Objective: We aimed to investigate if the use of read-out segmented echoplanar imaging with additional two-dimensional navigator correction (Readout Segmentation of Long Variable Echo, RESOLVE) for acquiring prostate diffusion-weighted imaging (DWI) improves image quality, compared to single-shot echoplanar imaging (ss-EPI). Methods: This single-center prospective study cohort included 162 males with suspected prostate cancer, who underwent 3 Tesla multiparametric MRI (3T-mpMRI). Two abdominal radiologists, blinded to the clinical information, separately reviewed each 3T-mpMRI study to rank geometrical distortion, degree of rectal distention, lesion conspicuity, and anatomic details delineation first on ss-EPI-DWI and later on RESOLVE-DWI using 5-point scales (1 = excellent, 5 = poor). The average of the ranking scores given by two readers was generated and used as the final score. Results: There was good-to-excellent interreader agreement for scoring image quality parameters on both ss-EPI and RESOLVE. Geometrical distortion scores > 3 was seen in 12.3% (20/162) of ss-EPI images, with all having geometrical distortion score <3 on RESOLVE (p < .001). The mean image distortion score was significantly less on RESOLVE than ss-EPI (1.16 vs 1.61, p < .01 regardless of rectal gas, p< .05 when stratified by the degree of rectal distention ). RESOLVE was superior to ss-EPI for lesion conspicuity (mean 1.35 vs 1.53, p< .002) and anatomic delineation (2.60 vs 2.68, p< .001) of prostate on DWI. Conclusion: Compared to conventional ss-EPI, the use of RESOLVE for acquisition of prostate DWI resulted in significantly enhanced image quality and reduced geometrical distortion. Advances in knowledge: RESOLVE could be an alternative or replacement of ss-EPI for acquiring prostate DWI with significantly less geometrical distortion and significantly improved lesion conspicuity and anatomic delineation.

Background: Around 1 in 59 individuals is diagnosed with Autism Spectrum Disorder (ASD), according to CDS statistics. Conventionally, ASD has been diagnosed using functional brain regions, regions of interest, or multi-tissue-based training in artificial intelligence models. The objective of the exhibit study is to develop an efficient deep learning network for identifying ASD using structural magnetic resonance imaging (MRI)-based brain scans. Methods: In this work, we developed a VGG-based deep learning network capable of diagnosing autism using whole brain gray matter (GM) tissues. We trained our deep network with 132 MRI T1 images from normal controls and 140 MRI T1 images from ASD patients sourced from the Autism Brain Imaging Data Exchange (ABIDE) dataset. Results: The number of participants in both ASD and normal control (CN) subject groups was not statistically different (p = 0.23). The mean age of the CN subject group was 14.62 years (standard deviation: 4.34), and the ASD group had mean age of 14.89 years (standard deviation: 4.29). Our deep learning model accomplished a training accuracy of 97% and a validation accuracy of 96% over 50 epochs without overfitting. Conclusions: To the best of our knowledge, this is the first study to use GM tissue alone for diagnosing ASD using VGG-Net.

A Chance fracture, also known as a “seatbelt fracture,” is “an unstable spinal injury that usually occurs at the thoracolumbar junction. Complications of a Chance fracture include spinal cord injury, neurological deficits, and long-term functional impairment if not promptly identified and treated. In this case report, we present the case of a 13-year-old patient who sustained a Chance fracture following a 30-foot vertical fall, an atypical mechanism of injury. Imaging revealed a vertebral body fracture with a horizontal break through the posterior elements and accompanying ligamentous tears. Management included surgical stabilization via pedicle screw fixation and laminectomy. This case highlights the importance of considering axial loading mechanisms in pediatric trauma patients and the role of timely surgical intervention in preventing long-term complications.

Background: Around 1 in 59 individuals is diagnosed with Autism Spectrum Disorder (ASD), according to CDS statistics. Conventionally, ASD has been diagnosed using functional brain regions, regions of interest, or multi-tissue-based training in artificial intelligence models. The objective of the exhibit study is to develop an efficient deep learning network for identifying ASD using structural magnetic resonance imaging (MRI) based brain scans. Methods: In this work, we developed a VGG-based deep learning network capable of diagnosing autism using whole brain gray matter (GM) tissues. We trained our deep network with 132 MRI T1 images from normal controls and 140 MRI T1 images from ASD patients sourced from the Autism Brain Imaging Data Exchange (ABIDE) dataset. Results: The number of participants in both ASD and normal control (CN) subject groups was not statistically different (p=0.23). The mean age of CN subject was 14.62 years( standard deviation : 4.34 ), and ASD group had mean age of 14.89 years (standard deviation : 4.29). Our deep learning model accomplished a training accuracy of 97% and a validation accuracy of 96% over 50 epochs without overfitting. Conclusions: To the best of our knowledge, this is the first study to use GM tissue alone for diagnosing ASD.

Thoracic aortic pseudoaneurysms are a rare but serious complication of infectious processes, often resulting from mycotic (infectious) aneurysms, occurring when the vessel wall is compromised by an infection, leading to the formation of a pseudoaneurysm [1]. Mycotic aneurysms typically result from bacteremia or fungemia, with common sources being infective endocarditis or other systemic infections. Tuberculosis, though a common infectious disease worldwide, is an unusual cause of aortic pseudoaneurysm formation. We present the case of a 26-year-old Asian male with a history of positive Quantiferon test, who presented with two weeks of back pain, hemoptysis, cough, night sweats, fever, and chills. CT imaging revealed a pseudoaneurysm at the T11-T12 level, associated with an epidural abscess, prevertebral rim-enhancing collection, and signs of osteomyelitis. The patient underwent endovascular repair of the aortic aneurysm, followed by drainage and biopsy of the epidural abscess and antibiotic therapy for the underlying infection. This case demonstrates the rare occurrence of a mycotic aneurysm caused by tuberculous infection, and the importance of early diagnosis and prompt surgical intervention to prevent catastrophic complications such as rupture, sepsis, or neurological impairment.

Ischemic strokes occur due to a reduction in blood flow to the brain, typically from an occluded artery. Embolic strokes are a subset where the blockage is caused by dislodged thrombus that travels from distal areas of the body, such as the heart or large arteries, and lodges in a cerebral vessel, leading to localized ischemia. While emboli are common causes of ischemic strokes, calcific emboli causing an ischemic stroke are much less reported and are frequently underdiagnosed. We report here a case of a patient with stroke symptoms who was found to have a carotid plaque on imaging, who then acutely worsened after admission, and subsequently found to have numerous new calcified emboli on a follow-up head CT.