Unusual case of a gigantic bladder mimicking a large pelvic cystic mass.

Pelvic masses are more common in females compared to the males. Bladder distension secondary to urinary retention can also mimic as a pelvic mass. However, it is rare to see chronic urinary retention with no clinical urinary symptoms. We present a case report of an elderly male who presented with abdominal pain and progressive worsening of breathing, along with abdominal distension. Initially, patient was thought to have a large cystic pelvic mass, causing bilateral renal hydronephrosis due to the ureteric compression. However, urinary cauterization drained 19,000 ml of the urine leading to not only resolution of the symptoms but also clinical improvement of the patient.

Evaluating Critical Appraisal Skills by Introducing Journal Clubs to Preclinical Dental Students Using the Assessing Competency in Evidence-Based Medicine (ACE) Tool Through Pre and Post-testing.

Background The ineffective utilization of journal clubs (JCs) for pre-clinical dental students has led to a lack of research into their effectiveness in developing skills such as critical reasoning and evidence-based medicine (EBM) practice. Therefore, we have implemented JCs in first-year undergraduate dental students and measured their effectiveness using the integrated Assessing Competency in Evidence-Based Medicine (ACE) tool. Methodology We conducted a quasi-experimental study where EBM was included in the curriculum for pre-clinical students as a hybrid model with a year-long blended learning approach. The 50-student class was divided into five groups of 10 students, with each group participating in seven JCs related to the physiology curriculum. After conducting critical analysis in self-directed learning sessions, students created interactive PowerPoint presentations followed by discussion. Instructors offered feedback after each session based on 1-2 levels in Kirkpatrick's training evaluation model. Inferential statistics were used for comparative analysis of the ACE tool pre- and post-test using SPSS version 26 (IBM Corp., Armonk, NY, USA). Results A linear trend in median score from 6 in the pre-test to 9 in the post-test was detected using the box and whisker plot. Using paired sample t-test, the mean difference (95% confidence interval) between the pre-test and post-test responses was -3.14 (-2.32 to -3.96) (p < 0.001). In terms of the post-test responses, each item's difficulty index ranged from 0.3 to 0.9. Internal reliability was in the acceptable range of >0.15 (range = 0.5-0.18). The item discriminatory index was in the range of 0.8 to >0.2. Cronbach's alpha was 0.64, which was deemed acceptable. Conclusions Our results show that pre-clinical dentistry students appreciated the use of JCs to improve active learning, critical appraisal, analytical, and decision-making skills. The 15-item ACE measure is a useful and reliable tool for assessing dentistry students' EBM proficiency in Pakistan.

Data-specific mask-guided image reconstruction for diffuse optical tomography.

Conventional approaches in diffuse optical tomography (DOT) image reconstruction often address the ill-posed inverse problem via regularization with a constant penalty parameter, which uniformly smooths out the solution. In this study, we present a data-specific mask-guided scheme that incorporates a prior mask constraint into the image reconstruction framework. The prior mask was created from the DOT data itself by exploiting the multi-measurement vector formulation. We accordingly propose two methods to integrate the prior mask into the reconstruction process. First, as a soft prior by exploiting a spatially varying regularization. Second, as a hard prior by imposing a region-of-interest-limited reconstruction. Furthermore, the latter method iterates between discrete and continuous steps to update the mask and optical parameters, respectively. The proposed methods showed enhanced optical contrast accuracy, improved spatial resolution, and reduced noise level in DOT reconstructed images compared with the conventional approaches such as the modified Levenberg-Marquardt approach and the l1-regularization based sparse recovery approach.

Development of digital breast tomosynthesis and diffuse optical tomography fusion imaging for breast cancer detection.

Diffuse optical tomography (DOT) non-invasively measures the functional characteristics of breast lesions using near infrared light to probe tissue optical properties. This study aimed to evaluate a new digital breast tomosynthesis (DBT)/DOT fusion imaging technique and obtain preliminary data for breast cancer detection. Twenty-eight women were prospectively enrolled and underwent both DBT and DOT examinations. DBT/DOT fusion imaging was created after acquisition of both examinations. Two breast radiologists analyzed DBT and DOT images independently, and then finally evaluated the fusion images. The diagnostic performance of each reading session was compared and interobserver agreement was assessed. The technical success rate was 96.4%, with one failure due to an error during DOT data storage. Among the 27 women finally included in the analysis, 13 had breast cancer. The areas under the receiver operating characteristic curve (AUCs) for DBT were 0.783 and 0.854 for readers 1 and 2, respectively. DOT showed comparable diagnostic performance to DBT for both readers. The AUCs were significantly improved (P = 0.004) when the DBT/DOT fusion images were used. Interobserver agreements were highest for the DBT/DOT fusion images. In conclusion, this study suggests that DBT/DOT fusion imaging technique appears to be a promising tool for breast cancer diagnosis.

