Integration of 3D Volumetric Computed Tomography Scan Image Data with Conventional Well Logs for Detection of Petrophysical Rock Classes

Summary Core measurements are used for rock classification and improved formation evaluation in both cored and noncored wells. However, the acquisition of such measurements is time-consuming, delaying rock classification efforts for weeks or months after core retrieval. On the other hand, well-log-based rock classification fails to account for rapid spatial variation of rock fabric encountered in heterogeneous and anisotropic formations due to the vertical resolution of conventional well logs. Interpretation of computed tomography (CT) scan data has been identified as an attractive and high-resolution alternative for enhancing rock texture detection, classification, and formation evaluation. Acquisition of CT scan data is accomplished shortly after core retrieval, providing high-resolution data for use in petrophysical workflows in relatively short periods of time. Typically, CT scan data are used as two-dimensional (2D) cross-sectional images, which is not suitable for quantification of three-dimensional (3D) rock fabric variation, which can increase the uncertainty in rock classification using image-based rock-fabric-related features. The methods documented in this paper aim to quantify rock-fabric-related features from whole-core 3D CT scan image stacks and slabbed whole-core photos using image analysis techniques. These quantitative features are integrated with conventional well logs and routine core analysis (RCA) data for fast and accurate detection of petrophysical rock classes. The detected rock classes are then used for improved formation evaluation. To achieve the objectives, we conducted a conventional formation evaluation. Then, we developed a workflow for preprocessing of whole-core 3D CT-scan image stacks and slabbed whole-core photos. Subsequently, we used image analysis techniques and tailor-made algorithms for the extraction of image-based rock-fabric-related features. Then, we used the image-based rock-fabric-related features for image-based rock classification. We used the detected rock classes for the development of class-based rock physics models to improve permeability estimates. Finally, we compared the detected image-based rock classes against other rock classification techniques and against image-based rock classes derived using 2D CT scan images. We applied the proposed workflow to a data set from a siliciclastic sequence with rapid spatial variations in rock fabric and pore structure. We compared the results against expert-derived lithofacies, conventional rock classification techniques, and rock classes derived using 2D CT scan images. The use of whole-core 3D CT scan image-stacks-based rock-fabric-related features accurately captured changes in the rock properties within the evaluated depth interval. Image-based rock classes derived by integration of whole-core 3D CT scan image-stacks-based and slabbed whole-core photos-based rock-fabric-related features agreed with expert-derived lithofacies. Furthermore, the use of the image-based rock classes in the formation evaluation of the evaluated depth intervals improved estimates of petrophysical properties such as permeability compared to conventional formation-based permeability estimates. A unique contribution of the proposed workflow compared to the previously documented rock classification methods is the derivation of quantitative features from whole-core 3D CT scan image stacks, which are conventionally used qualitatively. Furthermore, image-based rock-fabric-related features extracted from whole-core 3D CT scan image stacks can be used as a tool for quick assessment of recovered whole core for tasks such as locating best zones for extraction of core plugs for core analysis and flagging depth intervals showing abnormal well-log responses.

Sutures ultrasound: useful diagnostic screening for posterior plagiocephaly

Purpose Posterior plagiocephaly (PP) is a common clinical condition in pediatric age. There are two main causes of PP: postural plagiocephaly and craniosynostosis. Early diagnosis is important, as it prevents neurological complications and emergencies. Diagnosis in the past was often made late and with imaging tests that subjected the infant to a high radiation load. Suture ultrasound does not use ionizing radiation; it is easy to perform, allows an early diagnosis, and directs toward the execution of the cranial 3D-CT scan, neurosurgical consultation, and possible intervention. The aim of the study is to describe the high sensitivity and specificity of suture ultrasound for the differential diagnosis between plagiocephaly and craniosynostosis. Methods We reported our prospective experience and compared it with the data in the literature through a systematic review. The systematic review was conducted on electronic medical databases (PubMed, Embase, Cochrane Library, Scopus, and Web of Science) evaluating the published literature up to November 2020. According to Preferred Reporting Items for Systematic Reviews and Meta-ANALYSES (PRISMA statement), we identified 2 eligible studies. Additionally, according to AMSTAR 2, all included reviews have been critically rated as high quality. A total of 120 infants with abnormal skull shape were examined in NICU. All underwent clinical and ultrasound examination. Results Of the total, 105 (87.5%) had plagiocephaly and 15 dolichocephaly/scaphocephaly (12.5%). None of these had associated other types of malformations and/or neurological disorders. The synostotic suture was identified ultrasonographically in 1 infant and subsequently confirmed by 3D CT scan (100%). Conclusion Cranial sutures ultrasonography can be considered in infants a selective, excellent screening method for the evaluation of skull shape deformities as first technique before the 3D CT scan exam and subsequent neurosurgical evaluation. Cranial suture ultrasonography should be considered part of clinical practice especially for pediatricians.

