Axial Computed Tomography Slices Research Articles

End-to-End Semi-Supervised Opportunistic Osteoporosis Screening Using Computed Tomography.

Osteoporosis is the most common metabolic bone disease and can cause fragility fractures. Despite this, screening utilization rates for osteoporosis remain low among populations at risk. Automated bone mineral density (BMD) estimation using computed tomography (CT) can help bridge this gap and serve as an alternative screening method to dual-energy X-ray absorptiometry (DXA). The feasibility of an opportunistic and population agnostic screening method for osteoporosis using abdominal CT scans without bone densitometry phantom-based calibration was investigated in this retrospective study. A total of 268 abdominal CT-DXA pairs and 99 abdominal CT studies without DXA scores were obtained from an oncology specialty clinic in the Republic of Korea. The center axial CT slices from the L1, L2, L3, and L4 lumbar vertebrae were annotated with the CT slice level and spine segmentation labels for each subject. Deep learning models were trained to localize the center axial slice from the CT scan of the torso, segment the vertebral bone, and estimate BMD for the top four lumbar vertebrae. Automated vertebra-level DXA measurements showed a mean absolute error (MAE) of 0.079, Pearson's r of 0.852 (P<0.001), and R2 of 0.714. Subject-level predictions on the held-out test set had a MAE of 0.066, Pearson's r of 0.907 (P<0.001), and R2 of 0.781. CT scans collected during routine examinations without bone densitometry calibration can be used to generate DXA BMD predictions.

Accuracy and reproducibility of two-dimensional computed tomography-based positioning of femoral component rotational alignment in preoperative planning for total knee arthroplasty

BackgroundPoor rotation of the femoral component in total knee arthroplasty (TKA) can result in various postoperative complications, underscoring the critical importance of preoperative planning.PurposeTo improve the accuracy of femoral component positioning during TKA, this study compared the accuracy and repeatability of different two-dimensional (2D) computed tomography (CT) measurement methods for measuring the posterior condylar angle (PCA) in preoperative TKA planning.MethodsA retrospective analysis was conducted on 75 patients (150 knees) who underwent bilateral lower extremity computed tomography angiography (CTA) at Fuyang People's Hospital from January 2021 to July 2021. Three different methods were used to measure the PCA based on 2D CT images (axial CT slices) and three-dimensional(3D) models (femoral models reconstructed from CT data) in this study. Method 1: Single-plane 2D CT measurement, measuring PCA in the most obvious single-plane CT slice of the surgical transepicondylar axis (sTEA); Method 2: multi-plane 2D CT measurement, identifying and locating anatomical landmarks in multiple 2D CT slices and measuring PCA; Method 3: 3D model measurement, measuring PCA in the reconstructed femur 3D model. Compare the differences in PCA measurements between the three measurement methods. A positive PCA measurement was recorded when the sTEA was externally rotated relative to the posterior condylar line (PCL). Any difference exceeding 3° between the PCA measurement in the 2D CT and the PCA reference value in the 3D model was classified as an outlier. The intraclass correlation coefficient (ICC) and Bland–Altman method were utilized to assess the intra- and inter-observer reproducibility of the three measurement methods.ResultsThe PCA measurement in the single-plane 2D CT was 1.91 ± 1.94°, with a measurement error of − 1.22 ± 1.32° and 12.7% of outlier values. In the multi-plane 2D CT, the PCA measurement was 2.96 ± 1.68°, with a measurement error of -0.15 ± 0.91° and 6.0% of outlier values. The PCA measurement in the 3D model was 3.12 ± 1.69°. The PCA measurement in single-plane 2D CT was notably smaller than that in multi-plane 2D CT and 3D models, with no significant difference between the latter two. The multi-plane 2D CT showed significantly lower measurement error and outlier values than the single-plane 2D CT. All three PCA measurement methods exhibited high reproducibility (ICC: 0.93 ~ 0.97).ConclusionsUsing of multi-plane 2D CT for measuring PCA in preoperative planning of TKA has high reproducibility and accuracy, with fewer outlier values. We recommend preoperative measurement of PCA using muti-plane 2D CT to improve the accuracy of positioning the femoral component rotational alignment during surgery.

