Uncertainty-weighted Multi-tasking for T1ρT_{1\rho} and T2_2 Mapping in the Liver with Self-supervised Learning

Multi-parametric mapping of MRI relaxations in liver has the potential of revealing pathological information of the liver. A self-supervised learning based multi-parametric mapping method is proposed to map T$T_{1\rho}$ and T$_2$ simultaneously, by utilising the relaxation constraint in the learning process. Data noise of different mapping tasks is utilised to make the model uncertainty-aware, which adaptively weight different mapping tasks during learning. The method was examined on a dataset of 51 patients with non-alcoholic fatter liver disease. Results showed that the proposed method can produce comparable parametric maps to the traditional multi-contrast pixel wise fitting method, with a reduced number of images and less computation time. The uncertainty weighting also improves the model performance. It has the potential of accelerating MRI quantitative imaging

Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions

Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis and intervention to improve the outcome of breast cancer patients. In the past decade, deep learning has shown remarkable progress in breast cancer imaging analysis, holding great promise in interpreting the rich information and complex context of breast imaging modalities. Considering the rapid improvement in the deep learning technology and the increasing severity of breast cancer, it is critical to summarize past progress and identify future challenges to be addressed. In this paper, we provide an extensive survey of deep learning-based breast cancer imaging research, covering studies on mammogram, ultrasound, magnetic resonance imaging, and digital pathology images over the past decade. The major deep learning methods, publicly available datasets, and applications on imaging-based screening, diagnosis, treatment response prediction, and prognosis are described in detail. Drawn from the findings of this survey, we present a comprehensive discussion of the challenges and potential avenues for future research in deep learning-based breast cancer imaging.Comment: Survey, 41 page

Hong Kong Chinese school children with elevated urine melamine levels: A prospective follow up study

<p>Abstract</p> <p>Background</p> <p>In 2008, the outbreak of kidney stones in children fed by melamine-tainted milk products in Mainland China has caused major public concern of food safety. We identified Hong Kong school children with elevated urine melamine level from a community-based school survey in 2007-08 and reviewed their clinical status in 2009.</p> <p>Methods</p> <p>In 2007-08, 2119 school children participated in a primary and secondary school survey in Hong Kong using a cluster sampling method. Urine aliquots from 502 subjects were assayed for melamine level. High urine melamine level was defined as urine melamine/creatinine ratio >7.1 μg/mmol. Subjects with high urine melamine level were invited for clinical evaluation in 2009 including urinalysis and ultrasound imaging of the urinary system.</p> <p>Results</p> <p>The age range of this subcohort was 6 - 20 years with 67% girls (335 female and 167 male subjects). The spot urine melamine/creatinine ratio of the 502 urine aliquots ranged from undetectable to 1467 μg/mmol (median 0.8 μg/mmol). Of these, 213 subjects had undetectable level (42%). We invited 47 (9%) subjects with high urine melamine level for re-evaluation and one subject declined. The median duration of follow-up was 23.5 months (interquartile range: 19.8 - 30.6 months). None of the 46 subjects (28% boys, mean age 13.9 ± 2.9 years) had any abnormality detected on ultrasound study of the urinary system. All subjects had stable renal function with a median urine albumin-creatinine ratio of 0.70 mg/mmol (interquartile range: 0.00 - 2.55 mg/mmol).</p> <p>Conclusions</p> <p>Hong Kong Chinese school children with high urine melamine levels appeared to have benign clinical course in the short term although a long term follow-up study is advisable in those with persistently high urine melamine level.</p

Biomechanical analysis and modeling of different vertebral growth patterns in adolescent idiopathic scoliosis and healthy subjects

<p>Abstract</p> <p>Background</p> <p>The etiology of AIS remains unclear, thus various hypotheses concerning its pathomechanism have been proposed. To date, biomechanical modeling has not been used to thoroughly study the influence of the abnormal growth profile (i.e., the growth rate of the vertebral body during the growth period) on the pathomechanism of curve progression in AIS. This study investigated the hypothesis that AIS progression is associated with the abnormal growth profiles of the anterior column of the spine.</p> <p>Methods</p> <p>A finite element model of the spinal column including growth dynamics was utilized. The initial geometric models were constructed from the bi-planar radiographs of a normal subject. Based on this model, five other geometric models were generated to emulate different coronal and sagittal curves. The detailed modeling integrated vertebral body growth plates and growth modulation spinal biomechanics. Ten years of spinal growth was simulated using AIS and normal growth profiles. Sequential measures of spinal alignments were compared.</p> <p>Results</p> <p>(1) Given the initial lateral deformity, the AIS growth profile induced a significant Cobb angle increase, which was roughly between three to five times larger compared to measures utilizing a normal growth profile. (2) Lateral deformities were absent in the models containing no initial coronal curvature. (3) The presence of a smaller kyphosis did not produce an increase lateral deformity on its own. (4) Significant reduction of the kyphosis was found in simulation results of AIS but not when using the growth profile of normal subjects.</p> <p>Conclusion</p> <p>Results from this analysis suggest that accelerated growth profiles may encourage supplementary scoliotic progression and, thus, may pose as a progressive risk factor.</p