Mcnemar Test Reliability Essay

1. Introduction

Change in land use and land cover (LULC) is gaining recognition as a key driver of environmental changes [1,2]. Preserving the environmental resources while maintaining or enhancing the economic and social benefits from their use is a present day challenge. For this reason, there is a need to understand the pattern and trends of LULC changes on the local, regional and global scales. Advances in remote sensing science and associated technologies have made it possible to obtain valuable spatiotemporal information on LULC. The search for methods used for producing accurate LULC and determining LULC change over time has been an important component of remote sensing research within the last two decades or so. However, classifying a remote sensing imagery still remains a challenge that depends on many factors such as complexity of landscape in a study area, the choice of remote sensing data, and image processing and classification approaches etc [3,4]. Quite often, LULC maps derived from remote sensing are judged insufficient in quality and, thus not trustworthy for quantitative environmental application purposes [5,6,7]. This has led to questioning of the spectral and radiometric suitability of remotely sensed data sets for thematic mapping. This means that specific types of change must be identified using aerial photography and ground reconnaissance [5]. Wilkinson [7], based on a review of 15 years of peer-reviewed experiments on satellite image classification, observed that there has been no demonstrable improvement in classification performance over this 15-year period although a considerable inventiveness had occurred in establishing and testing new classification methods during the period [7]. This raises some doubts about the value of continued research efforts to improve classification algorithms in remote sensing. Jensen [8] opined that low reliability of remote sensing classification is not surprising because 95% of the scientists attempt to accomplish classification only using one variable i.e., spectral characteristic (colour) or black and white tone. However, other researchers have utilised ancillary data in combination with remote sensing data to improve classification accuracy [9,10,11,12,13,14]. This study is therefore built on the premise that the use of ancillary data combined with spectral and contextual knowledge will improve the overall accuracy of LULC classification.

Landsat TM/ETM+ spectral data are frequently used for LULC classification on regional scales [4,9,12,14,15] due to their relatively lower cost, longer history and higher frequency of archives. This is more important because information regarding the LULC over time and space is a fundamental requirement for environmental monitoring in order to prevent detrimental environmental impacts before they become irreparable. In this study, the Landsat TM data were classified with the most widely used parametric classifier, maximum likelihood decision rule and some ancillary data (e.g., DEM and knowledge of the locality, Land use data, vegetation index and textural analysis of the Landsat images) were combined through an expert (or hypothesis testing) system to improve the classification accuracy so that these classified maps could be used for detailed post-classification change detection. The aim of this paper was therefore to assess the hypothesis that the use of ancillary data could lead to improvement of land use classification. This aim is particularly pertinent because good quality satellite imageries of the study region for specific periods of interest to us were not available due to cloud cover and atmospheric haziness-common phenomena in the study region.

2. Study Area

The study area, generally referred to as the ‘Hunter Wine Country Private Irrigation District’ (HWCPID), is located in the Lower Hunter region of New South Wales (NSW) Australia, about 160 km north of Sydney (Figure 1). The region currently contains the sixth largest urban area in Australia with diverse land uses and landscapes, the latter consisting of coastline, mountains, lakes, floodplains and a river and also includes the world’s largest coal exporting port. Mining and industrial manufacturing have been the source of the strong economic activity of the region [16]. The regional planning strategy was focused on provision of sufficient new urban development and employment to meet expected strong demand for growth in population from 515,000 persons in 2006 to an estimated 675,000 persons by 2031 [16]. A substantial proportion of this increase in population is expected to be settled in the HWCPID. The HWCPID, covering an approximately 379 km2, is located within an undulating plain of the Lower Hunter valley, centred on the little town of Pokolbin. Geographically it lies between 151°09'43" E to 151°24'58" E Longitude and 32°37'21" S to 32°51'45" S Latitude. In HWCPID land use ranges from viticulture and dairying to extensive grazing and forestry. Pastoral systems were the dominant agricultural land use in the region for past 100 years, while grape vines were introduced in the 1820s. However, the expansion of vineyards to their present level started in the latter half of the 20th century. Other land uses include livestock production for beef, and vegetable production. In order to protect the booming grape vine cultivation from drought, Pokolbin Pipeline Project (PPP) was established in 2000. The network was designed to supply water to nearly 400 properties spread throughout the project area (Ken Bray, personal communication, 2008).

Figure 1. Location of study area (HWCPID) in New South Wales as a Landsat TM image of 2005 (in RGB combination of bands 4, 3 and 5).