Convolutional neural network-based approach to estimate bulk optical properties in diffuse optical tomography.

Deep learning has been actively investigated for various applications such as image classification, computer vision, and regression tasks, and it has shown state-of-the-art performance. In diffuse optical tomography (DOT), the accurate estimation of the bulk optical properties of a medium is paramount because it directly affects the overall image quality. In this work, we exploit deep learning to propose a novel, to the best of our knowledge, convolutional neural network (CNN)-based approach to estimate the bulk optical properties of a highly scattering medium such as biological tissue in DOT. We validated the proposed method by using experimental, as well as, simulated data. For performance assessment, we compared the results of the proposed method with those of existing approaches. The results demonstrate that the proposed CNN-based approach for bulk optical property estimation outperforms existing methods in terms of estimation accuracy, with lower computation time.

Deep Learning Diffuse Optical Tomography.

Diffuse optical tomography (DOT) has been investigated as an alternative imaging modality for breast cancer detection thanks to its excellent contrast to hemoglobin oxidization level. However, due to the complicated non-linear photon scattering physics and ill-posedness, the conventional reconstruction algorithms are sensitive to imaging parameters such as boundary conditions. To address this, here we propose a novel deep learning approach that learns non-linear photon scattering physics and obtains an accurate three dimensional (3D) distribution of optical anomalies. In contrast to the traditional black-box deep learning approaches, our deep network is designed to invert the Lippman-Schwinger integral equation using the recent mathematical theory of deep convolutional framelets. As an example of clinical relevance, we applied the method to our prototype DOT system. We show that our deep neural network, trained with only simulation data, can accurately recover the location of anomalies within biomimetic phantoms and live animals without the use of an exogenous contrast agent.

HCV-RNA Pcr Positivity In Hcv Antibody Negative Patients Undergoing Haemodialysis.

BACKGROUND: It's estimated that almost 2.2% of the world's inhabitants suffer from hepatitis C virus (HCV). The most common cause of chronic liver disease in haemodialysis centres is due to HCV. In 1993, it was first described by Bukh and colleagues that HCV viremia can occur without any detectable antibodies to the HCV. Keeping this in mind the purpose of this cross-sectional study was to assess the frequency of HCV in haemodialysis patients by PCR who are serologically negative for HCV. METHODS: This cross-sectional study was conducted from 1st June to 31st December 2016 on all haemodialysis patients at MH Rawalpindi. Epidemiological data for gender, age, duration on haemodialysis, cause of chronic renal failure and any associated risk factor for acquiring hepatitis C infection was asked. Patients undergoing haemodialysis were investigated by fourth generation ELISA for Anti HCV antibodies, HCV DNA polymerase chain reaction, HCV genotype (where required) and liver function test were also done. RESULTS: A total of 201 patients were undergoing haemodialysis. Among these patients 73 were hepatitis "C" negative and 128 were hepatitis "C" positive. Among the 73 patients who were hepatitis C negative by ELISA method 17 (23%) were PCR positive. Of the 17 patients 13 (76.5%) were men and 4 (23.5%) were women. The mean age of the patients was 49.7±18.0 years and mean duration of haemodialysis was 4.4±4.1 months. The most common cause of CKD requiring haemodialysis was hypertension (64.7%). The most common genotype was type 1 (58.8%) followed by genotype 3 (41.2%). The mean viral load was 23583615.70 IU. CONCLUSIONS: HCV-RNA detection by PCR should be used as standard of care to detect HCV infection in patients undergoing haemodialysis.

A novel pre-processing technique for improving image quality in digital breast tomosynthesis.

