Development and Implementation of a Maryland State Program Providing Hospital Payment Incentives for Reduction in Readmission Disparities.

BACKGROUND: Social factors are a key determinant of hospital readmission. We describe the development of the country's first statewide policy providing hospitals with financial incentives to reduce readmission disparities. OBJECTIVE: To describe the development and evaluation of a novel program that measures hospital-level disparity in readmission and rewards hospitals for improvement. RESEARCH DESIGN: Observational study using inpatient claims. PARTICIPANTS: Baseline data included 454,372 all-cause inpatient discharges in 2018 and 2019. Of the included discharges, 34.01% involved Black patients, 40.44% involved female patients, 33.1% involved patients covered by Medicaid, and 11.76% involved patients who were readmitted. Mean age was 55.18. MEASURES: The key measure was the percentage change over time within the hospital in readmission disparity. Readmission disparity was measured using a multilevel model that gauged the association between social factors and readmission risk at a given hospital. Three social factors (Race, Medicaid coverage, and Area Deprivation Index) were combined into an index reflecting exposure to social adversity. RESULTS: Of the State's 45 acute-care hospitals, 26 exhibited improved disparity performance in 2019. LIMITATIONS: The program is limited to inpatients within a single state; the analysis does not provide evidence on the causal relationship between the intervention and readmission disparities. CONCLUSION: This represents the first large-scale effort in the US to link disparities to hospital payment. Because the methodology relies on claims data, it could easily be adopted elsewhere. The incentives are directed to within-hospital disparities, thus mitigating concerns about penalizing hospitals with patients with greater social exposure. This methodology could be used to measure disparity in other outcomes.

Automated development of the contrast-detail curve based on statistical low-contrast detectability in CT images.

PURPOSE: We have developed a software to automatically find the contrast-detail (C-D) curve based on the statistical low-contrast detectability (LCD) in images of computed tomography (CT) phantoms at multiple cell sizes and to generate minimum detectable contrast (MDC) characteristics. METHODS: A simple graphical user interface was developed to set the initial parameters needed to create multiple grid region of interest of various cell sizes with a 2-pixel increment. For each cell in the grid, the average CT number was calculated to obtain the standard deviation (SD). Detectability was then calculated by multiplying the SD of the mean CT numbers by 3.29. This process was automatically repeated as many times as the cell size was set at initialization. Based on the obtained LCD, the C-D curve was obtained and the target size at an MDC of 0.6% (i.e., 6-HU difference) was determined. We subsequently investigated the consistency of the target sizes for a 0.6% MDC at four locations within the homogeneous image. We applied the software to images with six noise levels, images of two modules of the American College of Radiology CT phantom, images of four different phantoms, and images of four different CT scanners. We compared the target sizes at a 0.6% MDC based on the statistical LCD and the results from a human observer. RESULTS: The developed system was able to measure C-D curves from different phantoms and scanners. We found that the C-D curves follow a power-law fit. We found that higher noise levels resulted in a higher MDC for a target of the same size. The low-contrast module image had a slightly higher MDC than the distance module image. The minimum size of an object detected by visual observation was slightly larger than the size using statistical LCD. CONCLUSIONS: The statistical LCD measurement method can generate a C-D curve automatically, quickly, and objectively.

Automated procedure for slice thickness verification of computed tomography images: Variations of slice thickness, position from iso-center, and reconstruction filter.

PURPOSE: The purpose of this study is to automate the slice thickness verification on the AAPM CT performance phantom and validate it for variations of slice thickness, position from iso-center, and reconstruction filter. METHODS: An automatic procedure for slice thickness verification on AAPM CT performance phantom was developed using MATLAB R2015b. The stair object image within the phantom was segmented, and the middle stair object was located. Its angle was determined using the Hough transformation, and the image was rotated accordingly. The profile through this object was obtained, and its full-width of half maximum (FWHM) was automatically measured. The FWHM indicated the slice thickness of the image. The automated procedure was applied with variations in three independent parameters, i.e., the slice thickness, the distance from the phantom to the iso-center, and the reconstruction filter. The automated results were compared to manual measurements made using electronic calipers. RESULTS: The differences of the automated results from the nominal slice thicknesses were within 1.0 mm. The automated results are comparable to those from manual approach (i.e., the difference of both is within 12%). The automatic procedure accurately obtained slice thickness even when the phantom was moved from the iso-center position by up to 4 cm above and 4 cm below the iso-center. The automated results were similar (to within 0.1 mm) for various reconstruction filters. CONCLUSIONS: We successfully developed an automated procedure of slice thickness verification and confirmed that the automated procedure provided accurate results. It provided an easy and effective method of determining slice thickness.