PEEK Implants: An Innovative Solution for Facial Aesthetic Surgery

The authors present a case report showing their experience with the use of PolyEtherEtherKetone (PEEK) implants as an innovative solution for the skeleton and soft tissues’ reshaping in facial aesthetic plastic surgery. This technique offers the surgeon a reliable and effective way to answer patients’ request of increasing volume and reshaping the malar area. A fifty-year-old patient complaining about hypoplasia of the malar area, after undergoing three operations of silicon implants’ placement and replacement, was still unsatisfied about the symmetry and feeling through the skin of the lower lid, the rim of the prostheses. The authors suggested the use of bone-anchored PEEK implants, to increase the volume and reshape the malar area by a skeleton and soft tissue camouflage. The treatment was planned and previewed on the preop 3-dimensional CT scans for the customization of the implants. Although no cases are reported in international literature on the use of this material in facial aesthetic surgery, this technique seems to offer a safe and effective solution for the treatment of patients asking to increase and modify the shape of their malar area. Custom made PEEK implants are already used in craniofacial reconstructive bony surgery with good results, and 3D CT scan planning is widely used in these cases. No complications were reported in the case reported and the outcomes seem to the authors and to the patient being, finally, satisfactory.

COVID19 Diagnosis using AutoML from 3D CT scans

Coronavirus is a pandemic that affects the respiratory system causing cough, shortness of breath, and death in severe cases. Polymerase chain reaction (PCR) tests are used to diagnose coronavirus. The false-negative rate of these tests is high, so there needs a supporting method for an accurate diagnosis. CT scan provides a detailed examination of the chest to diagnose COVID but a single CT scan comprises hundreds of slices. Expert and experienced radiologists and pulmonologists can diagnose COVID from these hundreds of slices, but this is very time-consuming. So an automatic artificial intelligence (AI) based method is required to diagnose coronavirus with high accuracy. Developing this AI-based technique requires a lot of resources and time, but once it is developed, it can significantly help the clinicians. This paper used an Automated machine learning (AutoML) technique that requires fewer resources (optimal architecture trials) and time to develop, resulting in the best diagnosis. The AutoML models are trained on 2D slices instead of 3D CT scans, and the predictions on unknown data (slices of CT scan) are aggregated to form a prediction of 3D CT scan. The aggregation process picked the most occurred case, whether COVID or non-COVID from all CT scan slices and labeled the 3D CT scan accordingly. Different thresholds are also used to label COVID or non-COVID 3D CT scans from 2D slices. The approach resulted in accuracy and F1-score of 89% and 88%, respectively. Implementation is available at github.com/talhaanwarch/mia-covid19

COVID19 Diagnosis using AutoML from 3D CT scans

Coronavirus is a pandemic that affects the respiratory system causing cough, shortness of breath, and death in severe cases. Polymerase chain reaction (PCR) tests are used to diagnose coronavirus. The false-negative rate of these tests is high, so there needs a supporting method for an accurate diagnosis. CT scan provides a detailed examination of the chest to diagnose COVID but a single CT scan comprises hundreds of slices. Expert and experienced radiologists and pulmonologists can diagnose COVID from these hundreds of slices, but this is very time-consuming. So an automatic artificial intelligence (AI) based method is required to diagnose coronavirus with high accuracy. Developing this AI-based technique requires a lot of resources and time, but once it is developed, it can significantly help the clinicians. This paper used an Automated machine learning (AutoML) technique that requires fewer resources (optimal architecture trials) and time to develop, resulting in the best diagnosis. The AutoML models are trained on 2D slices instead of 3D CT scans, and the predictions on unknown data (slices of CT scan) are aggregated to form a prediction of 3D CT scan. The aggregation process picked the most occurred case, whether COVID or non-COVID from all CT scan slices and labeled the 3D CT scan accordingly. Different thresholds are also used to label COVID or non-COVID 3D CT scans from 2D slices. The approach resulted in accuracy and F1-score of 89% and 88%, respectively. Implementation is available at github.com/talhaanwarch/mia-covid19

