13 research outputs found
Holistic image reconstruction for diffusion MRI
\u3cp\u3eDiffusion MRI provides unique information on the microarchitecture of biological tissues. One of the major challenges is finding a balance between image resolution, acquisition duration, noise level and image artifacts. Recent methods tackle this challenge by performing super-resolution reconstruction in image space or in diffusion space, regularization of the image data or of postprocessed data (such as the orientation distribution function, ODF) along different dimensions, and/or impose data-consistency in the original acquisition space. Each of these techniques has its own advantages; however, it is rare that even a few of them are combined. Here we present a holistic framework for diffusion MRI reconstruction that allows combining the advantages of all these techniques in a single reconstruction step. In proof-of-concept experiments, we demonstrate super-resolution on HARDI shells and in image space, regularization of the ODF and of the images in spatial and angular dimensions, and data consistency in the original acquisition space. Reconstruction quality is superior to standard reconstruction, demonstrating the feasibility of combining advanced techniques into one step.\u3c/p\u3
Holistic image reconstruction for diffusion MRI
Diffusion MRI provides unique information on the microarchitecture of biological tissues. One of the major challenges is finding a balance between image resolution, acquisition duration, noise level and image artifacts. Recent methods tackle this challenge by performing super-resolution reconstruction in image space or in diffusion space, regularization of the image data or of postprocessed data (such as the orientation distribution function, ODF) along different dimensions, and/or impose data-consistency in the original acquisition space. Each of these techniques has its own advantages; however, it is rare that even a few of them are combined. Here we present a holistic framework for diffusion MRI reconstruction that allows combining the advantages of all these techniques in a single reconstruction step. In proof-of-concept experiments, we demonstrate super-resolution on HARDI shells and in image space, regularization of the ODF and of the images in spatial and angular dimensions, and data consistency in the original acquisition space. Reconstruction quality is superior to standard reconstruction, demonstrating the feasibility of combining advanced techniques into one step
q-Space Deep Learning: Twelve-Fold Shorter and Model-Free Diffusion MRI Scans