Consensus guide on CT-based prediction of stopping-power ratio using a Hounsfield look-up table for proton therapy

Background and purposeStudies have shown large variations in stopping-power ratio (SPR) prediction from computed tomography (CT) across European proton centres. To standardise this process, a step-by-step guide on specifying a Hounsfield look-up table (HLUT) is presented here. Materials and methodsThe HLUT specification process is divided into six steps: Phantom setup, CT acquisition, CT number extraction, SPR determination, HLUT specification, and HLUT validation. Appropriate CT phantoms have a head- and body-sized part, with tissue-equivalent inserts in regard to X-ray and proton interactions. CT numbers are extracted from a region-of-interest covering the inner 70% of each insert in-plane and several axial CT slices in scan direction. For optimal HLUT specification, the SPR of phantom inserts is measured in a proton beam and the SPR of tabulated human tissues is computed stoichiometrically at 100 MeV. Including both phantom inserts and tabulated human tissues increases HLUT stability. Piecewise linear regressions are performed between CT numbers and SPRs for four tissue groups (lung, adipose, soft tissue, and bone) and then connected with straight lines. Finally, a thorough but simple validation is performed. ResultsThe best practices and individual challenges are explained comprehensively for each step. A well-defined strategy for specifying the connection points between the individual line segments of the HLUT is presented. The guide was tested exemplarily on three CT scanners from different vendors, proving its feasibility. ConclusionThe presented step-by-step guide for CT-based HLUT specification with recommendations and examples can contribute to reduce inter-centre variations in SPR prediction.

Reference values for low muscle mass and myosteatosis using tomographic muscle measurements in living kidney donors

Low muscle mass and myosteatosis are associated with poor clinical outcomes. Computed tomography (CT) imaging is an objective method for muscle mass and quality assessment; however consensus on cut-off values is lacking. This study assessed age-, sex-, and body mass index (BMI)-specific reference values of skeletal muscle parameters and correlated muscle mass with 24-h urinary creatinine excretion (24-h UCE). In total, 960 healthy subjects were included in this study. Muscle mass and quality were determined using axial CT slices at the vertebral level L3. The muscle area was indexed for height (skeletal muscle index [SMI]). The mean age was 53 ± 11 years, and 50% were male. The SMI reference values for low muscle mass in males were 38.8 cm2/m2 (20–29 years), 39.2 (30–39 years), 39.9 (40–49 years), 39.0 (50–59 years), 37.0 (60–69 years), and 36.8 (70–79 years). For females, these reference values were 37.5 cm2/m2 (20–29 years), 35.5 (30–39 years), 32.8 (40–49 years), 33.2 (50–59 years), 31.2 (60–69 years), and 31.5 (70–79 years). 24-h UCE and SMI were significantly correlated (r = 0.54, p < 0.001) without bias between the two methods of assessing muscle mass. This study provides age-, sex-, and BMI-specific reference values for skeletal muscle parameters that will support clinical decision making.

Development and External Validation of a Deep Learning Algorithm to Identify and Localize Subarachnoid Hemorrhage on CT Scans.

In medical imaging, a limited number of trained deep learning algorithms have been externally validated and released publicly. We hypothesized that a deep learning algorithm can be trained to identify and localize subarachnoid hemorrhage (SAH) on head computed tomography (CT) scans and that the trained model performs satisfactorily when tested using external and real-world data. We used noncontrast head CT images of patients admitted to Helsinki University Hospital between 2012 and 2017. We manually segmented (i.e., delineated) SAH on 90 head CT scans and used the segmented CT scans together with 22 negative (no SAH) control CT scans in training an open-source convolutional neural network (U-Net) to identify and localize SAH. We then tested the performance of the trained algorithm by using external data sets (137 SAH and 1,242 control cases) collected in 2 foreign countries and also by creating a data set of consecutive emergency head CT scans (8 SAH and 511 control cases) performed during on-call hours in 5 different domestic hospitals in September 2021. We assessed the algorithm's capability to identify SAH by calculating patient- and slice-level performance metrics, such as sensitivity and specificity. In the external validation set of 1,379 cases, the algorithm identified 136 of 137 SAH cases correctly (sensitivity 99.3% and specificity 63.2%). Of the 49,064 axial head CT slices, the algorithm identified and localized SAH in 1845 of 2,110 slices with SAH (sensitivity 87.4% and specificity 95.3%). Of 519 consecutive emergency head CT scans imaged in September 2021, the algorithm identified all 8 SAH cases correctly (sensitivity 100.0% and specificity 75.3%). The slice-level (27,167 axial slices in total) sensitivity and specificity were 87.3% and 98.8%, respectively, as the algorithm identified and localized SAH in 58 of 77 slices with SAH. The performance of the algorithm can be tested on through a web service. We show that the shared algorithm identifies SAH cases with a high sensitivity and that the slice-level specificity is high. In addition to openly sharing a high-performing deep learning algorithm, our work presents infrequently used approaches in designing, training, testing, and reporting deep learning algorithms developed for medical imaging diagnostics. This study provides Class III evidence that a deep learning algorithm correctly identifies the presence of subarachnoid hemorrhage on CT scan.