An improved method for automated calculation of the water-equivalent diameter for estimating size-specific dose in CT.

PURPOSE: The aim of this study is to propose an algorithm for the automated calculation of water-equivalent diameter (Dw ) and size-specific dose estimation (SSDE) from clinical computed tomography (CT) images containing one or more substantial body part. METHODS: All CT datasets were retrospectively acquired by the Toshiba Aquilion 128 CT scanner. The proposed algorithm consisted of a contouring stage for the Dw calculation, carried out by taking the six largest objects in the cross-sectional image of the patient's body, followed by the removal of the CT table depending on the center position (y-axis) of each object. Validation of the proposed algorithm used images of patients who had undergone chest examination with both arms raised up, one arm placed down and both arms placed down, images of the pelvic region consisting of one substantial object, and images of the lower extremities consisting of two separated areas. RESULTS: The proposed algorithm gave the same results for Dw and SSDE as the previous algorithm when images consisted of one substantial body part. However, when images consisted of more than one substantial body part, the new algorithm was able to detect all parts of the patient within the image. The Dw values from the proposed algorithm were 9.5%, 15.4%, and 39.6% greater than the previous algorithm for the chest region with one arm placed down, both arms placed down, and images with two legs, respectively. The SSDE values from the proposed algorithm were 8.2%, 11.2%, and 20.6% lower than the previous algorithm for the same images, respectively. CONCLUSIONS: We have presented an improved algorithm for automated calculation of Dw and SSDE. The proposed algorithm is more general and gives accurate results for both Dw and SSDE whether the CT images contain one or more than one substantial body part.

Cost Awareness of Common Supplies Is Severely Impaired Among All Members of the Surgical Team.

BACKGROUND: Increased health care spending concerns have generated interest in reducing operating room (OR) costs, but the cost awareness of the surgical team selecting intraoperative supplies remains unclear. This work characterizes knowledge of supply cost among surgeons and OR staff in a large academic hospital and seeks to examine the role of experience and training with regards to cost insight. METHODS: This work is a cross-sectional study of surgeons, trainees, nurses, and surgical technicians (n = 372) across all surgical specialties at a large academic hospital. Participants completed a survey reporting frequency of use and estimated cost for 11 common surgical supplies as well as opinions on access to cost information in the OR. Cost estimation error was expressed as the ratio of estimated-to-actual cost, and groups were compared with one-way analysis of variance and chi-squared testing. Spearman correlation (ρ) was used to describe the relationship between monotonic variables. RESULTS: Overestimation error was universal and ranged widely (3.80-49.79). There was no significant difference in estimation accuracy when stratified by role or years of experience. Less expensive items had higher rates of estimation error than more expensive items (P < 0.001), and a moderately strong relationship was found between decreased item cost and increased estimation error (ρ: 0.49). The overwhelming majority (91%) of respondents expressed a desire to learn more about supply pricing. CONCLUSIONS: Price knowledge of common supplies is globally impaired for entire surgical team but coexists with a strong desire to augment cost awareness. Improved access to cost information has a high potential to inform surgical decision-making and decrease OR waste.

Safety-Net Hospitals, Neighborhood Disadvantage, and Readmissions Under Maryland's All-Payer Program: An Observational Study.

Background: Safety-net hospitals have higher-than-expected readmission rates. The relative roles of the mean disadvantage of neighborhoods the hospitals serve and the disadvantage of individual patients in predicting a patient's readmission are unclear. Objective: To examine the independent contributions of the patient's neighborhood and the hospital's service area to risk for 30-day readmission. Design: Retrospective observational study. Setting: Maryland. Participants: All Maryland residents discharged from a Maryland hospital in 2015. Measurements: Predictors included the disadvantage of neighborhoods for each Maryland resident (area disadvantage index) and the mean disadvantage of each hospital's discharged patients (safety-net index). The primary outcome was unplanned 30-day hospital readmission. Generalized estimating equations and marginal modeling were used to estimate readmission rates. Results were adjusted for clinical readmission risk. Results: 13.4% of discharged patients were readmitted within 30 days. Patients living in neighborhoods at the 90th percentile of disadvantage had a readmission rate of 14.1% (95% CI, 13.6% to 14.5%) compared with 12.5% (CI, 11.8% to 13.2%) for similar patients living in neighborhoods at the 10th percentile. Patients discharged from hospitals at the 90th percentile of safety-net status had a readmission rate of 14.8% (CI, 13.4% to 16.1%) compared with 11.6% (CI, 10.5% to 12.7%) for similar patients discharged from hospitals at the 10th percentile of safety-net status. The association of readmission risk with the hospital's safety-net index was approximately twice the observed association with the patient's neighborhood disadvantage status. Limitations: Generalizability outside Maryland is unknown. Confounding may be present. Conclusion: In Maryland, residing in a disadvantaged neighborhood and being discharged from a hospital serving a large proportion of disadvantaged neighborhoods are independently associated with increased risk for readmission. Primary Funding Source: National Institute on Minority Health and Health Disparities and Maryland Health Services Cost Review Commission.

