then used to discover clinically relevant shape variations in a population of hip joints.

Implementing Shared, Standardized Imaging Protocols to Improve Cross-Enterprise Workflow and Quality.

Value-based imaging requires appropriate utilization and the delivery of consistently high-quality imaging at an acceptable cost. Challenges include developing standardized imaging protocols, ensuring consistent application by technologists, and monitoring quality. These challenges increase as enterprises grow in geographical extent and complexity through mergers or partnerships. Our imaging enterprise includes a university hospital and clinic system, a large county hospital and healthcare system, and a pediatric hospital and health system. Studies across the three systems are interpreted by one large academic radiology group with expertise in various subspecialties. Our goals were as follows: (1) Standardize imaging protocols; (2) adapt the imaging protocols to specific modalities and available equipment; and (3) disseminate this knowledge across all of the sites of care. Our approach involved three components: (1) facilitation of imaging protocol definition across subspecialty radiologist teams; (2) creation of a database which links the clinical imaging protocols to the scanner/machine specific acquisition protocols; and (3) delivery of a protocol library and updates to all users regardless of location. We successfully instituted a process for the development, implementation, and delivery of standardized imaging protocols in a complex, multi-institutional healthcare system. Key elements for success include (1) a Project Champion who is able to articulate the importance of protocol standardization in improving the quality of patient care, (2) strong, effective modality-specific operational committees, (3) a Project Lead to manage the process efficiently, and (4) an electronic publishing of the protocol database to facilitate ease of access and use.

A joint space-angle regularization approach for single 4D diffusion image super-resolution.

Low signal-to-noise-ratio and limited scan time of diffusion magnetic resonance imaging (dMRI) in current clinical settings impede obtaining images with high spatial and angular resolution (HSAR) for a reliable fiber reconstruction with fine anatomical details. To overcome this problem, we propose a joint space-angle regularization approach to reconstruct HSAR diffusion signals from a single 4D low resolution (LR) dMRI, which is down-sampled in both 3D-space and q-space. Different from the existing works which combine multiple 4D LR diffusion images acquired using specific acquisition protocols, the proposed method reconstructs HSAR dMRI from only a single 4D dMRI by exploring and integrating two key priors, that is, the nonlocal self-similarity in the spatial domain as a prior to increase spatial resolution and ridgelet approximations in the diffusion domain as another prior to increase the angular resolution of dMRI. To more effectively capture nonlocal self-similarity in the spatial domain, a novel 3D block-based nonlocal means filter is imposed as the 3D image space regularization term which is accurate in measuring the similarity and fast for 3D reconstruction. To reduce computational complexity, we use the L2 -norm instead of sparsity constraint on the representation coefficients. Experimental results demonstrate that the proposed method can obtain the HSAR dMRI efficiently with approximately 2% per-voxel root-mean-square error between the actual and reconstructed HSAR dMRI. The proposed approach can effectively increase the spatial and angular resolution of the dMRI which is independent of the acquisition protocol, thus overcomes the inherent resolution limitation of imaging systems.

Enhancement of multimodality texture-based prediction models via optimization of PET and MR image acquisition protocols: a proof of concept

Texture-based radiomic models constructed from medical images have the potential to support cancer treatment management via personalized assessment of tumour aggressiveness. While the identification of stable texture features under varying imaging settings is crucial for the translation of radiomics analysis into routine clinical practice, we hypothesize in this work that a complementary optimization of image acquisition parameters prior to texture feature extraction could enhance the predictive performance of texture-based radiomic models. As a proof of concept, we evaluated the possibility of enhancing a model constructed for the early prediction of lung metastases in soft-tissue sarcomas by optimizing PET and MR image acquisition protocols via computerized simulations of image acquisitions with varying parameters. Simulated PET images from 30 STS patients were acquired by varying the extent of axial data combined per slice (‘span’). Simulated T1-weighted and T2-weighted MR images were acquired by varying the repetition time and echo time in a spin-echo pulse sequence, respectively. We analyzed the impact of the variations of PET and MR image acquisition parameters on individual textures, and we investigated how these variations could enhance the global response and the predictive properties of a texture-based model. Our results suggest that it is feasible to identify an optimal set of image acquisition parameters to improve prediction performance. The model constructed with textures extracted from simulated images acquired with a standard clinical set of acquisition parameters reached an average AUC of in bootstrap testing experiments. In comparison, the model performance significantly increased using an optimal set of image acquisition parameters (), with an average AUC of . Ultimately, specific acquisition protocols optimized to generate superior radiomics measurements for a given clinical problem could be developed and standardized via dedicated computer simulations and thereafter validated using clinical scanners.