Interischial Spine Distance Is a Simple Index of the Narrow Pelvis That Can Predict Difficulty During Laparoscopic Low Anterior Resection.

A narrow pelvis makes laparoscopic rectal resection difficult. This study aimed to evaluate whether a simple measurement on computed tomography can predict procedural difficulty. A total of 62 patients with low rectal cancer underwent conventional laparoscopic low anterior resection. The interischial spine (IS) distance (ie, the distance between the ischial spines) was measured on an axial computed tomography slice. The operative time, blood loss, and time from the insertion of linear staplers to completion of clamping on the distal end of the rectum (clamp time) were compared between patients with narrow or wide pelvises. Overall, 42 men and 20 women with low rectal cancer were assessed. The mean tumor size was 34.5mm. Total or tumor-specific mesorectal excisions were performed in all cases; high ligation and resection of the inferior mesenteric arteries were carried out in 92% of patients. The mean operative time and blood loss were 206 minutes and 15mL, respectively. Four patients (6.5%) experienced postoperative complications, including 2 anastomotic leaks (3.2%). The mean IS distance was 93.3mm. In simple linear regression analysis, a shorter IS distance correlated with a longer operative time ( R2 =0.08, P =0.030) and the clamp time ( R2 =0.07, P =0.046). Using a receiver operating characteristic curve, a narrow pelvis was defined as an IS distance <94.7mm. Multivariate regression analysis revealed that an IS distance <94.7mm (odds ratio: 3.51; P =0.04) was independently associated with a longer clamp time. The IS distance is a simple and useful measurement for predicting the difficulty of laparoscopic low anterior resection.

Synthetic FDG-Positron Emission Tomography Images for Patients with Non-Small Cell Lung Cancer: A Deep Learning-Based Approach Using Computed Tomography Images

<h3>Purpose/Objective(s)</h3> Deep learning-based, medical image-to-image transformation has been limited so far to computed tomography (CT) / magnetic resonance (MR) translation, and few studies have been reported on Positron Emission Tomography (PET) synthesis. Synthetic ¹⁸F-FDG-PET (sFDG-PET) image from CT provides the advantage of lowering exposure, cost, and labor, and facilitating longitudinal follow-up, particularly for patients with limited access to a PET scanner. Furthermore, CT can be performed on demand, whereas FDG-PET entails advanced preparation. Nevertheless, sFDG-PET generation faces the impediment of determining a valid anatomy-to-function transformation. In this work, we present a novel, deep learning-based sFDG-PET generation method from lung CT images. <h3>Materials/Methods</h3> An IRB-approved retrospective study was conducted at our institution for patients undergoing stereotactic body radiotherapy for early-stage non-small cell lung cancer. For each patient, CT, measured PET images, and planning target (PTV) volumes were extracted using COMKAT Image Tool. A workflow to generate sFDG-PET images as outputs from CT inputs, referred to as 2C-UcGAN, was developed based on conditional generative adversarial network design, and consisted of a U-Net generator accepting a 2channel-input: axial CT slices as input in channel 1, and mediastinal window-adjusted CT slices in channel 2. Training patches (size = 128 × 128 × 128) were centered around the PTV, and training was performed on an Intel workstation, 128G of RAM, NVIDIA TITAN XP GPU, Epoch = 50, using a programming environment. Standardized uptake values (SUVs) were calculated. A classic pix2pix network was used as a benchmark for comparison. Mean absolute error (MAE), root mean square error (RMSE), and correlation coefficient (R) of SUVs normalized to body weight were compared, in addition to sensitivity (Sen), specificity (Spe), positive and negative predictive values (PPV and NPV) for PTV uptake. <h3>Results</h3> A total of 165 patients were included and randomly divided into training (n=100), validation (n=19), and testing (n=46). Among the testing patients, 15 nodules were not ¹⁸F-FDG-PET avid. Results are listed in Table 1. Prediction time was in the range of a second. 2C-UcGAN outperformed pix2pix with a MAE of 1.21 g/mL and yielded higher PTV Sen and PPV than pix2pix, with acceptable Spe and NPV. <h3>Conclusion</h3> Using an intelligent combination of windowed CT radiological features, promising results are attained for metabolic characteristic prediction. The achieved MAE presents a considerable improvement in accuracy compared to the previously reported algorithm. Given its potential for implementing CT-only follow-up, further studies are warranted across different patient populations.