Comparison of central, peripheral, and weighted size-specific dose in CT.

The objective of this study is to determine X-ray dose distribution and the correlation between central, peripheral and weighted-centre peripheral doses for various phantom sizes and tube voltages in computed tomography (CT). We used phantoms developed in-house, with various water-equivalent diameters (Dw) from 8.5 up to 42.1 cm. The phantoms have one hole in the centre and four holes at the periphery. By using these five holes, it is possible to measure the size-specific central dose (Ds,c), peripheral dose (Ds,p), and weighted dose (Ds,w).The phantoms are scanned using a CT scanner (Siemens Somatom Definition AS), with the tube voltage varied from 80 up to 140 kVps. The doses are measured using a pencil ionization chamber (Ray safe X2 CT Sensor) in every hole for all phantoms. The relationships between Ds,c, Ds,p, and Ds,w, and the water-equivalent diameter are established. The size-conversion factors are calculated. Comparisons between Ds,c, Ds,p, and Ds,ware also established. We observe that the dose is relatively homogeneous over the phantom for water-equivalent diameters of 12-14 cm. For water-equivalent diameters less than 12 cm, the dose in the centre is higher than at the periphery, whereas for water-equivalent diameters greater than 14 cm, the dose at the centre is lower than that at the periphery. We also find that the distribution of the doses is influenced by the tube voltage. These dose distributions may be useful for calculating organ doses for specific patients using their CT images in future clinical practice.

An evaluation of computed tomography dose index measurements using a pencil ionisation chamber and small detectors.

The aim of this study was to compare the values of the computed tomography dose index 100 (CTDI100) obtained using two small detectors (i.e. a small ionisation chamber and a small solid state detector) with those obtained from a 100 mm pencil ionisation chamber for various input CT parameters: beam width, kVp, mAs, pitch, and head-body phantom variation. The measurement of CTDI100 using the 100 mm pencil chamber was carried out in a single rotation of axial mode, while the measurement using small detectors was carried out in helical mode. The differences of CTDI100 values obtained with two small detectors were about 7% for all variations. The differences of CTDI100 values obtained with small detectors and a 100 mm pencil ionisation chamber for beam widths of more than 4 mm were within 40%. However, for the narrowest beam widths (4 mm), the difference between them was very large (about 150%).

Development of a novel artifact-free eye shield based on silicon rubber-lead composition in the CT examination of the head.

The aim of this work was to develop a novel artifact-free eye shield and evaluate its effect on the dose received by the eye lens and the resulting image quality in the CT examination of the head. A new material for an eye shield was synthesised from silicon rubber (SR) and lead (Pb) using a simple method. The percentage of Pb was varied from 0 to 5% wt. An anthropomorphic head phantom was scanned with and without the SR-Pb eye shield, and compared with a tungsten paper (WP) eye shield. The distance from the eye shield and head was varied from 0 to 5 cm. The dose to the eye lens was measured using photo-luminescence detectors (PLDs). The presence of artifacts was determined by measuring CT numbers at different eye lens locations and by subtracting images with and without the eye shield. The dose reduction increases with increasing Pb content in the SR-Pb eye shield. A 5% wt SR-Pb eye shield reduced the eye lens dose by up to 50%, whereas the WP eye shield reduced the dose by up to 86%. The CT numbers in images with the SR-Pb eye shield in the regions of both eyes and the center of the head phantom is similar to those without the eye shield, indicating that there is no artifact in the resulting image. Using the WP eye shield, there is considerable artifact with the CT number increasing by up to 700% in the regions of both eyes and the center of the head. It is found that the distance between the SR-Pb eye shield and the head does not affect either the dose or the resulting images. A SR-Pb-based eye shield can be applied in clinical environments and should be placed directly above the eye surface for dose optimisation.