Numerous scientific fields rely on elaborate but partly suboptimal data processing pipelines. An example is diffusion magnetic resonance imaging (diffusion MRI), a non-invasive microstructure assessment method with a prominent application in neuroimaging. Advanced diffusion models providing accurate microstructural characterization so far have required long acquisition times and thus have been inapplicable for children and adults who are uncooperative, uncomfortable, or unwell. We show that the long scan time requirements are mainly due to disadvantages of classical data processing. We demonstrate how deep learning, a group of algorithms based on recent advances in the field of artificial neural networks, can be applied to reduce diffusion MRI data processing to a single optimized step. This modification allows obtaining scalar measures from advanced models at twelve-fold reduced scan time and detecting abnormalities without using diffusion models. We set a new state of the art by estimating diffusion kurtosis measures from only 12 data points and neurite orientation dispersion and density measures from only 8 data points. This allows unprecedentedly fast and robust protocols facilitating clinical routine and demonstrates how classical data processing can be streamlined by means of deep learning
Surgical Treatment of Chronic Osteomyelitis
Introduction. In this study, we analysed the results of applying various surgical methods in the combined treatment of inflammatory diseases of bones and joints.Materials and methods. The work was based on data from a multi-dimensional cohort study using non-concurrent (historical) control. A retrospective study included the analysis of medical records covering the period of 2009β2016 (1059 patients). A prospective study consisted in analysing the effectiveness of modern surgical methods in the combined treatment of inflammatory diseases of bones and joints in patients hospitalised to the Septic Surgery Department of the G.G. Kuvatov Republican Clinical Hospital (Ufa, Russia) in 2017β2018 (285 patients).Results and discussion. An analysis of the authorsβ own data revealed that injuries (73.21%) and infectious complications after receiving surgery on bones and joints (15.03%) are the most common causes of osteomyelitis. In most cases, the following list of measures is optimal for diagnosing suspected osteomyelitis of various etiologies: X-ray, general clinical tests supplemented by the fistulography or CT of the affected area prior to surgery, as well as the examination of surgical material after surgery. The use of modern methods for surgical debridement and surgical repair of bone defects in the combined treatment of patients with chronic osteomyelitis can significantly reduce the relapse rate. It is recommended that patients with osteomyelitis be treated at large in-patient surgical facilities, which include a specialised department for the treatment of surgical infections and corresponding support services.Conclusion. Apparently, there is no one most optimal method for treating osteomyelitis. The optimal effect in the treatment of osteomyelitis is achieved through a personalised set of therapeutic measures using the following methods: laser vaporisation, negative-pressure wound therapy, ultrasonic cavitation in the focus of inflammation, as well as surgical repair of the post-trepanation bone defect or wound