Figure 1. Location of study area (HWCPID) in New South Wales as a Landsat TM image of 2005 (in RGB combination of bands 4, 3 and 5).

The area has been gaining popularity as a tourist attraction due to the presence of numerous wineries, stretching grape vineyard beyond the horizon, and golf courses. However, Pokolbin’s image of a bucolic rural landscape with its varied mosaic of vineyards, pastures, scattered woodlands and wineries, has been threatened by the prospects of overdevelopment [17]. This creates concerns among the public, and has evoked the inevitable tradeoffs between development, economic growth and environmental quality.

3. Methods

3.1. Landsat, Ancillary and Reference Data

For the purpose of this study the orthocorrected Landsat images of following were procured: Landsat 5-MSS of January 8, 1985, Landsat 5-TM of August 6, 1995, and Landsat 5-TM of June 8, 2005. The study area is contained within the Landsat path 89, row 83. All images were re-projected to the new Australian Geodetic Datum GDA-1994 and were all re-sampled to a common nominal spatial grid of 25 m resolution using nearest neighbour technique. This was to facilitate the operations that would be required for the change detection analysis. The root mean square errors of re-sampling and re-projection of the images were less than 0.5 pixel, equivalent to approximately 7–15 m.

High resolution orthorectified aerial photographs acquired sometime between 2004 and 2006 were also procured from Plateau Images, Alstonville, New South Wales. Additionally, the following data were procured: black and white aerial photographs acquired in 1984, colour aerial photographs acquired in 1991 and 1998 (all from Department of Land, NSW Govt.), and the Singleton Land use geodatabase (currency 2000-2007) and digital elevation model (DEM) (from Department of Natural Resources, NSW Govt.). The aerial photographs were orthorectified using the above mentioned orthorectified aerial photographs (years 2004 to 2006). The aerial photographs for each time period were mosaicked as one image for convenience of projection. The resolutions of these aerial photographs were 2 m. The aerial photographs were mainly used as reference data and the Singleton Land use geodatabase and DEM were utilized as ancillary data for post-classification correction using knowledge base.

Table 1. LULC categories delineated for the classification.

LULC categoryDescription
WoodlandForest covers including tree cover along the creeks
Pasture/scrublandNatural and cultivated pastures, and scrubs with partial grassland
VineyardIrrigated and non irrigated vineyards
Built-upCommercial, and residential areas, and other areas with man-made structure; roads, railway lines
Water-bodyFarm dams, sewage ponds
Mine/quarryMining areas
OliveOlive plantations (for 2005 only)

3.2. LULC Classification Based on Maximum Likelihood Classifier

Maximum likelihood classifier (MLC) is the most widely adopted parametric classification algorithm [8,11,18,19,20]. For this reason we used MLC for the spectral classification of the Landsat images. Taking into account the spectral characteristics of the satellite images and existing knowledge of land use of the study area, six LULC categories (Table 1) were respectively identified and classified for 1985 and 1995 and seven for 2005, as the Olive category did not exist prior to 1995. Though this category covered only a small proportion of the region, we delineated it due to its expansion in recent years.

Jensen [8] has opined that the derivation of the level II classes of US Geological Survey Land-use Land-Cover Classification System, using Landsat TM data, is inappropriate due to the limitations imposed by the medium spatial resolution and the difficulty in interpretability. The same limitations were applicable in this study as Pasture/scrubland and Vineyard could not be separated into irrigated and non-irrigated ones, due to their noisy Landsat spectral signatures and difficulty in interpreting them. Of the two Landsat TM images used, one thermal band (band 6) was excluded prior to MLC classification. However, in the case of Landsat MSS image, all the four bands were used for classification. The aerial photograph corresponding to each year was used to identify the “true” LULC parcels on the ground used for training. In cases where a single pre-defined LULC category has a different spectral signature in different areas, multiple signatures were created, but were later merged into one signature for a given LULC category. We performed an evaluation of collected signatures through exploratory analysis of histogram, contingency matrix and computing signature separability using a given transformed divergence for a distance between signatures. Signatures were recollected if not producing satisfactory results. In the case of Water LULC category, signature was collected from a feature space of 2-5 band combination (non-parametric rule). Thresholding was also done which is the process of identifying the pixels in a classified image that are the most likely to be classified incorrectly [21]. The distance image and output thematic raster layer produced by MLC were used for thresholding. The tails of histograms (pixels that are most likely to be misclassified have the higher distance file values at the tail of the histogram of the distance image) were cut off interactively and saved and the removed pixels were viewed. Consequently there were only a few small speckles of the removed pixels. Once the collected signatures were comparatively satisfactory, multiple signatures were merged into one signature for a given LULC category and used for the classification.