Usage of image registration and three-dimensional visualization tools on serial computed tomography for the analysis of patients with traumatic intraparenchymal hemorrhages

The aim of this study was to apply registration and three-dimensional (3D) display tools to assess the evolution of intraparenchymal hemorrhage (IPH) in patients with traumatic brain injury (TBI). We identified 109 TBI patients who had two computed tomography (CT) scans within 4 days retrospectively. The IPH was manually outlined. The registration was performed in 39 lesions from 29 patients with lesion volume < 1.5 cm on both baseline and follow-up CT. The center of mass (COM) of each lesion was calculated, and the distance between baseline and follow-up CT was used to evaluate the registration effect. The mean distances of COM before registration in the XYZ, XY, and YZ coordinates were 20.5 ± 10.2 mm, 17.8 ± 9.4 mm, and 15.9 ± 9.4 mm, respectively, which decreased significantly (p < 0.001) to 7.9 ± 4.9, 7.8 ± 5.0, and 6.1 ± 4.1 mm after registration. A 3D short video displaying the rendering view of all lesions in 34 randomly selected patients from baseline and follow-up scans were presented side-by-side for comparison. The detection rate of new IPH lesions increased in 3D videos (100%) as compared with axial CT slices (78.6–92.9%). A very high interrater agreement (k = 0.856) on perceiving IPH lesion progression upon viewing 3D video was noted, and the absolute volume increase was significantly higher (p < 0.001) for progressive lesions (median 7.36 cc) over non-progressive lesions (median 0.01 cc). Compared to patients with spontaneous hemorrhagic stroke, evaluation of multiple small traumatic hemorrhages in TBI is more challenging. The applied image analysis and visualization methods may provide helpful tools for comparing changes between serial CT scans.

Does Fracture Severity of Intertrochanteric Fracture in Elderly Caused by Low-Energy Trauma Affected by Gluteus Muscle Volume?

PurposeThe aim of this study was to determine whether there is a correlation between the type and stability of intertrochanteric fractures caused by low-energy trauma and gluteus muscle volume.Materials and MethodsA total of 205 elderly (>65 years) patients with intertrochanteric fractures caused by low-energy trauma treated from January 2018 to December 2020 were included in this study. The mean age of patients was 81.24 years (range, 65-100 years). Fractures were classified according to the Jensen modification of the Evans classification. The cross-sectional area of the contralateral gluteus muscle (minimus, medius, and maximus) was measured in preoperative axial computed tomography slices. An analysis and comparison of age, body mass index (BMI), weight, height, and the gluteus muscle area in each fracture type group was performed.ResultsIn the uni-variable analysis, statistically significant taller height was observed in patients in the stable intertrochanteric fracture (modified Evans 1 and 2) group compared with those in the unstable intertrochanteric fracture (modified Evans 3, 4, and 5) group (P<0.05). In addition, significantly higher BMI-adjusted gluteus muscle area (gluteus muscle area/BMI) was observed for the stable intertrochanteric fracture group compared with the unstable intertrochanteric fracture group except for the BMI-adjusted gluteus minimus area (P=0.112). In multivariable analysis, only the BMI-adjusted gluteus maximus (P=0.042) and total gluteus areas (P=0.035) were significantly higher in the stable group.ConclusionGluteal muscularity around the hip, especially the gluteus maximus, had a significant effect on the stability of intertrochanteric fractures.

Development of a fully automatic deep learning system for L3 selection and body composition assessment on computed tomography