q-Space Deep Learning for Twelve-Fold Shorter and Model-Free Diffusion MRI Scans
Diffusion MRI uses a multi-step data processing pipeline. With certain steps being prone to instabilities, the pipeline relies on considerable amounts of partly redundant input data, which requires long acquisition time. This leads to high scan costs and makes advanced diffusion models such as diffusion kurtosis imaging (DKI) and neurite orientation dispersion and density imaging (NODDI) inapplicable for children and adults who are uncooperative, uncomfortable or unwell. We demonstrate how deep learning, a group of algorithms in the field of artificial neural networks, can be applied to reduce diffusion MRI data processing to a single optimized step. This method allows obtaining scalar measures from advanced models at twelve-fold reduced scan time and detecting abnormalities without using diffusion models
Bias and Precision Analysis of Diffusional Kurtosis Imaging for Different Acquisition Schemes
Π₯ΠΈΡΡΡΠ³ΠΈΡΠ΅ΡΠΊΠΎΠ΅ Π»Π΅ΡΠ΅Π½ΠΈΠ΅ Ρ ΡΠΎΠ½ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΎΡΡΠ΅ΠΎΠΌΠΈΠ΅Π»ΠΈΡΠ°
Introduction. In this study, we analysed the results of applying various surgical methods in the combined treatment of inflammatory diseases of bones and joints.Materials and methods. The work was based on data from a multi-dimensional cohort study using non-concurrent (historical) control. A retrospective study included the analysis of medical records covering the period of 2009β2016 (1059 patients). A prospective study consisted in analysing the effectiveness of modern surgical methods in the combined treatment of inflammatory diseases of bones and joints in patients hospitalised to the Septic Surgery Department of the G.G. Kuvatov Republican Clinical Hospital (Ufa, Russia) in 2017β2018 (285 patients).Results and discussion. An analysis of the authorsβ own data revealed that injuries (73.21%) and infectious complications after receiving surgery on bones and joints (15.03%) are the most common causes of osteomyelitis. In most cases, the following list of measures is optimal for diagnosing suspected osteomyelitis of various etiologies: X-ray, general clinical tests supplemented by the fistulography or CT of the affected area prior to surgery, as well as the examination of surgical material after surgery. The use of modern methods for surgical debridement and surgical repair of bone defects in the combined treatment of patients with chronic osteomyelitis can significantly reduce the relapse rate. It is recommended that patients with osteomyelitis be