3.3. Post-Classification Refinement Using Ancillary Data and Logic Rule

As the LULC maps were noisy due to similarities of the spectral responses of certain land cover categories such as Pasture/scrubland, Vineyard and Built-up (as discussed in section 4.1 below), a post-classification refinement was developed and applied using ancillary information using a hypothesis testing framework of Knowledge Engineer [21] to reduce classification errors. The hypothesis framework was constructed by using the Singleton land use map, DEM, textural analysis and NDVI (Normalized Difference Vegetation Index) derived from the Landsat images. The framework was further augmented by the use of the orthorectified aerial photographs. Through this framework the misclassified pixels of MLC were re-evaluated and correctly reclassified. The different post-classification procedures adopted for the mix-classified LULC categories, namely, Built-up and Vineyard, are described as follows.

3.3.1. Built-up post-classification correction

The Built-up LULC patches under Landsat images were generally characterized by high textural value resulting from variegation caused by different features such as buildings, street grids and urban corridors. This is in contrast to the homogenous Pastures which have little to no textural variation. In this study, the MLC maps had high commission error especially in low built-up areas. For this reason, a modified textural analysis [9] was used. However, in our case, the correction based on textural analysis was only applied for the low-built-up areas to avoid increasing the omission error. For this, an AOI (area of interest) was drawn around the high-built-up area, and using the logic rule shown in Figure 2, all Built-up pixels of MLC were retained as such in the post-classification corrected (PCC) map. The MLC Built-up patches in the rest of the study area were modified using the following logic rules: the Built-up pixels of MLC classification with texture value above some critical level (it was ≥5 for 1985 and 2005 Landsat images, and ≥20 in the case of 1995 images) are retained as new Built-up pixels (Figure 2a). The remainder of the MLC Built-up patches were reclassified based on their NDVI threshold values, for example, if NDVI is less than -0.05, then allocated it to Water-body, and if the NDVI value is between -0.05 and 0.15, then reclassified to Vineyard, otherwise to Pasture/scrubland (Figure 2b). These threshold values were determined by detailed inspection of the textural images and NDVI images derived from the respective Landsat imageries corresponding to the LULC categories of interest which was guided with the use of orthorectified aerial photograph of the nearby period.

Figure 2. Hypothesis testing framework for Built-up correction. In both (a) and (b): the left white box is the hypothesis being tested, the ellipses represent the conjunctive decision rules and right shaded boxes represent the variables used. (a) Built-up of MLC classification with the Landsat textural value above some critical level only is retained as Built-up of PCC classification in case of low Built-up areas. (b) Built-up of MLC classification which is not included as Built-up of PCC classification are reclassified to other LULC categories based on critical levels of NDVI values.

Figure 2. Hypothesis testing framework for Built-up correction. In both (a) and (b): the left white box is the hypothesis being tested, the ellipses represent the conjunctive decision rules and right shaded boxes represent the variables used. (a) Built-up of MLC classification with the Landsat textural value above some critical level only is retained as Built-up of PCC classification in case of low Built-up areas. (b) Built-up of MLC classification which is not included as Built-up of PCC classification are reclassified to other LULC categories based on critical levels of NDVI values.

The texture analysis [21] of the TM band was performed using a 3 × 3 moving window and the variance Equation (1): where xij = DN value of pixel (i, j); n = number of pixels in a window; and M is the mean of the moving window which is defined in Equation (2):

1Aga khan University, Karachi, Pakistan
2Division Servizio di Radiologia, IRCCS Policlinico San Donato, Organization Università degli Studi di Milano, Milano, Italy

Received 29 September 2013; Revised 17 December 2013; Accepted 25 December 2013; Published 6 May 2014

Copyright © 2014 Wasim Memon et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

The goal of this study is to prospectively assess the additional value of oblique reformatted images for localizing POT, having surgery as a reference standard. Materials and Methods. 102 consecutive patients with suspected small bowel obstruction (SBO) underwent 64-slice multidetector row CT (MDCT) using surgical findings as reference standard. Two independent GI radiologists reviewed the CT scans to localize the exact POT by evaluating axial images (data set A) followed by axial, coronal, and oblique MPR images. CT findings were compared to surgical findings in terms of diagnostic performance. McNemar’s test was used to detect any statistical difference in POT evaluation between datasets A and B. Kappa statistics were applied for measuring agreement between two readers. Results. There was a diagnostic improvement of 9.9% in the case of the less experienced radiologist in localizing POT by using oblique reformatted images. The more experienced radiologist showed diagnostic improvement by 12.9%.

