129 research outputs found
Dispersive Shock Wave, Generalized Laguerre Polynomials and Asymptotic Solitons of the Focusing Nonlinear Schr\"odinger Equation
We consider dispersive shock wave to the focusing nonlinear Schr\"odinger
equation generated by a discontinuous initial condition which is periodic or
quasi-periodic on the left semi-axis and zero on the right semi-axis. As an
initial function we use a finite-gap potential of the Dirac operator given in
an explicit form through hyper-elliptic theta-functions. The paper aim is to
study the long-time asymptotics of the solution of this problem in a vicinity
of the leading edge, where a train of asymptotic solitons are generated. Such a
problem was studied in \cite{KK86} and \cite{K91} using Marchenko's inverse
scattering technics. We investigate this problem exceptionally using the
Riemann-Hilbert problems technics that allow us to obtain explicit formulas for
the asymptotic solitons themselves that in contrast with the cited papers where
asymptotic formulas are obtained only for the square of absolute value of
solution. Using transformations of the main RH problems we arrive to a model
problem corresponding to the parametrix at the end points of continuous
spectrum of the Zakharov-Shabat spectral problem. The parametrix problem is
effectively solved in terms of the generalized Laguerre polynomials which are
naturally appeared after appropriate scaling of the Riemann-Hilbert problem in
a small neighborhoods of the end points of continuous spectrum. Further
asymptotic analysis give an explicit formula for solitons at the edge of
dispersive wave. Thus, we give the complete description of the train of
asymptotic solitons: not only bearing envelope of each asymptotic soliton, but
its oscillating structure are found explicitly. Besides the second term of
asymptotics describing an interaction between these solitons and oscillating
background is also found. This gives the fine structure of the edge of
dispersive shock wave.Comment: 36 pages, 5 figure
Magnetic resonance imaging in recognition of ectopic pancreatic tissue (Clinical observation)