Assessment of patient dose and noise level of clinical CT images: automated measurements.

We investigated comparisons between patient dose and noise in pelvic, abdominal, thoracic and head CT images using an automatic method. 113 patient images (37 pelvis, 34 abdominal, 25 thoracic, and 17 head examinations) were retrospectively and automatically examined in this study. Water-equivalent diameter (Dw), size-specific dose estimates (SSDE) and noise were automatically calculated from the center slice for every patient image. The Dw was calculated based on auto-contouring of the patients' edges, and the SSDE was calculated as the product of the volume CT dose index (CTDIvol) extracted from the Digital Imaging and Communications in Medicine (DICOM) header and the size conversion factor based on the Dw obtained from AAPM 204. The noise was automatically measured as a minimum standard deviation in the map of standard deviations. A square region of interest of about 1 cm2 was used in the automated noise measurement. The SSDE values for the pelvis, abdomen, thorax, and head were 21.8 ± 7.3 mGy, 22.0 ± 4.5 mGy, 21.5 ± 4.7 mGy, and 65.1 ± 1.7 mGy, respectively. The SSDEs for the pelvis, abdomen, and thorax increased linearly with increasing Dw, and for the head with constant tube current, the SSDE decreased with increasing Dw. The noise in the pelvis, abdomen, thorax, and head were 5.9 ± 1.5 HU, 5.2 ± 1.4 HU, 4.9 ± 0.8 HU and 3.9 ± 0.2 HU, respectively. The noise levels for the pelvis, abdomen, and thorax of the patients were relatively constant with Dw because of tube current modulation. The noise in the head image was also relatively constant because Dw variations in the head are very small. The automated approach provides a convenient and objective tool for dose optimizations.

An algorithm for automated modulation transfer function measurement using an edge of a PMMA phantom: Impact of field of view on spatial resolution of CT images.

PURPOSE: The purpose of this study was to introduce a new algorithm for automated measurement of the modulation transfer function (MTF) using an edge of a readily available phantom and to evaluate the effect of reconstruction filter and field of view (FOV) on the spatial resolution in the CT images. METHODS: Our automated MTF measurement consisted of several steps. The center of the image was established and an appropriate region of interest (ROI) designated. The edge spread function (ESF) was determined, and a suitably interpolated ESF curve was differentiated to obtain the line spread function (LSF). The LSF was Fourier transformed to obtain the MTF. All these steps were accomplished automatically without user intervention. The results of the automated MTF from the edge phantom were validated by comparing them with a point image, and the results of the automated calculation were validated by the standard fitting method. The automated MTF calculation was then applied to the images of two polymethyl methacrylate (PMMA) phantoms and a wire phantom which had been scanned by a Toshiba Alexion 4-slice CT scanner and reconstructed with various filter types and FOVs. RESULTS: The difference in the 50% MTF values obtained from the edge and point phantoms were within ±4%. The values from the automated and fitted methods agreed to within ±2%, indicating that the automated MTF calculation was accurate. The automated MTF calculation was able to differentiate MTF curves for various filters. The spatial resolution values were 0.37 ± 0.00, 0.71 ± 0.01, and 0.78 ± 0.01 cycles/mm for FC13, FC30 and FC52 filters, respectively. The spatial resolution of the images decrease linearly (R2  > 0.98) with increasing FOVs. CONCLUSION: An automated MTF method was successfully developed using an edge phantom, the PMMA phantom. The method is easy to implement in a clinical environment and is not influenced by user experience.

Automated Calculation of Water-equivalent Diameter (DW) Based on AAPM Task Group 220.