1. Introduction

Small bowel obstruction (SBO) is a common clinical condition as a cause of abdominal pain, accounting for approximately 20% of all emergency admissions for acute abdomen. It is also amongst one of the commonest bowel pathologies leading to surgical consultation [1–3].

Early diagnosis of SBO obstruction is essential to prevent bowel ischemia. In the past, the paradigm was to “never let the sun set or rise on an obstructed bowel.” This probably was an evidence of limitations regarding availability of various imaging modalities for diagnosing the exact site of SBO [4].

Although plain X-ray abdominal evaluation still remains the investigation of first choice in cases of suspected SBO due to its low cost and wide availability, it cannot reliably diagnose the exact level of obstruction and thus can only serve as a basis for triage for further imaging workup [5, 6].

With the ongoing developments in imaging techniques overtime, computed tomography (CT) has emerged as an excellent modality in the diagnosis of SBO. CT scan not only reliably diagnoses SBO but can also be of great help in determining the cause, severity, and the precise point of obstruction [7–10]. Localizing the point of transition (POT) is empirical as it increases confidence in diagnosis, guides patient care, and thus helps in further management.

Ongoing dilation of the intestine increases luminal pressures. When luminal pressures exceed venous pressures, loss of venous drainage causes increasing edema and hyperemia of the bowel. This may eventually lead to compromised arterial flow to the bowel, causing ischemia, necrosis, and perforation. One such example is closed-loop obstruction, in which a section of bowel is obstructed proximally and distally. In such cases there may be few presenting symptoms with rapid progression to ischemia. Localizing the point of transition is imperative to pick the diagnosis in these patients.

Another important reason for finding the discrete transition point is that it helps guide operative planning. The number of points of transition being either one or more is also helpful in deciding between laparotomy and laparoscopic surgery.

In certain cases of extensively dilated bowel loops or lean and thin patients in whom interfaces between bowel loops are very thin due to paucity of intraperitoneal adipose tissue, it may be challenging to localize the POT reliably using axial slices alone [11, 12].

With the currently available multidetector CT (MDCT) scanners, we can get near-isotropic voxels, which can be less than a millimeter in dimension and thus can produce multiplanar reformations with spatial resolution similar to axial sections. This may potentially enhance the role of reformatted images in localizing POT.

Thus, the purpose of this study was to prospectively assess the additional value of oblique reformations from isotropic voxels obtained using a 64-slice MDCT to localize POT, having surgery as a reference standard [13–16].

2. Materials and Methods

This was a cross-sectional study carried out between January, 2008, and July, 2011, at Aga Khan University Hospital, Karachi. All patients who entered the emergency department with strong suspicion of SBO on plain radiographs and underwent a CT examination for evaluation of SBO were included in the study. A total of 187 consecutive patients were considered for inclusion in our work. Among these 187 patients, 102 underwent surgery. The remaining 85 patients were conservatively managed and were therefore excluded. CT examinations were performed using a 64-slice MDCT (Toshiba Aquilion 64) without oral contrast administration unless specifically requested by the surgical team. Images were acquired starting from the diaphragmatic dome extending to the pubic symphysis with section thickness of 5 mm at 5 mm interval with beam pitch of 1.5, rotation time of 5 seconds using 120 kV, and 350 mA and 175 mAs and WL = 340/40. All patients received 1.5–2 mL/kg body weight of nonionic contrast (name, brand) warmed to body temperature, injected at a rate of 3-4 mL/s using a mechanical power injector (name, brand) through a 20 G cannula inserted into an antecubital vein. Images were acquired in arterial and portovenous phases using 10-second delay for arterial and 65 seconds for delayed phases. After the raw data was acquired, reconstructions were performed.

The axial sections, that is, the raw data, were reconstructed in two steps: first with 5 mm-thick sections at 5 mm intervals in the transverse plane followed by 0.5 mm thick sections at 1.5 mm intervals.