A rare clinical observation of retroperitoneal ectopy of pancreatic tissue with diligence to the kidney and jejunum is presented. For the first time, semiotics of ectopia is described according to the data of magnetic resonance imaging, which allows to determine with a high degree of reliability the belonging of the revealed structures to the tissues of the pancreas
ΠΠΈΡΡΡΠ°Π»ΡΠ½Π°Ρ Π±ΡΠΎΠ½Ρ ΠΎΡΠΊΠΎΠΏΠΈΡ ΠΌΡΠ»ΡΡΠΈΡΠΏΠΈΡΠ°Π»ΡΠ½ΠΎΠΉ ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΠΎΠΉ ΡΠΎΠΌΠΎΠ³ΡΠ°ΡΠΈΠΈ Π² Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠ΅ ΠΎΠΏΡΡ ΠΎΠ»Π΅Π²ΡΡ ΠΏΠΎΡΠ°ΠΆΠ΅Π½ΠΈΠΉ ΡΡΠ°Ρ Π΅ΠΎΠ±ΡΠΎΠ½Ρ ΠΈΠ°Π»ΡΠ½ΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΡ
The purpose: to assess the value of techniques of virtual bronchoscopy(VB) in improving the diagnostic information content of multislice computed tomography (MSCT) in the diagnosis and prevalence of neoplastic lesions of the tracheobronchial system (TBS).Materials and methods. Analyzed the data virtual bronchoscopy multislice computed tomography we have developed methods 87 patients with tumors of TBS.Results. A comprehensive analysis of native, postprocessing data and volumetric reconstructions was possible to more fully assess the nature of the changes to the topography, the extent and prevalence of neoplastic lesions of the tracheobronchial system. To carry out differential diagnostics of benign and malignant lesions, especially in stenotic lesions, when the execution of bronchofibroscopy was impossible. Signs of malignancy of the tumor had a wide base and destroys the adjacent cartilage structures, a rough bumpy surface, infiltration of the wall of the trachea, bronchus.Conclusion. WB MSCT optimal method of diagnosis, determining the probabilistic nature of neoplastic lesions of TBS, the prevalence of the process. When stenotic lesions of the trachea, WB MSCT is becoming the method of choice in assessing the extent of the process.Β Π¦Π΅Π»Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ: ΡΡΠΎΡΠ½ΠΈΡΡ Π·Π½Π°ΡΠ΅Π½ΠΈΠ΅ ΠΌΠ΅ΡΠΎΠ΄ΠΈΠΊ Π²ΠΈΡΡΡΠ°Π»ΡΠ½ΠΎΠΉ Π±ΡΠΎΠ½Ρ