The purpose of this study is to accurately and effectively automate the calculation of the water-equivalent diameter (DW) from 3D CT images for estimating the size-specific dose. DW is the metric that characterizes the patient size and attenuation. In this study, DW was calculated for standard CTDI phantoms and patient images. Two types of phantom were used, one representing the head with a diameter of 16 cm and the other representing the body with a diameter of 32 cm. Images of 63 patients were also taken, 32 who had undergone a CT head examination and 31 who had undergone a CT thorax examination. There are three main parts to our algorithm for automated DW calculation. The first part is to read 3D images and convert the CT data into Hounsfield units (HU). The second part is to find the contour of the phantoms or patients automatically. And the third part is to automate the calculation of DW based on the automated contouring for every slice (DW,all). The results of this study show that the automated calculation of DW and the manual calculation are in good agreement for phantoms and patients. The differences between the automated calculation of DW and the manual calculation are less than 0.5%. The results of this study also show that the estimating of DW,all using DW,n=1 (central slice along longitudinal axis) produces percentage differences of -0.92% ± 3.37% and 6.75%± 1.92%, and estimating DW,all using DW,n=9 produces percentage differences of 0.23% ± 0.16% and 0.87% ± 0.36%, for thorax and head examinations, respectively. From this study, the percentage differences between normalized size-specific dose estimate for every slice (nSSDEall) and nSSDEn=1 are 0.74% ± 2.82% and -4.35% ± 1.18% for thorax and head examinations, respectively; between nSSDEall and nSSDEn=9 are 0.00% ± 0.46% and -0.60% ± 0.24% for thorax and head examinations, respectively.

Predictors of breastfeeding exclusivity among migrant and Canadian-born women: results from a multi-centre study.

The objective of this study was to examine and compare predictors of breastfeeding exclusivity among migrant and Canadian-born women. As part of a longitudinal study, a sample of 1184 mothers were recruited from 12 hospitals in Canada and completed questionnaires at 1 and 16 weeks post-partum that included diverse questions from the following domains: demographic, social, migration, obstetrical, breastfeeding and maternal mood. After bivariate analysis, multivariate logistic regression analysis was completed to examine and compare predictors of exclusive breastfeeding at 16 weeks post-partum. Among migrant women, factors predictive of breastfeeding exclusivity included non-refugee immigrant or asylum-seeking status, residence in Toronto or Vancouver, maternal age of ≥35 years, feels most comfortable in the country of origin or nowhere and higher Gender-related Development Index of the country of origin. Factors predictive of not exclusively breastfeeding included maternal age of <20 years, not planning to exclusively breastfeed, not making the decision to breastfeed before pregnancy and not exclusively breastfeeding at 1 week post-partum. Among Canadian-born women, factors predictive of a lower likelihood of breastfeeding exclusivity included not living with father of infant, infant neonatal intensive care unit admission, planned duration of exclusive breastfeeding for <6 months, not exclusively breastfeeding at 1 week post-partum and Edinburgh Postnatal Depression Scale score of ≥10. The only similar risk factor predicting a lower likelihood of breastfeeding exclusivity between migrant and Canadian-born women was not exclusively breastfeeding at 1 week post-partum; all other risk factors were dissimilar, suggesting that these groups might benefit from different strategies to optimise breastfeeding outcomes.

A novel method for developing contrast-detail curves from clinical patient images based on statistical low-contrast detectability.

Purpose. To develop a method to extract statistical low-contrast detectability (LCD) and contrast-detail (C-D) curves from clinical patient images.Method. We used the region of air surrounding the patient as an alternative for a homogeneous region within a patient. A simple graphical user interface (GUI) was created to set the initial configuration for region of interest (ROI), ROI size, and minimum detectable contrast (MDC). The process was started by segmenting the air surrounding the patient with a threshold between -980 HU (Hounsfield units) and -1024 HU to get an air mask. The mask was trimmed using the patient center coordinates to avoid distortion from the patient table. It was used to automatically place square ROIs of a predetermined size. The mean pixel values in HU within each ROI were calculated, and the standard deviation (SD) from all the means was obtained. The MDC for a particular target size was generated by multiplying the SD by 3.29. A C-D curve was obtained by iterating this process for the other ROI sizes. This method was applied to the homogeneous area from the uniformity module of an ACR CT phantom to find the correlation between the parameters inside and outside the phantom, for 30 thoracic, 26 abdominal, and 23 head images.Results. The phantom images showed a significant linear correlation between the LCDs obtained from outside and inside the phantom, with R2values of 0.67 and 0.99 for variations in tube currents and tube voltages. This indicated that the air region outside the phantom can act as a surrogate for the homogenous region inside the phantom to obtain the LCD and C-D curves.Conclusion. The C-D curves obtained from outside the ACR CT phantom show a strong linear correlation with those from inside the phantom. The proposed method can also be used to extract the LCD from patient images by using the region of air outside as a surrogate for a region inside the patient.