treated at large in-patient surgical facilities, which include a specialised department for the treatment of surgical infections and corresponding support services.Conclusion. Apparently, there is no one most optimal method for treating osteomyelitis. The optimal effect in the treatment of osteomyelitis is achieved through a personalised set of therapeutic measures using the following methods: laser vaporisation, negative-pressure wound therapy, ultrasonic cavitation in the focus of inflammation, as well as surgical repair of the post-trepanation bone defect or wound.ΠΠ²Π΅Π΄Π΅Π½ΠΈΠ΅. ΠΡΠ½ΠΎΠ²Π½ΠΎΠΉ ΡΠ΅Π»ΡΡ Π΄Π°Π½Π½ΠΎΠ³ΠΎ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΠΏΠΎΡΠ»ΡΠΆΠΈΠ»Π° ΠΎΡΠ΅Π½ΠΊΠ° ΡΠΎΠ±ΡΡΠ²Π΅Π½Π½ΡΡ
ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠ² ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² Ρ
ΠΈΡΡΡΠ³ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π»Π΅ΡΠ΅Π½ΠΈΡ Π² ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ½ΠΎΠΌ Π»Π΅ΡΠ΅Π½ΠΈΠΈ Π²ΠΎΡΠΏΠ°Π»ΠΈΡΠ΅Π»ΡΠ½ΡΡ
Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠΉ ΠΊΠΎΡΡΠ΅ΠΉ ΠΈ ΡΡΡΡΠ°Π²ΠΎΠ².ΠΠ°ΡΠ΅ΡΠΈΠ°Π»Ρ ΠΈ ΠΌΠ΅ΡΠΎΠ΄Ρ. Π ΡΠ°Π±ΠΎΡΡ Π²ΠΎΡΠ»ΠΈ Π΄Π°Π½Π½ΡΠ΅ ΡΠ°Π·Π½ΠΎΠ½Π°ΠΏΡΠ°Π²Π»Π΅Π½Π½ΠΎΠ³ΠΎ ΠΊΠΎΠ³ΠΎΡΡΠ½ΠΎΠ³ΠΎ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Ρ Π½Π΅ΠΏΠ°ΡΠ°Π»Π»Π΅Π»ΡΠ½ΡΠΌ (ΠΈΡΡΠΎΡΠΈΡΠ΅ΡΠΊΠΈΠΌ) ΠΊΠΎΠ½ΡΡΠΎΠ»Π΅ΠΌ. Π Π΅ΡΡΠΎΡΠΏΠ΅ΠΊΡΠΈΠ²Π½ΠΎΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ Π²ΠΊΠ»ΡΡΠ°Π»ΠΎ Π°Π½Π°Π»ΠΈΠ· ΠΈΡΡΠΎΡΠΈΠΉ Π±ΠΎΠ»Π΅Π·Π½Π΅ΠΉ Π² ΠΏΠ΅ΡΠΈΠΎΠ΄ Ρ 2009 ΠΏΠΎ 2016 Π³. (1059 ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ²), ΠΏΡΠΎΡΠΏΠ΅ΠΊΡΠΈΠ²Π½ΠΎΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ Π·Π°ΠΊΠ»ΡΡΠ°Π»ΠΎΡΡ Π² Π°Π½Π°Π»ΠΈΠ·Π΅ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ
ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² Ρ
ΠΈΡΡΡΠ³ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π»Π΅ΡΠ΅Π½ΠΈΡ Π² ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ½ΠΎΠΌ Π»Π΅ΡΠ΅Π½ΠΈΠΈ Π³Π½ΠΎΠΉΠ½ΡΡ
Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠΉ ΠΊΠΎΡΡΠ΅ΠΉ ΠΈ ΡΡΡΡΠ°Π²ΠΎΠ² Ρ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ², Π³ΠΎΡΠΏΠΈΡΠ°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
Π² ΠΎΡΠ΄Π΅Π»Π΅Π½ΠΈΠ΅ Π³Π½ΠΎΠΉΠ½ΠΎΠΉ Ρ
ΠΈΡΡΡΠ³ΠΈΠΈ Π ΠΠ ΠΈΠΌ. Π.Π. ΠΡΠ²Π°ΡΠΎΠ²Π° (Π³. Π£ΡΠ°) Π² ΠΏΠ΅ΡΠΈΠΎΠ΄ 2017β2018 Π³Π³. (285Β ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ²).Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΈ ΠΎΠ±ΡΡΠΆΠ΄Π΅Π½ΠΈΠ΅. Π ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ΅ Π°Π½Π°Π»ΠΈΠ·Π° ΡΠΎΠ±ΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΠΌΠ°ΡΠ΅ΡΠΈΠ°Π»Π° Π²ΡΡΠ²Π»Π΅Π½ΠΎ, ΡΡΠΎ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΡΠ°ΡΡΡΠΌΠΈ ΠΏΡΠΈΡΠΈΠ½Π°ΠΌΠΈ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΠΎΡΡΠ΅ΠΎΠΌΠΈΠ΅Π»ΠΈΡΠΎΠ² ΡΠ²Π»ΡΡΡΡΡ ΡΡΠ°Π²ΠΌΡ (73,21Β %) ΠΈ ΠΈΠ½ΡΠ΅ΠΊΡΠΈΠΎΠ½Π½ΡΠ΅ ΠΎΡΠ»ΠΎΠΆΠ½Π΅Π½ΠΈΡ ΠΏΠΎΡΠ»Π΅ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΉ Π½Π° ΠΊΠΎΡΡΡΡ