ΠΎΡΠΊΠΎΠΏΠΈΠΈ (ΠΠ) Π² ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΠΈ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠ²Π½ΠΎΡΡΠΈ ΠΌΡΠ»ΡΡΠΈΡΠΏΠΈΡΠ°Π»ΡΠ½ΠΎΠΉ ΠΊΠΎΠΌ- ΠΏΡΡΡΠ΅ΡΠ½ΠΎΠΉ ΡΠΎΠΌΠΎΠ³ΡΠ°ΡΠΈΠΈ (ΠΠ‘ΠΠ’) Π² Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠ΅ ΠΈ ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½Π½ΠΎΡΡΠΈ ΠΎΠΏΡΡ
ΠΎΠ»Π΅Π²ΠΎΠ³ΠΎ ΠΏΠΎΡΠ°ΠΆΠ΅Π½ΠΈΡ ΡΡΠ°Ρ
Π΅ΠΎΠ±ΡΠΎΠ½Ρ
ΠΈ- Π°Π»ΡΠ½ΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΡ (Π’ΠΠ‘).ΠΠ°ΡΠ΅ΡΠΈΠ°Π» ΠΈ ΠΌΠ΅ΡΠΎΠ΄Ρ. ΠΡΠΎΠ°Π½Π°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Ρ Π΄Π°Π½Π½ΡΠ΅ ΠΠ ΠΠ‘ΠΠ’ ΠΏΠΎ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π½ΠΎΠΉ Π½Π°ΠΌΠΈ ΠΌΠ΅ΡΠΎΠ΄ΠΈΠΊΠ΅ Ρ 87 Π±ΠΎΠ»ΡΠ½ΡΡ
Ρ ΠΎΠΏΡΡ
ΠΎΠ»ΡΠΌ Π’ΠΠ‘.Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ. ΠΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ½ΡΠΉ Π°Π½Π°Π»ΠΈΠ· Π½Π°ΡΠΈΠ²Π½ΡΡ
, ΠΏΠΎΡΡΠΏΡΠΎΡΠ΅ΡΡΠΈΠ½Π³ΠΎΠ²ΡΡ
ΠΈ Π΄Π°Π½Π½ΡΡ
ΠΎΠ±ΡΠ΅ΠΌΠ½ΡΡ
ΡΠ΅ΠΊΠΎΠ½ΡΡΡΡΠΊΡΠΈΠΉ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ» Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΠΏΠΎΠ»Π½ΠΎ ΠΎΡΠ΅Π½ΠΈΡΡ ΠΏΡΠΈΡΠΎΠ΄Ρ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΠΉ, ΡΠΎΠΏΠΈΠΊΡ, ΠΏΡΠΎΡΡΠΆΠ΅Π½Π½ΠΎΡΡΡ ΠΈ ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½Π½ΠΎΡΡΡ ΠΎΠΏΡΡ
ΠΎΠ»Π΅Π²ΠΎΠ³ΠΎ ΠΏΠΎΡΠ°ΠΆΠ΅Π½ΠΈΡ Π’ΠΠ‘, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΏΡΠΎΠ²Π΅ ΡΡΠΈ Π΄ΠΈΡΡΠ΅ΡΠ΅Π½ΡΠΈΠ°Π»ΡΠ½ΡΡ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΡ Π΄ΠΎΠ±ΡΠΎΠΊΠ°ΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΠΈ Π·Π»ΠΎΠΊΠ°ΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΠΏΠΎΡΠ°ΠΆΠ΅Π½ΠΈΡ, ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎ ΠΏΡΠΈ ΡΡΠ΅Π½ΠΎ Π·ΠΈΡΡΡΡΠΈΡ
ΠΏΠΎΡΠ°ΠΆΠ΅Π½ΠΈΡΡ
, ΠΊΠΎΠ³Π΄Π° Π²ΡΠΏΠΎΠ»Π½Π΅Π½ΠΈΠ΅ Π±ΡΠΎΠ½Ρ
ΠΎΡΠΈΠ±ΡΠΎΡΠΊΠΎΠΏΠΈΠΈ Π±ΡΠ»ΠΎ Π½Π΅Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎ. ΠΡΠΈΠ·Π½Π°ΠΊΠ°ΠΌΠΈ Π·Π»ΠΎΠΊΠ°ΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΡΡΠΈ ΠΎΠΏΡΡ
ΠΎΠ»ΠΈ Π±ΡΠ»ΠΈ ΡΠΈΡΠΎΠΊΠΎΠ΅ ΠΎΡΠ½ΠΎΠ²Π°Π½ΠΈΠ΅ Ρ ΡΠ°Π·ΡΡΡΠ΅Π½ΠΈΠ΅ΠΌ ΠΏΠΎΠ΄Π»Π΅ΠΆΠ°ΡΠΈΡ
Ρ
ΡΡΡΠ΅Π²ΡΡ
ΡΡΡΡΠΊΡΡΡ, Π½Π΅ΡΠΎΠ²Π½Π°Ρ Π±ΡΠ³ΡΠΈΡΡΠ°Ρ ΠΏΠΎΠ²Π΅ΡΡ
Π½ΠΎΡΡΡ, ΠΈΠ½ΡΠΈΠ»ΡΡΡΠ°ΡΠΈΡ ΡΡΠ΅Π½ΠΊΠΈ ΡΡΠ°Ρ
Π΅ΠΈ, Π±ΡΠΎΠ½Ρ
ΠΎΠ².ΠΡΠ²ΠΎΠ΄Ρ. ΠΠ ΠΠ‘ΠΠ’ β ΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΡΠΉ ΠΌΠ΅ΡΠΎΠ΄ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ, ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ Π²Π΅ΡΠΎΡΡΠ½ΠΎΡΡΠ½ΠΎΠΉ ΠΏΡΠΈΡΠΎΠ΄Ρ ΠΎΠΏΡΡ