Conversion factors of effective and equivalent organ doses with the air kerma area product in patients undergoing coronary angiography and percutaneous coronary interventions

To derive effective dose (E), organ dose (HT) and conversion factors with the air kerma area product (KAP) in coronary angiography (CA) and percutaneous coronary intervention (PCI) by the radial route, using the ICRP 103 tissue weighting factors.The study included 34 patients referred for CA and 31 for PCI. E and HT were derived from in-the-field KAP measurements using Montecarlo methods.Median KAP of 23.2 and 56.8Gycm2 and E of 6.9 and 20.0mSv were found for CA and PCI, respectively. Mean KAP and E were significantly higher in males than in females (52.4±40.0 vs 32.3±16.6Gycm2; p=0.02) and (16.8±13.6 vs 10.7±5.8mSv; p=0.04). KAP (r=0.39; p=0.001) and E (r=0.34; p=0.005) showed a significant correlation with the patient’s weight.Conversion factors between KAP and E (E/KAP) were 0.30±0.04mSvGy−1cm−2 for CA and 0.33±0.05mSvGy−1cm−2 for PCI. No significant differences in the E/KAP between males and females were found (0.31±0.05 vs 0.33±0.05; p=0.08). Again, no significant correlation was found between E/KAP and patient’s weight (r=0.23; p=0.07).The correlation between E and KAP was excellent for CA (r=0.99) and PCI (r=0.96). The correlation between HT and KAP ranged from r=0.87 to r=1 and from r=0.71 to r=0.98 for CA and PCI, respectively.A single factor, the total KAP, could be used for a specific acquisition protocol to reliably estimate E and HT without the need of a patient’s specific analysis. Conversion factors might be installation, X-ray beam quality or protocol dependent.

Validation of an XCT/fDOT System on Mice

In this paper we present systems for dual modality imaging, combining fluorescence-enhanced diffuse optical tomography and X-ray computed tomography. Fluorescence diffuse optical tomography is carried out in a cylindrical geometry, which ensures optimal sampling and a straight forward integration with the X-ray modality. Specific acquisition protocols and reconstruction software have been developed to this end. The X-ray computed tomography serves two purposes. First, it provides the anatomical information in the registered dual modality images. Second, it provides the actual shape and boundaries of the animal as a priori input to the fluorescence reconstruction algorithm. To evaluate the performance of the optical imaging system, experiments have been conducted on phantoms, mice with inserted fluorescing capillaries, and finally on mice bearing tumors, ex-vivo and in-vivo. Experiments on mice with capillaries inserted in different region of interest, allow estimating the detection limits of fluorophore concentrations. The fluorescence reconstructions are shown to be geometrically consistent with the X-ray images. Finally we demonstrate the capability of the bimodal system to localize real tumours in mice in-vivo. These results show that dual modality fluorescence-enhanced diffuse optical tomography and X-ray computed tomography imaging in cylindrical geometry has a high potential for small animal tumour evolution studies.

Modeling of NMR processing, toward efficient unattended processing of NMR experiments

Many alternative processing techniques have recently been proposed in the literature. Most of these techniques rely on specific acquisition protocols as well as on specific data processing techniques, the need for an efficient versatile and expandable NMR processing tool would be a particularly timely addition to the modern NMR spectroscopy laboratory. The work presented here consists in a modeling of the various possible NMR data processing approaches. This modeling presents a common working frame for most of the modern acquisition/processing protocols. Two different data modeling approaches are presented, strong modeling and weak modeling, depending whether the system under study or the measurement is modeled. The emphasis is placed on the weak modeling approach. This modeling is implemented in a computer program developed in python and called NPK standing (standing for NMR Processing Kernel), organized in four logical layers (i) mathematical kernel; (ii) elementary actions; (iii) processing phases; (iv) processing strategies. This organisation, along with default values for most processing parameters allows the use of the program in an unattended manner, producing close to optimal spectra. Examples are shown for 1D and 2D processing, and liquid and solid NMR spectroscopy. NPK is available from the site: http://abcis.cbs.cnrs.fr/NPK.

Conventional MRI in Multiple Sclerosis

During the past 10 years, conventional magnetic resonance imaging (cMRI) has become an established tool for the assessment of patients with multiple sclerosis (MS) and to monitor treatment trials. This is mainly due to the sensitivity and reproducibility of cMRI in the detection of MS-related damage. A large effort has also been devoted to develop imaging strategies capable of providing accurate estimates of the extent of disease-related damage not only in the brain, but also in the spinal cord and optic nerve. Guidelines have been defined to integrate MR findings in the diagnostic evaluation of patients at presentation with clinically isolated syndromes suggestive of MS, and specific acquisition protocols have been offered for monitoring longitudinal changes in patients with established disease. Despite the fact that the role of cMRI in MS has been profoundly obviated by the advent of modern and quantitative MR techniques, several issues are still unresolved. Technical development in acquisition and postprocessing, as well as the introduction of high-field magnets in the clinical arena, are likely to increase our understanding of disease pathobiology, mainly through an increased ability to quantify the extent of gray matter damage.