This second dataset of reconstructed axial sections scans was then used to acquire coronal reformations in the coronal plane with a thickness of 3 mm at 1.5 mm intervals using soft tissue algorithm. The acquisition of reformations is part of the routine MDCT protocol in our department. All these reconstructions were performed by the technologist at the CT console with a commercially available console system devoted to rapid reconstruction and later sent to picture archive and communication system (PACS). Oblique reformations were performed by the readers at the Vitrea station from available data. We were able to get near-isotropic voxels, producing multiplanar reformations with spatial resolution similar to axial sections. This potentially enhanced the role of reformatted images in localizing POT. Two independent radiologists with 10 and 12 years of experience in abdominal imaging reviewed the images. Both observers were blinded to surgical findings and reviewed two different datasets (A, including axial and coronal images only; B, volume data for oblique reconstructions in addition to dataset A) with an 8-week interval to prevent recall bias. Reviewing radiologists were blinded to surgical findings of the level of obstruction. CT was evaluated to localize the exact location of the point of transition (POT) and etiology of obstruction. Also, readers were asked to rate the confidence of localizing and reporting the POT after use of data set B using a semiquantitative scale (increased, not changed, and decreased). CT findings were compared to surgical findings in terms of diagnostic performance. Interpretations were compared with surgical findings by primary researcher to evaluate for accuracy. McNemar’s test was used to detect any statistical difference in POT evaluation between datasets A and B. Kappa statistics were applied for measuring agreement between two readers for their findings of localizing the POT. SPSS version 11 was used for statistical analysis. A value <0.05 was considered significant.

3. Results

The commonest cause of SBO in the study group was adhesions found on laparotomy (), followed by hernias () and small bowel obstruction secondary to tuberculosis (). Other causes included postradiation stricture formation, gall stone ileus, tumor, volvulus, abscess formation, and foreign body/bezoars (Table 1).

Table 1: Cause of small bowel obstruction in the study group.

3.1. Cause of Small Bowel Obstruction in the Study Group

A total of 102 cases of surgically proven SBO were included in the study. Among these 102 patients, the less experienced radiologist correctly localized the transition zone in 85 cases (84.2%) using data set A and 95 cases when using data set B (94.15%), with a 9.9% diagnostic improvement (95% CI 2.7%−11.6%).

The more experienced radiologist correctly localized the transition zone in 83 cases (82.2%) using data set A, versus 96 cases (95.0%) using data set B, improving by 12.9% with 95% CI of 6.5% to 12.9% (Table 2).

Table 2: Improvement in accuracy of detecting point of transition after using data set B.

3.2. Improvement in and Accuracy of Detecting Point of Transition after Using Data Set B

When evaluating dataset B, the less experienced radiologist reported increased confidence in diagnosis of POT in 93 cases and similar confidence in 83 cases compared to the evaluation of dataset A. The more experienced radiologist reported increased confidence in diagnosis of POT in 95 cases and similar confidence in 82 cases compared to the evaluation of dataset A. The confidence scores for the presence of point of transition for both radiologists were higher after using data set B (axial, coronal, and oblique reformatted images) as compared to data set A (only axial and coronal images). Thus use of oblique reconstructions enhanced confidence in localizing POT () (Figures 1, 2, and 3).

Figure 1: Effect of oblique MPRs on confidence of radiologist in diagnosing point of transition.

Figure 2: CT scan of 48-year-old female with acute abdominal pain. (a) Axial and coronal CT images obtained with intravenous contrast agents show dilated small-bowel loops (red arrow heads and green circles). (b) Oblique reformation shows dilated small-bowel loops with a transition point (green arrow) in the midabdomen. Multiple small mesenteric lymph nodes also visualized (yellow circles).

Figure 3: CT scans in a 54-year-old woman with a two-month history of nausea and vomiting acutely presenting with worsening of symptoms. (a) Axial and coronal CT images obtained with intravenous contrast agent show herniated loops of small-bowel loops through a defect in anterior abdominal wall; however, the tract of bowel could not be completely elucidated on axial and coronal sections alone (yellow circles). (b) Oblique reformation exactly shows the outgoing and incoming loops of bowel with proximal dilated segments of bowel representing partial obstruction (blue circles and curved arrows).

Kappa statistics for the measurement of agreement were found between readers for both sets A and B. For the data set A, there was good agreement between both radiologists (value = 0.51). For the data set B, the value was even higher, 0.71, indicating a higher level of agreement after using MPRs (Figure 4).

Figure 4: Agreement between readers.