ΠΎΠ»Π΅Π²ΡΡ
ΠΏΠΎΡΠ°ΠΆΠ΅Π½ΠΈΠΉ Π’ΠΠ‘, ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½Π½ΠΎΡΡΠΈ ΠΏΡΠΎΡΠ΅ΡΡΠ°. ΠΡΠΈ ΡΡΠ΅Π½ΠΎΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΠΎΡΠ°ΠΆΠ΅Π½ΠΈΡΡ
ΡΡΠ°Ρ
Π΅ΠΈ ΠΠ‘ΠΠ’ ΠΠ ΡΡΠ°Π½ΠΎΠ²ΠΈΡΡΡ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠΌ Π²ΡΠ±ΠΎΡΠ° Π² ΠΎΡΠ΅Π½ΠΊΠ΅ ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½Π½ΠΎΡΡΠΈ ΠΏΡΠΎΡΠ΅ΡΡΠ°
ΠΠΈΡΡΡΠ°Π»ΡΠ½Π°Ρ Π±ΡΠΎΠ½Ρ ΠΎΡΠΊΠΎΠΏΠΈΡ ΠΌΡΠ»ΡΡΠΈΡΠΏΠΈΡΠ°Π»ΡΠ½ΠΎΠΉ ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΠΎΠΉ ΡΠΎΠΌΠΎΠ³ΡΠ°ΡΠΈΠΈ Π² Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠ΅ ΠΎΠΏΡΡ ΠΎΠ»Π΅Π²ΡΡ ΠΏΠΎΡΠ°ΠΆΠ΅Π½ΠΈΠΉ ΡΡΠ°Ρ Π΅ΠΈ
The aim of this study was to estimate diagnostic value of virtual bronchoscopy (VB) in patients with trachea neoplasms.Methods. Results of multidetector computed tomography with virtual bronchoscopy (MDCT"VB) were analyzed in 31 patients with neoplastic lesions of the trachea according to an original method developed in our institution.Results. The method allowed complete evaluation of location and extension of tumors, differentiation between benign and malignant tumors including those complicated by stenosis precluded from bronchoscopic examination. A sessileΒ tumor with destructed underlying tissue, a rough surface, infiltrated tracheal wall, tumor extension outside the trachea, and infiltration of the mediastinum were considered as malignant signs. Similar signs were seen in metastatic lesions of the trachea from lung carcinoma: the carina and the distal trachea were injured if the tumor grew from a main bronchus, a tracheal wall was injured if a lung tumor penetrated the mediastinum.Conclusion.Β MDCT"VB appears to be the optimal diagnostic method in tumors of the trachea. In cases of trachea stenosis, MDCT"VB is the preferable methodΒ for evaluation of the tumor extension. Virtual modeling of an intratracheal tumor could help to make a decision about radical treatment.ΠΠ°ΡΠ΅ΡΠΈΠ°Π»Ρ ΠΈ ΠΌΠ΅ΡΠΎΠ΄Ρ. ΠΡΠΎΠ°Π½Π°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Ρ Π΄Π°Π½Π½ΡΠ΅ Π²ΠΈΡΡΡΠ°Π»ΡΠ½ΠΎΠΉ Π±ΡΠΎΠ½Ρ