ANISOTROPY QUANTIFICATION USING HIGH-RESOLUTION WHOLE-CORE CT-SCAN IMAGES

Spatial anisotropy and heterogeneity in petrophysical properties can significantly affect formation evaluation of hydrocarbon bearing formations. A common example is permeability anisotropy, which is a consequence of the depositional mechanisms of sediments. Additionally, the variation in spatial distribution of rock components and the effect of post-depositional processes on the physical and chemical structure of the rock constituents can strongly impact the directional dependency of petrophysical, electrical, and elastic properties. Therefore, image-based quantification of spatial distribution of rock constituents can be used for anisotropy evaluation. Assessment of anisotropy has been previously accomplished through use of pore-scale images. However, the discrete nature of this images gives a narrow picture of anisotropy in larger scales. Whole-core computed tomography (CT) scan images, despite revealing the distribution of rock components at a coarser scale, provide a continuous medium for anisotropy estimation. Assessment of anisotropy using three-dimensional (3D) CT-scan data and incorporation of that information in well-log-based formation evaluation is, however, not widely studied or practiced in the petroleum industry. The objectives of this paper are (a) to develop a method to quantify anisotropy utilizing whole-core 3D CT-scan image stacks, (b) to provide a semi-continuous measure of rock anisotropy, and (c) to show the value of the proposed method by means of estimation of directional-dependent elastic properties. First, we pre-process the raw whole-core CT-scan images to remove undesired image artifacts and to generate an image containing pixels representing only the recovered core material. Then, we segment each whole-core CT-scan image stack into distinctive phases. Then, we conduct numerical simulations of electric potential distribution in conjunction with streamline tracing techniques to quantify the electrical tortuosity of the continuous phase in each cartesian direction. We employed the tortuosity distribution values in each direction as a measure of rock anisotropy. Finally, we use a simulation model to estimate direction-dependent elastic properties. We applied the introduced method to dual energy whole-core CT-scan image stacks acquired in a siliciclastic depth interval. Estimates of rock anisotropy obtained using the proposed method agreed with the observed visual distribution of the segmented phase and the observed heterogeneity in available slabbed whole-core photos and 2D CT-scan images. Additionally, estimation of directional-dependent elastic properties demonstrated the value of the proposed method. Anisotropy results coincided with directional-dependent estimation of elastic properties. We observed measurable anisotropy in the 3D CT-scan image stacks, which is important to be quantitatively taken into account in petrophysical/ mechanical evaluation of this formation. A unique contribution of the proposed workflow is the use of core-scale image data for anisotropy estimation and the continuous nature of the anisotropy estimates when compared with workflows employing only pore-scale image data. It should also be noted that the proposed method can potentially be employed to identify the optimum locations to acquire core plugs for further assessment of rock anisotropy.

A Hybrid Approach for Automatic Aorta Segmentation in Abdominal 3D CT Scan Images

Due to rapid advancement in medical imaging, human anatomy is now observable in finer details bringing new dimensions to diagnosis and treatment. One such area which benefitted from advancement in medical imaging is aorta segmentation. Aorta segmentation is achieved by using anatomical features (shape and position of aorta) using specialized segmentation algorithms. These segmentation algorithms are broadly classified into two categories. The first type comprises of fast algorithms which exploits spatial and intensity properties of images. The second type are iterative algorithms which use optimization of a cost function to track aorta boundaries. Fast algorithms offer lower computation cost, whereas iterative algorithms offer better segmentation accuracy. Therefore, there is a tradeoff between segmentation accuracy and computational cost. In this work, a hybrid approach for aorta segmentation in 3D Computed Tomography (CT) scan images is proposed. The proposed approach produces high segmentation accuracy of intensity based (fast) approaches at reduced computational cost. The proposed technique is evaluated using real world 3D abdominal CT scan images. The proposed approach can either be used as a fast-standalone segmentation procedure, or as a pre-segmentation procedure for iterative and more accurate approaches.