ΠΈΒ ΡΡΡΡΠ°Π²Π°Ρ
(15,03Β %). ΠΠΎΠΌΠΏΠ»Π΅ΠΊΡ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΌΠ΅ΡΠΎΠΏΡΠΈΡΡΠΈΠΉ ΠΏΡΠΈ ΠΏΠΎΠ΄ΠΎΠ·ΡΠ΅Π½ΠΈΠΈ Π½Π° ΠΎΡΡΠ΅ΠΎΠΌΠΈΠ΅Π»ΠΈΡΠΈΡΠ΅ΡΠΊΠΈΠΉ ΠΏΡΠΎΡΠ΅ΡΡ, Π²ΠΊΠ»ΡΡΠ°ΡΡΠΈΠΉ Π² ΡΠ΅Π±Ρ ΡΠ΅Π½ΡΠ³Π΅Π½ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅, ΠΎΠ±ΡΠ΅ΠΊΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΠΈΠ΅ Π°Π½Π°Π»ΠΈΠ·Ρ, Π΄ΠΎΠΏΠΎΠ»Π½Π΅Π½Π½ΡΠ΅ ΡΠΈΡΡΡΠ»ΠΎΠ³ΡΠ°ΡΠΈΠ΅ΠΉ ΠΈΠ»ΠΈ ΠΠ’ ΠΏΠΎΡΠ°ΠΆΠ΅Π½Π½ΠΎΠΉ ΠΎΠ±Π»Π°ΡΡΠΈ Π΄ΠΎ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΈ ΠΈ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ ΠΌΠ°ΡΠ΅ΡΠΈΠ°Π»Π° ΠΏΠΎΡΠ»Π΅ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΈ, ΠΎΠΏΡΠΈΠΌΠ°Π»Π΅Π½ Π΄Π»Ρ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ ΠΎΡΡΠ΅ΠΎΠΌΠΈΠ΅Π»ΠΈΡΠΎΠ² ΡΠ°Π·Π»ΠΈΡΠ½ΠΎΠΉ ΡΡΠΈΠΎΠ»ΠΎΠ³ΠΈΠΈ Π² Π±ΠΎΠ»ΡΡΠΈΠ½ΡΡΠ²Π΅ ΡΠ»ΡΡΠ°Π΅Π². ΠΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ
ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² Ρ
ΠΈΡΡΡΠ³ΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ ΠΈ ΠΏΠ»Π°ΡΡΠΈΠΊΠΈ ΠΊΠΎΡΡΠ½ΡΡ
Π΄Π΅ΡΠ΅ΠΊΡΠΎΠ² Π² ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ½ΠΎΠΌ Π»Π΅ΡΠ΅Π½ΠΈΠΈ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² ΡΒ Ρ
ΡΠΎΠ½ΠΈΡΠ΅ΡΠΊΠΈΠΌΠΈ ΠΎΡΡΠ΅ΠΎΠΌΠΈΠ΅Π»ΠΈΡΠ°ΠΌΠΈ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ Π΄ΠΎΡΡΠΎΠ²Π΅ΡΠ½ΠΎ ΡΠΌΠ΅Π½ΡΡΠΈΡΡ ΡΠ°ΡΡΠΎΡΡ ΡΠ΅ΡΠΈΠ΄ΠΈΠ²Π° Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΡ. ΠΠ΅ΡΠ΅Π½ΠΈΠ΅ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Ρ ΠΎΡΡΠ΅ΠΎΠΌΠΈΠ΅Π»ΠΈΡΠ°ΠΌΠΈ ΠΏΡΠ΅Π΄ΠΏΠΎΡΡΠΈΡΠ΅Π»ΡΠ½Π΅Π΅ ΠΎΡΡΡΠ΅ΡΡΠ²Π»ΡΡΡ Π² ΠΊΡΡΠΏΠ½ΡΡ
Ρ
ΠΈΡΡΡΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΡΠ°ΡΠΈΠΎΠ½Π°ΡΠ°Ρ
, ΠΈΠΌΠ΅ΡΡΠΈΡ
Π² ΡΠ²ΠΎΠ΅ΠΌ ΡΠΎΡΡΠ°Π²Π΅ ΡΠΏΠ΅ΡΠΈΠ°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ΅ Ρ
ΠΈΡΡΡΠ³ΠΈΡΠ΅ΡΠΊΠΎΠ΅ ΠΎΡΠ΄Π΅Π»Π΅Π½ΠΈΠ΅ Π΄Π»Ρ Π»Π΅ΡΠ΅Π½ΠΈΡ Ρ
ΠΈΡΡΡΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈΠ½ΡΠ΅ΠΊΡΠΈΠΉ ΠΈ ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΡΡΡΠΈΠ΅ Π²ΡΠΏΠΎΠΌΠΎΠ³Π°ΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠ»ΡΠΆΠ±Ρ.ΠΠ°ΠΊΠ»ΡΡΠ΅Π½ΠΈΠ΅. ΠΠ΄Π½ΠΎΠ·Π½Π°ΡΠ½ΠΎ Π»ΡΡΡΠ΅Π³ΠΎ ΠΌΠ΅ΡΠΎΠ΄Π° Π»Π΅ΡΠ΅Π½ΠΈΡ ΠΎΡΡΠ΅ΠΎΠΌΠΈΠ΅Π»ΠΈΡΠΎΠ², Π½Π° Π½Π°Ρ Π²Π·Π³Π»ΡΠ΄, Π½Π΅ ΡΡΡΠ΅ΡΡΠ²ΡΠ΅Ρ. ΠΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΡΠΉ ΡΡΡΠ΅ΠΊΡ ΠΏΡΠΈ Π»Π΅ΡΠ΅Π½ΠΈΠΈ ΠΎΡΡΠ΅ΠΎΠΌΠΈΠ΅Π»ΠΈΡΠΎΠ² Π΄ΠΎΡΡΠΈΠ³Π°Π΅ΡΡΡ ΠΏΡΠΈ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠΈ ΠΈΠ½Π΄ΠΈΠ²ΠΈΠ΄ΡΠ°Π»ΡΠ½ΠΎ ΠΏΠΎΠ΄ΠΎΠ±ΡΠ°Π½Π½ΠΎΠ³ΠΎ Π½Π°Π±ΠΎΡΠ° Π»Π΅ΡΠ΅Π±Π½ΡΡ
ΠΌΠ΅ΡΠΎΠΏΡΠΈΡΡΠΈΠΉ Ρ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ΠΌ ΡΠ°ΠΊΠΈΡ
ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ², ΠΊΠ°ΠΊ Π»Π°Π·Π΅ΡΠ½Π°Ρ Π²Π°ΠΏΠΎΡΠΈΠ·Π°ΡΠΈΡ, Π²Π°ΠΊΡΡΠΌΠ½Π°Ρ ΡΠ΅ΡΠ°ΠΏΠΈΡ ΡΠ°Π½, ΡΠ»ΡΡΡΠ°Π·Π²ΡΠΊΠΎΠ²Π°Ρ ΠΊΠ°Π²ΠΈΡΠ°ΡΠΈΡ Π² Π²ΠΎΡΠΏΠ°Π»ΠΈΡΠ΅Π»ΡΠ½ΠΎΠΌ ΠΎΡΠ°Π³Π΅, ΠΏΠ»Π°ΡΡΠΈΠΊΠ° ΠΏΠΎΡΡΡΡΠ΅ΠΏΠ°Π½Π°ΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ Π΄Π΅ΡΠ΅ΠΊΡΠ° ΠΊΠΎΡΡΠΈ ΠΈΠ»ΠΈ ΡΠ°Π½Ρ