ΠΎΡΠΊΠΎΠΏΠΈΠΈ, ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΡΠ΅ Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΠΌΡΠ»ΡΡΠΈΡΠΏΠΈΡΠ°Π»ΡΠ½ΠΎΠΉ ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΠΎΠΉ ΡΠΎΠΌΠΎΠ³ΡΠ°ΡΠΈΠΈ ΠΏΠΎ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π½ΠΎΠΉ Π°Π²ΡΠΎΡΡΠΊΠΎΠΉ ΠΌΠ΅ΡΠΎΠ΄ΠΈΠΊΠ΅ Ρ Π±ΠΎΠ»ΡΠ½ΡΡ
(n = 31) Ρ ΠΎΠΏΡΡ
ΠΎΠ»Π΅Π²ΡΠΌΠΈ ΠΏΠΎΡΠ°ΠΆΠ΅Π½ΠΈΡΠΌΠΈ ΡΡΠ°Ρ
Π΅ΠΈ ΠΏΠ΅ΡΠ²ΠΈΡΠ½ΠΎΠ³ΠΎ ΠΈ Π²ΡΠΎΡΠΈΡΠ½ΠΎΠ³ΠΎΒ Π³Π΅Π½Π΅Π·Π°.Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ. ΠΡΠΈ Π΄Π°Π½Π½ΠΎΠΌ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄Π΅ ΠΊ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠ΅ ΠΎΠΏΡΡ
ΠΎΠ»Π΅Π²ΡΡ
ΠΏΠΎΡΠ°ΠΆΠ΅Π½ΠΈΠΉ ΠΎΡΠ³Π°Π½Π° ΠΏΠΎΡΠ²ΠΈΠ»Π°ΡΡ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΡ Π½Π΅ ΡΠΎΠ»ΡΠΊΠΎ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΠΏΠΎΠ»Π½ΠΎΠΉ ΠΎΡΠ΅Π½ΠΊΠΈ ΡΠΎΠΏΠΈΠΊΠΈ, ΠΏΡΠΎΡΡΠΆΠ΅Π½Π½ΠΎΡΡΠΈ ΠΈ ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½Π½ΠΎΡΡΠΈ ΠΏΡΠΎΡΠ΅ΡΡΠ°, Π½ΠΎ ΠΈ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ Π΄ΠΈΡΡΠ΅ΡΠ΅Π½ΡΠΈΠ°Π»ΡΠ½ΠΎΠΉ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ Π΄ΠΎΠ±ΡΠΎΠΊΠ°ΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΠΈ Π·Π»ΠΎΠΊΠ°ΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΠΏΠΎΡΠ°ΠΆΠ΅Π½ΠΈΡ, ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎ ΠΏΡΠΈ ΡΡΠ΅Π½ΠΎΠ·ΠΈΡΡΡΡΠΈΡ
ΠΏΠΎΡΠ°ΠΆΠ΅Π½ΠΈΡΡ
, ΠΊΠΎΠ³Π΄Π° Π²ΡΠΏΠΎΠ»Π½Π΅Π½ΠΈΠ΅ ΡΠΈΠ±ΡΠΎΠ±ΡΠΎΠ½Ρ
ΠΎΡΠΊΠΎΠΏΠΈΠΈ Π½Π΅Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎ.ΠΠ°ΠΊΠ»ΡΡΠ΅Π½ΠΈΠ΅. ΠΡΠΈΠ·Π½Π°ΠΊΠ°ΠΌΠΈ Π·Π»ΠΎΠΊΠ°ΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΡΡΠΈ ΠΎΠΏΡΡ
ΠΎΠ»ΠΈ ΡΠ²ΠΈΠ»ΠΈΡΡ ΡΠΈΡΠΎΠΊΠΎΠ΅ ΠΎΡΠ½ΠΎΠ²Π°Π½ΠΈΠ΅ Ρ ΡΠ°Π·ΡΡΡΠ΅Π½ΠΈΠ΅ΠΌ ΠΏΠΎΠ΄Π»Π΅ΠΆΠ°ΡΠΈΡ
Ρ
ΡΡΡΠ΅Π²ΡΡ
ΡΡΡΡΠΊΡΡΡ, Π½Π΅ΡΠΎΠ²Π½Π°Ρ Π±ΡΠ³ΡΠΈΡΡΠ°Ρ ΠΏΠΎΠ²Π΅ΡΡ
Π½ΠΎΡΡΡ, ΠΈΠ½ΡΠΈΠ»ΡΡΡΠ°ΡΠΈΡ ΡΡΠ΅Π½ΠΊΠΈ ΡΡΠ°Ρ
Π΅ΠΈ, Π²ΡΡ
ΠΎΠ΄ ΠΏΡΠΎΡΠ΅ΡΡΠ° Π·Π° ΠΏΡΠ΅Π΄Π΅Π»Ρ ΠΎΡΠ³Π°Π½Π° Ρ ΠΈΠ½ΡΠΈΠ»ΡΡΡΠ°ΡΠΈΠ΅ΠΉ ΡΠΊΠ°Π½Π΅ΠΉ ΡΡΠ΅Π΄ΠΎΡΡΠ΅Π½ΠΈΡ,Β ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½ΠΈΡ Π½Π° ΠΏΠΈΡΠ΅Π²ΠΎΠ΄. ΠΡΠΈ Π²ΡΠΎΡΠΈΡΠ½ΡΡ
ΠΏΠΎΡΠ°ΠΆΠ΅Π½ΠΈΡΡ
ΡΡΠ°Ρ
Π΅ΠΈ ΠΏΡΠΈ ΡΠ°ΠΊΠ΅ Π»Π΅Π³ΠΊΠΎΠ³ΠΎ Π½Π°Π±Π»ΡΠ΄Π°Π»ΠΈΡΡ ΡΠ΅ ΠΆΠ΅ ΠΏΡΠΈΠ·Π½Π°ΠΊΠΈ β ΠΏΠΎΡΠ°ΠΆΠ΅Π½ΠΈΠ΅ ΠΊΠ°ΡΠΈΠ½ΡΒ ΠΈ Π΄ΠΈΡΡΠ°Π»ΡΠ½ΡΡ
ΠΎΡΠ΄Π΅Π»ΠΎΠ² ΡΡΠ°Ρ
Π΅ΠΈ β ΠΏΡΠΈ ΡΠΎΡΡΠ΅ ΠΈΠ· Π³Π»Π°Π²Π½ΠΎΠ³ΠΎ Π±ΡΠΎΠ½Ρ