A challenge and solution for automatic thin slice thickness measurements on images of the Catphan phantom.

Purpose. The use of the Hough transform for angle detection is quite accurate for relatively wide slice thickness. However, the Hough transform fails to accurately detect the angle for thin slice thickness. This study proposes a method for automatically measuring the thickness of thin slices on images of a Catphan phantom.Methods. In the proposed method, the angle of the phantom's orientation was determined based on the relative coordinates of the four hole objects in the phantom. After the angles of the wires were determined, the profiles of pixel values across the wire objects were constructed. Finally, their full widths at half maximum (FWHMs) were determined and multiplied bytan23° to obtain the slice thicknesses of the images. The results of the proposed method were compared to a previous method, which used the Hough transform to obtain the phantom's orientation. We used slice thicknesses ranging from 0.8 mm to 5.0 mm, and phantom angles from 0° to 10°.Results. Our proposed method detected the angle of the phantom accurately for thin slices, whereas a previous method did not accurately detect the angle. The results of the slice thickness using this current method were slightly higher (within 7.9%) compared to the previous method. However, the results of the two methods did not differ significantly (p-value > 0.05). Using different angles, the current method detected all the angles more accurately. Again, the slice thicknesses were not significantly different from the previous method (p-value > 0.05).Conclusion. The proposed method for measuring the thickness of thin slices in an image of a Catphan phantom, based on the relative coordinates of the four hole objects in the phantom, outperformed a previous method based on the Hough transform.

A Practical Method for Slice Spacing Measurement Using the American Association of Physicists in Medicine Computed Tomography Performance Phantom.

Background: The slice spacing has a crucial role in the accuracy of computed tomography (CT) images in sagittal and coronal planes. However, there is no practical method for measuring the accuracy of the slice spacing. Purpose: This study proposes a novel method to automatically measure the slice spacing using the American Association of Physicists in Medicine (AAPM) CT performance phantom. Methods: The AAPM CT performance phantom module 610-04 was used to measure slice spacing. The process of slice spacing measurement involves a pair of axial images of the module containing ramp aluminum objects located at adjacent slice positions. The middle aluminum plate of each image was automatically segmented. Next, the two segmented images were combined to produce one image with two stair objects. The centroid coordinates of two stair objects were automatically determined. Subsequently, the distance between these two centroids was measured to directly indicate the slice spacing. For comparison, the slice spacing was calculated by accessing the slice position attributes from the DICOM header of both images. The proposed method was tested on phantom images with variations in slice spacing and field of view (FOV). Results: The results showed that the automatic measurement of slice spacing was quite accurate for all variations of slice spacing and FOV, with average differences of 9.0% and 9.3%, respectively. Conclusion: A new automated method for measuring the slice spacing using the AAPM CT phantom was successfully demonstrated and tested for variations of slice spacing and FOV. Slice spacing measurement may be considered an additional parameter to be checked in addition to other established parameters.

Prediction of duration of breastfeeding among migrant and Canadian-born women: results from a multi-center study.

OBJECTIVE: To examine and compare predictors of breastfeeding duration among migrant and Canadian-born women. STUDY DESIGN: As part of a longitudinal study, a sample of 1503 mothers was recruited from 12 hospitals in Canada who completed questionnaires at 1 and 16 weeks postpartum. Following bivariate analysis, multivariate logistic regression analyses were completed to examine and compare predictors of continued breastfeeding at 16 weeks postpartum among migrant and Canadian-born women. RESULTS: Among migrant women, factors predictive of breastfeeding duration included maternal age ≥ 35 years, primiparity, and breast engorgement pain at 1 week postpartum. Factors predictive of discontinued breastfeeding in this group included maternal age <20 years, higher gender-related development index of country of origin, no previous breastfeeding experience, breastfeeding duration of peers <6 months, planned duration of exclusive breastfeeding <6 months, and not exclusively breastfeeding at 1 week postpartum. Among Canadian-born women, factors predictive of breastfeeding duration included residence in Vancouver and maternal age ≥ 35 years. Factors predictive of discontinued breastfeeding included residence in Toronto, maternal age <20 years, smoking at 16 weeks postpartum, primiparity, planned duration of exclusive breastfeeding <6 months, and not exclusively breastfeeding at 1 week postpartum. CONCLUSIONS: Although certain predictors for breastfeeding duration were similar between migrant and Canadian-born women, several were dissimilar, suggesting that these groups might benefit from different strategies to optimize breastfeeding outcomes.