As sarcopenia research has been gaining emphasis, the need for quantification of abdominal muscle on computed tomography (CT) is increasing. Thus, a fully automated system to select L3 slice and segment muscle in an end-to-end manner is demanded. We aimed to develop a deep learning model (DLM) to select the L3 slice with consideration of anatomic variations and to segment cross-sectional areas (CSAs) of abdominal muscle and fat. Our DLM, named L3SEG-net, was composed of a YOLOv3-based algorithm for selecting the L3 slice and a fully convolutional network (FCN)-based algorithm for segmentation. The YOLOv3-based algorithm was developed via supervised learning using a training dataset (n = 922), and the FCN-based algorithm was transferred from prior work. Our L3SEG-net was validated with internal (n = 496) and external validation (n = 586) datasets. Ground truth L3 level CT slice and anatomic variation were identified by a board-certified radiologist. L3 slice selection accuracy was evaluated by the distance difference between ground truths and DLM-derived results. Technical success for L3 slice selection was defined when the distance difference was < 10 mm. Overall segmentation accuracy was evaluated by CSA error and DSC value. The influence of anatomic variations on DLM performance was evaluated. In the internal and external validation datasets, the accuracy of automatic L3 slice selection was high, with mean distance differences of 3.7 ± 8.4 mm and 4.1 ± 8.3 mm, respectively, and with technical success rates of 93.1% and 92.3%, respectively. However, in the subgroup analysis of anatomic variations, the L3 slice selection accuracy decreased, with distance differences of 12.4 ± 15.4 mm and 12.1 ± 14.6 mm, respectively, and with technical success rates of 67.2% and 67.9%, respectively. The overall segmentation accuracy of abdominal muscle areas was excellent regardless of anatomic variation, with CSA errors of 1.38–3.10 cm2. A fully automatic system was developed for the selection of an exact axial CT slice at the L3 vertebral level and the segmentation of abdominal muscle areas.

Three-Dimensional Measures of Bony Resection During Femoral Osteochondroplasty Are Related to Alpha Angle Measures: A Cadaveric Study

PurposeTo determine whether 3-dimensional (3D)–reconstructed proximal femoral bone models can be used to quantify femoral osteochondroplasty and to determine whether the 3D-based metrics are related to clinical alpha angle measures.MethodsSix cadaveric specimens with cam-type morphology underwent open femoral osteochondroplasty. Alpha angles were measured on the oblique axial computed tomography slice before and after femoral osteochondroplasty. Preoperative and postoperative computed tomography–based 3D reconstructed femur models were generated for each cadaveric specimen. A 3D-3D registration technique was used to merge the preoperative and postoperative models to measure the surface-to-surface distance between the model surfaces. Bivariate correlation analyses were used to determine the correlations between the preoperative, and the difference between the preoperative and postoperative alpha angle (Δ alpha angle) measures and each of the femoral osteochondroplasty variables of surface area (mm2), volume (mm3), maximum height (mm), and mean height (mm). The strength of the bivariate correlations was defined as follows: weak 0.1 to 0.3, moderate 0.3 to 0.5, and strong as 0.5 to 1.00.ResultsBivariate correlations revealed a strong positive correlation between preoperative alpha angle with femoral osteochondroplasty volume (r = 0.899, P = .007) and surface area (r = 0.899, P = .007). No significant correlations were found between the change in alpha angle and the osteochondroplasty variables.ConclusionsIn this study, pre- and postoperative 3D bone models could be used to quantify femoral osteochondroplasty and to determine if the 3D-based metrics are related to clinical alpha angle measures.Clinical Relevance3D-reconstructed image bone models may be helpful to ensure that adequate femoroplasty is performed intraoperatively, in particular with arthroscopic approach in which visualization may be challenging due to capsular management issues and surgeon experience.

Utility of Preoperative Computed Tomography-Based Body Metrics in Relation to Postoperative Complications in Pediatric Liver Transplantation Recipients.

Computed tomography (CT)‐derived body metrics such as skeletal muscle index (SMI), psoas muscle index (PMI), and subcutaneous fat area index (ScFI) are measurable components of sarcopenia, frailty, and nutrition. While these body metrics are advocated in adults for predicting postoperative outcomes after liver transplantation (LT), little is known about their value in pediatric populations. This study assessed the relation between preoperative CT‐based body metrics and postoperative short‐term outcomes in pediatric LT recipients. Patients aged 0‐18 years who underwent a primary LT were retrospectively included (n = 101; median age 0.5 years; range 0.2‐17.1). SMI, PMI, and ScFI were derived from preoperative axial CT slices. Postoperative outcomes and complications within 90 days were correlated with the CT‐based body metrics. To classify postoperative infections, the Clavien‐Dindo (CD) classification was used. Subgroup analyses were performed for age groups (<1, 1‐10, and >10 years old). An optimal threshold for test performance was defined using Youden’s J‐statistic and receiver operating characteristic curve as appropriate. ScFI was significantly (P = 0.001) correlated with moderate to severe postoperative infections (CD grade 3‐5) in children aged <1 year, with the optimal ScFI threshold being ≤27.1 cm2/m2 (sensitivity 80.4% and specificity 77.8%). A weak negative correlation between SMI and the total duration of hospital stay (R = −0.3; P = 0.01) and intensive care unit (ICU) stay (R = −0.3; P = 0.01) was observed in children aged <1 year. No other associations between CT‐based body metrics and postoperative outcomes were shown. In children aged <1 year with cirrhotic liver disease undergoing LT, preoperative CT‐based body metrics were correlated with moderate to severe postoperative infections (ScFI) and with longer duration of hospital and ICU stay (SMI), and thus can be considered important tools for pre‐LT risk assessment.