Π°, ΡΡΠ΅Π½ΠΊΠΈ ΡΡΠ°Ρ
Π΅ΠΈ β ΠΏΡΠΈ ΠΏΡΠΎΡΠ°ΡΡΠ°Π½ΠΈΠΈ ΡΠ°ΠΊΠ° Π»Π΅Π³ΠΊΠΎΠ³ΠΎ Π² ΡΡΠ΅Π΄ΠΎΡΡΠ΅Π½ΠΈΠ΅
Metastasises small cell lung cancer, revealed at multislises computer tomography (MSCT)
For the purpose of specification of possibilities of MSCT in revealing metastases, estimation of semiotics of metastatic lesion of various organs by small cell lung cancer by means of MSCT it has been examined 372 patients (348 men, 24 women) with hystologically verified SCLC aging from 29 to 81 years. Metastases have been revealed in 205 of 372 patients (55,1 %). Most frequently in lungs - 97 patients (47,32 %), in 45 (21,95 % ) patients metastases were revealed in adrenal glands, in 67 (32,68 % ) patients metastases were revealed in brain, in 53 (25,85 % ) - in liver, in 39 (19 % ) patients - in bone. In 178 (86,83 % ) cases have been revealed plural metastases, at 145 patients that has made 70,7 % from all patients with metastases - metastases had several localizations. Metastases in liver, adrenal glands, bones had typical semiotics, around metastases in brain from 21,95 % of cases was not defined an edema, lesion did not cause dislocation syndrome. It distinguished metastases SCLC. Research has shown high sensitivity in revealing of metastases on MSCT and high prevalence of metastatic lesion at SCLC.Π‘ ΡΠ΅Π»ΡΡ ΡΡΠΎΡΠ½Π΅Π½ΠΈΡ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΠ΅ΠΉ ΠΌΠ½ΠΎΠ³ΠΎΡΡΠ΅Π·ΠΎΠ²ΠΎΠΉ ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΠΎΠΉ ΡΠΎΠΌΠΎΠ³ΡΠ°ΡΠΈΠΈ Π² Π²ΡΡΠ²Π»Π΅Π½ΠΈΠΈ ΠΌΠ΅ΡΠ°ΡΡΠ°Π·ΠΎΠ², ΠΎΡΠ΅Π½ΠΊΠΈ ΡΠ΅ΠΌΠΈΠΎΡΠΈΠΊΠΈ ΠΌΠ΅ΡΠ°ΡΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΏΠΎΡΠ°ΠΆΠ΅Π½ΠΈΡ ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
ΠΎΡΠ³Π°Π½ΠΎΠ² ΠΏΡΠΈ ΠΠΠ /1 Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΠΠ‘ΠΠ’ Π±ΡΠ»ΠΎ ΠΎΠ±ΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΎ 372 Π±ΠΎΠ»ΡΠ½ΡΡ
(348 ΠΌΡΠΆΡΠΈΠ½, 24 ΠΆΠ΅Π½ΡΠΈΠ½Ρ) Ρ Π³ΠΈΡΡΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈ Π²Π΅ΡΠΈΡΠΈΡΠΈΡΠΎΠ²Π°Π½Π½ΡΠΌ ΠΌΠ΅Π»ΠΊΠΎΠΊΠ»Π΅ΡΠΎΡΠ½ΡΠΌ ΡΠ°ΠΊΠΎΠΌ Π»Π΅Π³ΠΊΠΎΠ³ΠΎ Π² Π²ΠΎΠ·ΡΠ°ΡΡΠ΅ ΠΎΡ 29 Π΄ΠΎ 81 Π³ΠΎΠ΄Π°. ΠΠ΅ΡΠ°ΡΡΠ°Π·Ρ Π±ΡΠ»ΠΈ Π²ΡΡΠ²Π»Π΅Π½Ρ Ρ 205 ΠΈΠ· 372 Π±ΠΎΠ»ΡΠ½ΡΡ