PURPOSE: Nonlinear pre-reconstruction processing of the projection data in computed tomography (CT) where accurate recovery of the CT numbers is important for diagnosis is usually discouraged, for such a processing would violate the physics of image formation in CT. However, one can devise a pre-processing step to enhance detectability of lesions in digital breast tomosynthesis (DBT) where accurate recovery of the CT numbers is fundamentally impossible due to the incompleteness of the scanned data. Since the detection of lesions such as micro-calcifications and mass in breasts is the purpose of using DBT, it is justified that a technique producing higher detectability of lesions is a virtue. METHODS: A histogram modification technique was developed in the projection data domain. Histogram of raw projection data was first divided into two parts: One for the breast projection data and the other for background. Background pixel values were set to a single value that represents the boundary between breast and background. After that, both histogram parts were shifted by an appropriate amount of offset and the histogram-modified projection data were log-transformed. Filtered-backprojection (FBP) algorithm was used for image reconstruction of DBT. To evaluate performance of the proposed method, we computed the detectability index for the reconstructed images from clinically acquired data. RESULTS: Typical breast border enhancement artifacts were greatly suppressed and the detectability of calcifications and masses was increased by use of the proposed method. Compared to a global threshold-based post-reconstruction processing technique, the proposed method produced images of higher contrast without invoking additional image artifacts. CONCLUSIONS: In this work, we report a novel pre-processing technique that improves detectability of lesions in DBT and has potential advantages over the global threshold-based post-reconstruction processing technique. The proposed method not only increased the lesion detectability but also reduced typical image artifacts pronounced in conventional FBP-based DBT.

Sampling scheme optimization for diffuse optical tomography based on data and image space rankings.

We present a methodology for the optimization of sampling schemes in diffuse optical tomography (DOT). The proposed method exploits singular value decomposition (SVD) of the sensitivity matrix, or weight matrix, in DOT. Two mathematical metrics are introduced to assess and determine the optimum source­detector measurement configuration in terms of data correlation and image space resolution. The key idea of the work is to weight each data measurement, or rows in the sensitivity matrix, and similarly to weight each unknown image basis, or columns in the sensitivity matrix, according to their contribution to the rank of the sensitivity matrix, respectively. The proposed metrics offer a perspective on the data sampling and provide an efficient way of optimizing the sampling schemes in DOT. We evaluated various acquisition geometries often used in DOT by use of the proposed metrics. By iteratively selecting an optimal sparse set of data measurements, we showed that one can design a DOT scanning protocol that provides essentially the same image quality at a much reduced sampling.

Pattern of biopsy proven renal diseases at PNS SHIFA, Karachi: A cross-sectional survey.

INTRODUCTION: Percutaneous renal biopsy (RB) is an invaluable diagnostic procedure in patients with medical renal diseases. OBJECTIVES: To determine the pattern of biopsy proven renal disease (BPRD) from a tertiary care naval hospital in Karachi, Pakistan. METHODS AND MATERIALS: All the renal biopsies in adult patients (≥18 years) performed at our hospital from 2008 to 2012 were retrospectively reviewed. The biopsies were evaluated by light microscopy and immunofluorescence. RESULTS: A total 60 cases were analyzed. The mean age was 33.3±12.9 years (range: 18 to 72 years).The male to female ratio was 3:1. The most common indication of renal biopsy was nephrotic syndrome (43.3%), followed by renal failure (26.6%) and non-nephrotic proteinuria (23.3%). Primary glomerulonephritides (PGN) were predominant overall lesions, found in 46 (76.6%) of the total biopsies. Among PGN, the most common lesion was focal segmental glomerulosclerosis (FSGS), followed by membranous glomerulonephritis (MGN), IgA nephropathy (IgAN) and chronic sclerosing glomerulonephritis (CSGN) and a variety of rare lesions. Secondary glomerulonephritides (SGN) were found in only three (5%) cases. There were two cases of amyloidosis and one of lupus nephritis (LN). Tubulointerstitial disease (TID) and vascular disease were rare. CONCLUSION: This study provides information about the epidemiology of BPRD in a large tertiary care naval center in Southern Pakistan.

Twenty-seven year old man presenting with a strangulated diaphragmatic hernia eight years after the initial injury.

A 27-year-old man presented with a 5 day history of abdominal pain and distension, with associated constipation and vomiting. He had presented 8 years earlier following a traumatic injury to the left side of the chest, and no diaphragmatic injury was reported at that time. On this admission, a computed tomography scan showed herniation of the splenic flexure of the colon into the left hemithorax. Subsequently, he had an emergency laparotomy for resection, with formation of a loop ileostomy. The various imaging techniques all have advantages and disadvantages when diagnosing a traumatic diaphragmatic hernia. It is the clinician's role to maintain a high index of suspicion when a patient initially presents with trauma where a traumatic diaphragmatic hernia may be a possibility.

Brain stem and cerebellar findings in Joubert syndrome.

Joubert syndrome is often missed clinically and radiologically if not enough attention is paid to its subtle and variable clinical presentation and the imaging findings in the posterior fossa. The purpose of this paper is to illustrate the brain stem and cerebellar imaging findings in Joubert syndrome. Awareness of the clinical and neuroimaging findings in Joubert syndrome and maintenance of a high index of suspicion are essential to correctly diagnose this rare congenital malformation.