Preoperative planning of total knee arthroplasty: reliability of axial alignment using a three-dimensional planning approach.

Preoperative templating of total knee arthroplasty (TKA) can nowadays be performed three-dimensionally with software solutions using computed tomography (CT) datasets. Currently there is no consensus concerning the axial orientation of TKA components in three-dimensional (3D) planning. To assess intra-/inter-observer reliability of detection of different bony landmarks in planning axial component alignment using axial CT images and 3D reconstructions. Intra- and inter-observer reliability of determination of four predefined axial femoral and tibial axes was calculated using data from CT scans. Axes determination was performed on the axial slices and on the 3D reconstruction using preoperative planning software. In summary, 61 datasets were analyzed by one medical student (intra-observer reliability) and 15 datasets were analyzed by four different observers independently (inter-observer reliability). For the femur, clinical epicondylar axis and posterior condylar axis showed the best reliability with an inter-observer variability of 0.7° and 0.5°, respectively. For the tibia, posterior condylar axis provided best reliability (inter-observer variability: 1.7°). Overall variability was greater for tibial than for femoral axes. Reliability of axis determination was more accurate using axial CT slices rather than 3D reconstructions. The femoral clinical epicondylar axis is highly reliable. Landmarks for the tibia are not as easily identifiable as for the femur. The tibial posterior condylar axis presents the axis with highest reliability. Based on these results, clinical epicondylar axis for orientation of the femoral TKA component and posterior condylar axis for the tibial implant, both defined on axial slices can be recommended.

Effects of implant rotational malposition on contact surface area after implantation of the augmented glenoid baseplate in the setting of glenoid bone loss.

Augmented glenoid baseplates are utilized in reverse total shoulder arthroplasty in the setting of glenoid bone loss. These implants permit lateralization of the joint line and correction of bony version abnormalities. To allow bone preservation in the setting of abnormal bony version or deficiency, the backside of the augmented glenoid baseplate is not perpendicular to the axis of the central post/screw. Thus, if the baseplate is implanted with any rotational malposition, this could affect the backside contact area available for ingrowth. The purpose of this study was to assess if rotational malpositioning of a full-wedge augmented baseplate alongside the axis of the central screw significantly affects the glenoid implant backside contact area. Seven synthetic scapulas (Sawbones, Vashon, WA) were used to implant a 15° full-wedge glenoid baseplate (Wright Medical, Memphis, TN) according to the manufacturer's technique. The contact pressure between the baseplate and the glenoid surface at rotational positions 5°, 10°, and 15° clockwise (CW) and counterclockwise (CCW) from the central axis was measured with Extreme Low Fujifilm Prescale (Tekscan, Boston, MA). The data was analyzed digitally to obtain a percentage of contact surface area. To evaluate gross contact, a computed tomography (CT) scan was performed and manual measurements of contact between the glenoid and the baseplate were conducted using a standardized axial CT slice. The average contact area at zero degrees of malrotation was 37.26 ± 3.27%. Average contact areas for the simulated malposition cases were 13.99 ± 9.39% at 15° CCW, 24.89 ± 5.11% at 10° CW, and 19.32 ± 3.13% at 15° CW. Each of these results was significant (p < 0.003). On computed tomography, at 15° CCW, the contact area decreased by 39%; at 15° CW, the contact area decreased by 38%. The use of augmented glenoid baseplates presents a technical challenge. It is difficult to avoid implant malrotation along the axis of the central peg/screw, because the final rotation of the baseplate must be chosen while the implant is several centimeters away from the bone. This study found that 10° and 15° malrotation about the glenoid baseplate's central axis leads to significant decreases in the implant-bone contact area. When implanting an augmented baseplate for total shoulder arthroplasty, it is important to minimize baseplate malrotation to decrease the risk of baseplate loosening.