(55,1%). ΠΠ°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΡΠ°ΡΡΠΎ Π²ΡΡΠ²Π»ΡΠ»ΠΈΡΡ ΠΌΠ΅ΡΠ°ΡΡΠ°Π·Ρ Π² Π»Π΅Π³ΠΊΠΈΠ΅ β 97 ΠΏΠ°ΡΠΈΠ΅Π½Ρ (47,32%), Ρ 45 (21,95%) ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Π±ΡΠ»ΠΈ Π²ΡΡΠ²Π»Π΅Π½Ρ ΠΌΠ΅ΡΠ°ΡΡΠ°Π·Ρ Π² Π½Π°Π΄ΠΏΠΎΡΠ΅ΡΠ½ΠΈΠΊΠΈ, Ρ 67 ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² (32,68%) Π±ΡΠ»ΠΈ Π²ΡΡΠ²Π»Π΅Π½Ρ ΠΌΠ΅ΡΠ°ΡΡΠ°Π·Ρ Π² Π³ΠΎΠ»ΠΎΠ²Π½ΠΎΠΉ ΠΌΠΎΠ·Π³, Ρ 53 (25,85%) - Π² ΠΏΠ΅ΡΠ΅Π½Ρ, Ρ 39 (19%) ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² - Π² ΠΊΠΎΡΡΠΈ. Π 178 (86,83%) ΡΠ»ΡΡΠ°ΡΡ
Π±ΡΠ»ΠΈ Π²ΡΡΠ²Π»Π΅Π½Ρ ΠΌΠ½ΠΎΠΆΠ΅ΡΡΠ²Π΅Π½Π½ΡΠ΅ ΠΌΠ΅ΡΠ°ΡΡΠ°Π·Ρ, Ρ 145 ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ², ΡΡΠΎ ΡΠΎΡΡΠ°Π²ΠΈΠ»ΠΎ 70,7 % ΠΎΡ Π²ΡΠ΅Ρ
Π±ΠΎΠ»ΡΠ½ΡΡ
Ρ ΠΌΠ΅ΡΠ°ΡΡΠ°Π·Π°ΠΌΠΈ - ΠΌΠ΅ΡΠ°ΡΡΠ°Π·Ρ Π½Π΅ΡΠΊΠΎΠ»ΡΠΊΠΈΡ
Π»ΠΎΠΊΠ°Π»ΠΈΠ·Π°ΡΠΈΠΉ. ΠΠ΅ΡΠ°ΡΡΠ°Π·Ρ Π² ΠΏΠ΅ΡΠ΅Π½Ρ, Π½Π°Π΄ΠΏΠΎΡΠ΅ΡΠ½ΠΈΠΊΠΈ, ΠΊΠΎΡΡΠΈ ΠΈΠΌΠ΅Π»ΠΈ ΡΠΈΠΏΠΈΡΠ½ΡΡ ΡΠ΅ΠΌΠΈΠΎΡΠΈΠΊΡ, Π²ΠΎΠΊΡΡΠ³ ΠΌΠ΅ΡΠ°ΡΡΠ°Π·ΠΎΠ² Π² Π³ΠΎΠ»ΠΎΠ²Π½ΠΎΠΉ ΠΌΠΎΠ·Π³ Ρ 21,95% ΡΠ»ΡΡΠ°Π΅Π² Π½Π΅ ΠΎΠΏΡΠ΅Π΄Π΅Π»ΡΠ»ΠΎΡΡ ΠΎΡΠ΅ΠΊΠ°, ΠΏΠΎΡΠ°ΠΆΠ΅Π½ΠΈΠ΅ Π½Π΅ Π²ΡΠ·ΡΠ²Π°Π»ΠΎ Π΄ΠΈΡΠ»ΠΎΠΊΠ°ΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ ΡΠΈΠ½Π΄ΡΠΎΠΌΠ°. ΠΡΠΎ ΠΎΡΠ»ΠΈΡΠ°Π»ΠΎ ΠΌΠ΅ΡΠ°ΡΡΠ°Π·Ρ ΠΌΠ΅Π»ΠΊΠΎΠΊΠ»Π΅ΡΠΎΡΠ½ΠΎΠ³ΠΎ ΡΠ°ΠΊΠ°. ΠΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΏΠΎΠΊΠ°Π·Π°Π»ΠΎ Π²ΡΡΠΎΠΊΡΡ ΡΡΠ²ΡΡΠ²ΠΈΡΠ΅Π»ΡΠ½ΠΎΡΡΡ Π² Π²ΡΡΠ²Π»Π΅Π½ΠΈΠΈ ΠΌΠ΅ΡΠ°ΡΡΠ°Π·ΠΎΠ² Π½Π° ΠΠ‘ΠΠ’ ΠΈ Π²ΡΡΠΎΠΊΡΡ ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½Π½ΠΎΡΡΡ ΠΌΠ΅ΡΠ°ΡΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΏΠΎΡΠ°ΠΆΠ΅Π½ΠΈΡ ΠΏΡΠΈ ΠΠΠ /1
ΠΠ΅ΡΠΎΠ΄Ρ ΠΌΠ΅Π΄ΠΈΡΠΈΠ½ΡΠΊΠΎΠΉ Π²ΠΈΠ·ΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ Π² Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠ΅ Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠΉ ΠΎΡΠ³Π°Π½ΠΎΠ² Π΄ΡΡ Π°Π½ΠΈΡ
Based on the assessment of radiological, computed tomographic, magnetic resonance, ultrasonic and other methods of radial diagnostics 3820 patients with various respiratory diseases were examined. Radiological syndromes of the diseases were classified. An algorithm was created for usage of the methods depending on clinical situation. A diagnostic logic is demonstrated on the example of the lung transparence disturbance syndrome.ΠΠ° ΠΎΡΠ½ΠΎΠ²Π°Π½ΠΈΠΈ Π°Π½Π°Π»ΠΈΠ·Π° Π΄Π°Π½Π½ΡΡ