4. Discussion

Small bowel obstruction is one of the commonest reasons for presentation to the emergency department. Even today with the recent advances in imaging the diagnosis is made on the basis of clinical signs and symptoms, with plain radiography as the initial approach. However regarding the management of SBO, it is imperative not only to diagnose its severity but also to localize the exact site of obstruction which is an important determinant of the prognosis and help to the surgeon [17, 18].

With CT it has now become possible to not only reliably diagnose SBO but also acquire multiplanar reformations. Technological advances have crept up the ladder with time, making us available scanners like 64-slice MDCT and above. This paradigm shift has negotiated the problems like limited -axis resolution, longer acquisition times allowing submillimeter isotropic data which not only is of great diagnostic help in cardiac, pulmonary, and musculoskeletal pathologies by facilitating better evaluation of anatomy but also has applications with respect to the abdomen and pelvis like pancreatic and gastric cancers as described by Prokesch et al. and Kim et al. in their respective studies [19–22].

Coronal reformatted images have also proved to be helpful in increasing confidence level of readers for the diagnosis of acute appendicitis [23].

In the literature several studies have evaluated the role of multiplanar reconstructions in SBO. Lazarus et al., Caoili and Paulson, and Furukawa et al. in their studies suggested that multiplanar reformations are helpful in the localization of the transitional zone. Jaffe et al. proved isotropic coronal multiplanar reformatted images to have additional diagnostic value in cases of bowel obstruction showing better agreement among independent observers especially for the diagnosis of level and cause. A recent study by Hodel et al. specifically evaluated the role of CT reformatted images in small bowel obstruction using 16-slice MDCT and reported an increase in both accuracy and confidence in the localization of the transition zone in CT of mechanical SBO [11, 13, 24–26].

Compared to the study by Hodel et al., the cases included in this study were all surgically proven with perioperative findings taken as the gold standard for point of transition. Moreover the study was performed with a 64-slice MDCT as opposed to a 16-slice MDCT.

Although some radiologists and surgeons would consider coronal sections to be a better view regarding display of bowel since Coronal view is almost analogous to the frontal view of an abdominal radiograph and is synonymous to the plane learned in surgical training, Coronal view cannot be employed alone in cases of SBO since all the parts of bowel are not included on an isolated reformatted view. This could lead to confusion of narrowed part with an adjacent structure [27].

We thus included both the axial and coronal images in data set A, to make both data sets as similar as possible. This would also help to more correctly elucidate the additional value of oblique MPRs. Sagittal sections were however excluded from the data sets since they do not display more bowel loops in a single image and additionally it is difficult to localize the bowel loop anatomically as compared to axial and coronal images making it more time-consuming with little potential improvement in diagnosis.

Since the study went on for duration of more than 4 years, image grid overload with volume data was one of the prime challenges the researchers were confronted with. To deal with this all the available volume data was saved in compact discs while removing it from the image grid every 3 months and then reloading it onto the workstation for making oblique reconstructions at the time of recording the findings. To reduce the image load the axial data was presented on the PACS as 5 mm-thick sections. Reconstructions were performed from the available source images which were 1.5 mm thick.

We had in the study group myriad causes of SBO proven either by surgery or histopathology, amongst which adhesions were the commonest cause followed by abdominal hernias and tuberculosis. Although SBO was studied in different data sets, the radiologist was not asked to record the probable cause of obstruction on available images since it was not the prime objective for the study.

We were able to achieve significantly improved accuracy in localization of transitional zone following use of MPRs by both radiologists with good agreement between the two. Although there was a difference between the two radiologists regarding experience, in our study no significant difference was found with respect to the results for both data sets A and B. We were also able to find that MPR enhances radiologist’s confidence in calling a particular level point of obstruction which corroborates results given in studies by Jaffe et al., Paulson et al., and Hodel et al.

All the 102 patients included in our study underwent surgery, amongst which 19 patients had multiple levels of obstruction mostly due to adhesions. It was difficult to pick all the transition in cases where there was more than a single point of obstruction. This factor together with cases with extensive dilatation of bowel loops (high grade obstruction) could have been the reason for reduced CT accuracy in the first data set (84.1% and 82.1%) [3, 7, 14, 28].

One of the limitations of our study is that we did not classify the bowel obstruction into its grades of severity. By classifying the obstruction into various grades we could correlate the accuracy rates for various grades using MPRs as well.

5. Conclusion

In conclusion it was found that although the oblique MPR increases the image load and is time-consuming it surely is a development and a new perspective of utilizing the available information whereby it can significantly prove as a powerful adjunct in diagnosis and management of SBO.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

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