Linear Anterior-Posterior Computed Tomography Parameters Used to Quantify Trochlear Dysplasia Are More Reliable Than Angular Measurements

(1) To evaluate the reliability of 9 commonly used quantitative parameters of the trochlear morphology on computed tomography (CT) and (2) to analyze for differences in the reliability regarding patient subgroups (patellofemoral instability [PFI] vs non-PFI). A retrospective analysis of lower-limb CT scans performed between August 1996 and February 2013 was performed. The CT scans of all patients with PFI and 30 randomly selected cases without a history of PFI (non-PFI) were included. The following measurements were performed on 1 proximal axial CT slice at the entrance of the trochlear groove and 1 slice 5 mm further distal: relative medial, central, and lateral trochlear height; trochlear depth; relative transverse trochlear shift; trochlear facet asymmetry; sulcus angle; and medial and lateral trochlear slope. Four investigators performed the measurements independently, and intraclass correlation coefficients (ICCs) were calculated for the entire study group, as well as for the PFI and non-PFI groups separately. In total, 66 cases (36 PFI cases) were included in the study. We found almost perfect inter-rater and intrarater agreement for the trochlear height on both axial CT slices (ICC, 0.831-0.977). For the other measurements, we found only fair reliability (ICC < 0.4) on the proximal CT slice, whereas on the distal CT slice, at least moderate reliability (ICC > 0.4) was observed. ICCs were lower for many parameters in the PFI group. Angular values were less reliable than linear values. In particular, measurements involving the medial facet (i.e., sulcus angle, medial trochlear slope, and trochlear facet asymmetry) were less reliable. When interpreting quantitative parameters defining the trochlear morphology, one must taken into account the considerably lower reliability of angular parameters such as the commonly used sulcus angle compared with linear measurements. Radiologic measurements are less reliable in cases of PFI than in subjects without instability. Level III, retrospective case-control study.

Automated size-specific dose estimates using deep learning image processing.

An automated vendor-independent system for dose monitoring in computed tomography (CT) medical examinations involving ionizing radiation is presented in this paper. The system provides precise size-specific dose estimates (SSDE) following the American Association of Physicists in Medicine regulations. Our dose management can operate on incomplete DICOM header metadata by retrieving necessary information from the dose report image by using optical character recognition. For the determination of the patient's effective diameter and water equivalent diameter, a convolutional neural network is employed for the semantic segmentation of the body area in axial CT slices. Validation experiments for the assessment of the SSDE determination and subsequent stages of our methodology involved a total of 335 CT series (60352 images) from both public databases and our clinical data. We obtained the mean body area segmentation accuracy of 0.9955 and Jaccard index of 0.9752, yielding a slice-wise mean absolute error of effective diameter below 2mm and water equivalent diameter at 1mm, both below 1%. Three modes of the SSDE determination approach were investigated and compared to the results provided by the commercial system GE DoseWatch in three different body region categories: head, chest, and abdomen. Statistical analysis was employed to point out some significant remarks, especially in the head category.

Transverse Pedicle Angle Is Associated With Pelvic Incidence and Increased in Lumbar Isthmic Spondylolisthesis.

Study Design:Retrospective radiographic review.Objectives:Investigate and quantify transverse pedicle angle (TPA), the medial-to-lateral pedicle angulation, and its potential association with pelvic incidence (PI) in patients with isthmic lumbar spondylolisthesis (ISLS) and compare to those with degenerative lumbar spondylolisthesis (DSLS) and controls.Methods:A total of 200 patients (64 ISLS, 70 DSLS, 66 control) were included. TPA was calculated at the L3-5 vertebral levels using axial computed tomography slices. PI was measured on lateral radiographs. Two independent observers completed the measurements. As a sensitivity analysis, TPA was also measured at the most cranial and caudal aspects of the L3-5 vertebral levels of a subset of participants (29 ISLS, 31 DSLS, 35 control) and the cranial to caudal change (ΔTPA) was calculated.Results:TPA values (mean ± SD) at L4 and L5 for ISLS (L4: 17.3° ± 3.7°, L5: 26.0° ± 5.2°) were significantly higher than those for the DSLS (L4: 14.3° ± 3.8°, L5: 22.2° ± 5.0°) and control (L4: 14.5° ± 3.9°, L5: 20.7° ± 3.8°) groups. TPA in the DSLS group was significantly higher than controls at L5, but not L4. High PI predicted wider TPA at L5 in both DSLS and ISLS. ΔTPA (mean ± SD) increased sequentially proceeding through the L3-5 spinal levels for the ISLS (L3: 6.8° ± 4.4°, L4: 8.7° ± 5.2°, L5: 15.6° ± 9.0°), DSLS (L3: 8.2° ± 6.0°, L4: 8.3° ± 5.9°, L5: 18.3° ± 7.2°), and control (L3: 6.8° ± 4.4°, L4: 8.2° ± 4.7°, L5: 17.7° ± 7.0°) groups.Conclusions:TPA was significantly increased in ISLS compared with DSLS and controls. High PI significantly predicted high TPA at the L5 vertebral level in ISLS and DSLS. ΔTPA increased sequentially proceeding through the lumbar spine across groups.