Algorithm development for automatic laser alignment assessment on an ACR CT phantom and its evaluation on sixteen CT scanners.

Purpose. The aim of this study is to develop software to automatically assess the laser alignment on the ACR CT phantom and evaluate its accuracy on sixteen CT scanners.Methods. Software for an automated method of laser alignment assessment on the ACR CT phantom was developed. Laser alignment assessment was based on the positions of the ball-bearing markers at the edge of the ACR CT phantom. The automatic assessment was performed using several steps, including segmentation to acquire the coordinates of the ball-bearing markers and determination of the distances between lines connecting them with lines through the center of the image. A comparison of the results from the automatic method with those from the manual method was performed. The manual measurements were carried out using MicroDicom Viewer. A Mann-Whitney U test was performed to determine the statistical difference between both methods. The evaluation was performed on images of the ACR CT phantom scanned with 16 CT scanners from 5 different CT manufacturers.Results. The results confirmed that our software successfully segments the ball-bearing markers and determines the laser alignment assessment on the ACR CT phantom. Evaluation of the algorithm with images from the 16 CT scanners revealed that the difference between the results from automatic and manual methods were about 0.2 mm with apvalue of around 0.7 (no statistical difference). Misalignment in they-axis was larger than the misalignment in the x-axisfor the majority of the scanners tested. It was found that the phantom tended to be placed 2 mm higher than the iso-center.Conclusions. Software to automatically assess CT laser alignment with the ACR CT phantom was successfully developed and evaluated. The automatic assessment was comparable to manual assessment. In addition, the automatic method was user independent and fast.

Automatic measurement of slice thickness in CT images of a Siemens phantom.

This study aims to develop a program in Python language for automatic measurement of slice thickness in computed tomography (CT) images of a Siemens phantom with different values of slice thickness, field of view (FOV), and pitch. A Siemens phantom was scanned using a Siemens 64-slice Somatom Perspective CT scanner with various slice thicknesses (i.e. 2, 4, 6, 8, and 10 mm), FOVs (i.e. 220, 260, and 300 mm), and pitch (i.e. 0.7, 0.9, and 1). Automatic measurement of slice thickness was performed by segmenting the ramp insert in the image and detecting angles of the ramp insert using the Hough transform. The resulting angles were subsequently used to rotate the image. Profiles of pixel along the ramp insert were made from the rotated images, and the slice thickness was calculated by determining the full-width at half maximum (FWHM) of the profiles. The product of the FWHM in pixels and the pixel size was corrected by the tangent of the ramp insert (i.e., 23°) to obtain the measured slice thickness. The results of the automatic measurements were compared with manual measurements carried out using a MicroDicom Viewer. The differences between the automatic and manual measurements at all slice thicknesses were less than 0.30 mm. The automatic and manual measurements had high linear correlations. For variations of the FOV and pitch, the differences between the automatic and manual measurement were less than 0.16 mm. The automatic and manual measurements were significantly different (p-value < 0.05) for slice thickness variation. In addition, the automatic and manual measurements were not significantly different (p-value > 0.05) for variations of FOV and pitch.

Impact of Noise Level on the Accuracy of Automated Measurement of CT Number Linearity on ACR CT and Computational Phantoms.

Background: Methods for segmentation, i.e., Full-segmentation (FS) and Segmentation-rotation (SR), are proposed for maintaining Computed Tomography (CT) number linearity. However, their effectiveness has not yet been tested against noise. Objective: This study aimed to evaluate the influence of noise on the accuracy of CT number linearity of the FS and SR methods on American College of Radiology (ACR) CT and computational phantoms. Material and Methods: This experimental study utilized two phantoms, ACR CT and computational phantoms. An ACR CT phantom was scanned by a 128-slice CT scanner with various tube currents from 80 to 200 mA to acquire various noises, with other constant parameters. The computational phantom was added by different Gaussian noises between 20 and 120 Hounsfield Units (HU). The CT number linearity was measured by the FS and SR methods, and the accuracy of CT number linearity was computed on two phantoms. Results: The two methods successfully segmented both phantoms at low noise, i.e., less than 60 HU. However, segmentation and measurement of CT number linearity are not accurate on a computational phantom using the FS method for more than 60-HU noise. The SR method is still accurate up to 120 HU of noise. Conclusion: The SR method outperformed the FS method to measure the CT number linearity due to its endurance in extreme noise.