11C-acetate PET imaging in prostate cancer

The patient population with a rising PSA following definitive local therapy represents the second largest group of prostate cancer patients [1]. These patients are characterized by a rising marker following definitive local therapy with either surgery or radiation, and by definition have no evidence of disease on standard imaging studies [2]. By some estimates, approximately 50,000 men in the USA enter this clinical state each year [3]. Although some reports suggest that the median amount of time that a patient will spend in this clinical state before developing metastatic disease is 8 years [4], in actuality these patients represent a wide spectrum of clinical risk. Some have rapidly progressive micrometastatic disease and are at high risk for developing metastases; others experience locally persistent disease or an otherwise indolent clinical course. Distinguishing between these patients plays a critical role in selecting appropriate treatment. A patient with aggressive micrometastatic disease is a candidate for systemic treatments. On the other hand, patients who have no systemic disease and an exclusively local residual tumor may be cured with local salvage strategies [5, 6]. This rising-PSA population is regarded by many clinical investigators as the most challenging in terms of testing and developing new therapies for prostate cancer care. These patients experience a highly variable clinical course, and often have no radiographic findings, no symptoms, and no tissue-based assessments by which treatment effects can be evaluated. To address these limitations, consensus guidelines have been published to define a framework for conducting trials, but even these guidelines recommend that investigators be cautious about testing drugs early in such patients, given the methodological challenges [7]. Paradoxically, though, radiologists are attracted to trials involving this clinical state because it is literally defined by the inadequacies of present imaging modalities for prostate cancer. Bone and CT scans are insensitive in detecting osseous metastases. For locally recurrent disease, magnetic resonance imaging (MRI), with a specific acquisition protocol that focuses on the pelvis, can detect abnormalities in the prostate bed, but again, distinguishing postoperative changes from active tumor is challenging, and many of these sites are in areas that are not routinely biopsied based on standard algorithms. Positron emission tomography (PET) has been posited by some as a solution to these limitations. Rather than examining the derivative effects on bone, it can demonstrate the biology of the tumor itself; it also offers the potential to image sites in bone and soft tissue simultaneously and to survey the entire body rapidly. It could possibly distinguish fibrosis from active cancer in the prostate bed in a postsurgical patient, a disease-free residual prostate from one containing active cancer in a postradiation therapy (post-RT) patient, and early bone metastases from nonmalignant injuries and inflammatory osseous lesions. A study by Dr. Albrecht and colleagues examined the use of PETwith C-acetate, a tracer of fatty acid synthesis. Although the mechanism of tracer uptake in cancers is not wholly clear, it has been posited that metabolic activity in the tumor occurs in a low-oxygen microenvironment, in This editorial commentary refers to the article http://dx.doi.org/10.1007/s00259-006-0163-x.

Assessment of the early organization and maturation of infants' cerebral white matter fiber bundles: A feasibility study using quantitative diffusion tensor imaging and tractography

The human infant is particularly immature at birth and brain maturation, with the myelination of white matter fibers, is protracted until adulthood. Diffusion tensor imaging offers the possibility to describe non invasively the fascicles spatial organization at an early stage and to follow the cerebral maturation with quantitative parameters that might be correlated with behavioral development. Here, we assessed the feasibility to study the organization and maturation of major white matter bundles in eighteen 1- to 4-month-old healthy infants, using a specific acquisition protocol customized to the immature brain (with 15 orientations of the diffusion gradients and a 700 s mm −2 b factor). We were able to track most of the main fascicles described at later ages despite the low anisotropy of the infant white matter, using the FACT algorithm. This mapping allows us to propose a new method of quantification based on reconstructed tracts, split between specific regions, which should be more sensitive to specific changes in a bundle than the conventional approach, based on regions-of-interest. We observed variations in fractional anisotropy and mean diffusivity over the considered developmental period in most bundles (corpus callosum, cerebellar peduncles, cortico-spinal tract, spino-thalamic tract, capsules, radiations, longitudinal and uncinate fascicles, cingulum). The results are in good agreement with the known stages of white matter maturation and myelination, and the proposed approach might provide important insights on brain development.