AIMC Topic: Magnetic Resonance Imaging

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A novel deep learning system for automated diagnosis and grading of lumbar spinal stenosis based on spine MRI: model development and validation.

Neurosurgical focus
OBJECTIVE: The study aimed to develop a single-stage deep learning (DL) screening system for automated binary and multiclass grading of lumbar central stenosis (LCS), lateral recess stenosis (LRS), and lumbar foraminal stenosis (LFS).

A multiregional multimodal machine learning model for predicting outcome of surgery for symptomatic hemorrhagic brainstem cavernous malformations.

Neurosurgical focus
OBJECTIVE: Given that resection of brainstem cavernous malformations (BSCMs) ends hemorrhaging but carries a high risk of neurological deficits, it is necessary to develop and validate a model predicting surgical outcomes. This study aimed to constru...

Fully automatic anatomical landmark localization and trajectory planning for navigated external ventricular drain placement.

Neurosurgical focus
OBJECTIVE: The aim of this study was to develop and validate a fully automatic anatomical landmark localization and trajectory planning method for external ventricular drain (EVD) placement using CT or MRI.

How I Do It: Three-Dimensional MR Neurography and Zero Echo Time MRI for Rendering of Peripheral Nerve and Bone.

Radiology
MR neurography sequences provide excellent nerve-to-background soft tissue contrast, whereas a zero echo time (ZTE) MRI sequence provides cortical bone contrast. By demonstrating the spatial relationship between nerves and bones, a combination of ren...

[A deep learning method for differentiating nasopharyngeal carcinoma and lymphoma based on MRI].

Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery
To development a deep learning(DL) model based on conventional MRI for automatic segmentation and differential diagnosis of nasopharyngeal carcinoma(NPC) and nasopharyngeal lymphoma(NPL). The retrospective study included 142 patients with NPL and 292...

Physiological Confounds in BOLD-fMRI and Their Correction.

NMR in biomedicine
Functional magnetic resonance imaging (fMRI) has opened new frontiers in neuroscience by instrumentally driving our understanding of brain function and development. Despite its substantial successes, fMRI studies persistently encounter obstacles stem...

Deep Learning Reveals Liver MRI Features Associated With PNPLA3 I148M in Steatotic Liver Disease.

Liver international : official journal of the International Association for the Study of the Liver
BACKGROUND: Steatotic liver disease (SLD) is the most common liver disease worldwide, affecting 30% of the global population. It is strongly associated with the interplay of genetic and lifestyle-related risk factors. The genetic variant accounting f...

Multiclass ensemble framework for enhanced prostate gland Segmentation: Integrating Self-ONN decoders with EfficientNet.

Computers in biology and medicine
Digital pathology relies on the morphological architecture of prostate glands to recognize cancerous tissue. Prostate cancer (PCa) originates in walnut shaped prostate gland in the male reproductive system. Deep learning (DL) pipelines can assist in ...

A combined attention mechanism for brain tumor segmentation of lower-grade glioma in magnetic resonance images.

Computers in biology and medicine
Low-grade gliomas (LGGs) are among the most problematic brain tumors to reliably segment in FLAIR MRI, and effective delineation of these lesions is critical for clinical diagnosis, treatment planning, and patient monitoring. Nevertheless, convention...

Estimation of time-to-total knee replacement surgery with multimodal modeling and artificial intelligence.

Computers in biology and medicine
BACKGROUND: The methods for predicting time-to-total knee replacement (TKR) do not provide enough information to make robust and accurate predictions.