Deep learning method for localization and segmentation of abdominal CT

Computed Tomography (CT) imaging is widely used for studying body composition, i.e., the proportion of muscle and fat tissues with applications in areas such as nutrition or chemotherapy dose design. In particular, axial CT slices from the 3rd lumbar (L3) vertebral location are commonly used for body composition analysis. However, selection of the third lumbar vertebral slice and the segmentation of muscle/fat in the slice is a tedious operation if performed manually. The objective of this study is to automatically find the middle axial slice at L3 level from a full or partial body CT scan volume and segment the skeletal muscle (SM), subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT) and intermuscular adipose tissue (IMAT) on that slice. The proposed algorithm includes an L3 axial slice localization network followed by a muscle-fat segmentation network. The localization network is a fully convolutional classifier trained on more than 12,000 images. The segmentation network is a convolutional neural network with an encoder–decoder architecture. Three datasets with CT images taken for patients with different types of cancers are used for training and validation of the networks. The mean slice error of 0.87±2.54 was achieved for L3 slice localization on 1748 CT scan volumes. The performance of five class tissue segmentation network evaluated on two datasets with 1327 and 1202 test samples. The mean Jaccard score of 97% was achieved for SM and VAT tissue segmentation on 1327 images. The mean Jaccard scores of 98% and 83% are corresponding to SAT and IMAT tissue segmentation on the same dataset. The localization and segmentation network performance indicates the potential for fully automated body composition analysis with high accuracy.

Abdominal musculature segmentation and surface prediction from CT using deep learning for sarcopenia assessment

PurposeThe purpose of this study was to build and train a deep convolutional neural networks (CNN) algorithm to segment muscular body mass (MBM) to predict muscular surface from a two-dimensional axial computed tomography (CT) slice through L3 vertebra. Materials and methodsAn ensemble of 15 deep learning models with a two-dimensional U-net architecture with a 4-level depth and 18 initial filters were trained to segment MBM. The muscular surface values were computed from the predicted masks and corrected with the algorithm's estimated bias. Resulting mask prediction and surface prediction were assessed using Dice similarity coefficient (DSC) and root mean squared error (RMSE) scores respectively using ground truth masks as standards of reference. ResultsA total of 1025 individual CT slices were used for training and validation and 500 additional axial CT slices were used for testing. The obtained mean DSC and RMSE on the test set were 0.97 and 3.7 cm2 respectively. ConclusionDeep learning methods using convolutional neural networks algorithm enable a robust and automated extraction of CT derived MBM for sarcopenia assessment, which could be implemented in a clinical workflow.

Single-slice CT measurements allow for accurate assessment of sarcopenia and body composition.

To evaluate the correlation between simple planimetric measurements in axial computed tomography (CT) slices and measurements of patient body composition and anthropometric data performed with bioelectrical impedance analysis (BIA) and metric clinical assessments. In this prospective cross-sectional study, we analyzed data of a cohort of 62 consecutive, untreated adult patients with advanced malignant melanoma who underwent concurrent BIA assessments at their radiologic baseline staging by CT between July 2016 and October 2017. To assess muscle and adipose tissue mass, we analyzed the areas of the paraspinal muscles as well as the cross-sectional total patient area in a single CT slice at the height of the third lumbar vertebra. These measurements were subsequently correlated with anthropometric (body weight) and body composition parameters derived from BIA (muscle mass, fat mass, fat-free mass, and visceral fat mass). Linear regression models were built to allow for estimation of each parameter based on CT measurements. Linear regression models allowed for accurate prediction of patient body weight (adjusted R2 = 0.886), absolute muscle mass (adjusted R2 = 0.866), fat-free mass (adjusted R2 = 0.855), and total as well as visceral fat mass (adjusted R2 = 0.887 and 0.839, respectively). Our data suggest that patient body composition can accurately and quantitatively be determined by using simple measurements in a single axial CT slice. This could be useful in various medical and scientific settings, where the knowledge of the patient's anthropometric parameters is not immediately or easily available. • Easy to perform measurements on a single CT slice highly correlate with clinically valuable parameters of body composition. • Body composition data were acquired using bioelectrical impedance analysis to correlate CT measurements with a non-imaging-based method, which is frequently lacking in previous studies. • The obtained equations facilitate a quick, opportunistic assessment of relevant parameters of body composition.