ΡΠ΅Π½ΡΠ³Π΅Π½ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ, ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΠΎ-ΡΠΎΠΌΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ, ΠΌΠ°Π³Π½ΠΈΡΠ½ΠΎΡΠ΅Π·ΠΎΠ½Π°Π½ΡΠ½ΠΎΠ³ΠΎ, ΡΠ»ΡΡΡΠ°Π·Π²ΡΠΊΠΎΠ²ΠΎΠ³ΠΎ ΠΈ Π΄ΡΡΠ³ΠΈΡ
ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² Π»ΡΡΠ΅Π²ΠΎΠ³ΠΎ ΠΎΠ±ΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ 3820 Π±ΠΎΠ»ΡΠ½ΡΡ
Ρ ΡΠ°Π·Π»ΠΈΡΠ½ΡΠΌΠΈ ΠΏΠ°ΡΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠΌΠΈ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡΠΌΠΈ ΠΎΡΠ³Π°Π½ΠΎΠ²Π΄ΡΡ
Π°Π½ΠΈΡ ΡΠΈΡΡΠ΅ΠΌΠ°ΡΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Ρ ΡΠ΅Π½ΡΠ³Π΅Π½ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΡΠΈΠ½Π΄ΡΠΎΠΌΡ Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠΉ, ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π° ΡΡ
Π΅ΠΌΠ° Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ ΠΌΠ΅ΡΠΎΠ΄ΠΈΠΊ Π² Π·Π°Π²ΠΈΡΠΈΠΌΠΎΡΡΠΈ ΠΎΡ ΠΊΠ»ΠΈΠ½ΠΈΠΊΠΎ-Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠΈΡΡΠ°ΡΠΈΠΈ. ΠΠ° ΠΏΡΠΈΠΌΠ΅ΡΠ΅ ΡΠΈΠ½Π΄ΡΠΎΠΌΠ° Π½Π°ΡΡΡΠ΅Π½ΠΈΡ Π»Π΅Π³ΠΎΡΠ½ΠΎΠΉ ΠΏΡΠΎΠ·ΡΠ°ΡΠ½ΠΎΡΡΠΈ ΡΠ°Π·Π±ΠΈΡΠ°Π΅ΡΡΡ Π»ΠΎΠ³ΠΈΠΊΠ° ΠΏΠΎΡΡΠ°Π½ΠΎΠ²ΠΊΠΈ Π΄ΠΈΠ°Π³Π½ΠΎΠ·Π°
Π£Π»ΡΡΡΠ°Π·Π²ΡΠΊΠΎΠ²ΠΎΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ Π³ΡΡΠ΄Π½ΠΎΠΉ ΠΊΠ»Π΅ΡΠΊΠΈ ΠΏΡΠΈ ΠΏΡΠΈΡΡΠ΅Π½ΠΎΡΠ½ΡΡ , Π΄ΠΈΠ°ΡΡΠ°Π³ΠΌΠ°Π»ΡΠ½ΡΡ ΠΎΡΠ°Π³ΠΎΠ²ΡΡ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡΡ
Forty-nine patients with various pulmonary, pleural and mediastinal pathology were examined with ultrasound. The ultrasonic approach was chosen after radiological and computed tomographic results had been analyzed. Ultrasound is a highly effective method for determination of cystic changes, lung cancer spreading, differentiation of various pleural diseases, differential diagnostics of retrosternal goitre, thymomas and lymphomas. This method is an important part of radial diagnostics of respiratory diseases. Its data make an origin of pathology more precise.ΠΡΠΎΠ²Π΅Π΄Π΅Π½ΠΎ ΡΠ»ΡΡΡΠ°Π·Π²ΡΠΊΠΎΠ²ΠΎΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ 49 Π±ΠΎΠ»ΡΠ½ΡΠΌ Ρ ΡΠ°Π·Π»ΠΈΡΠ½ΠΎΠΉ ΠΏΠ°ΡΠΎΠ»ΠΎΠ³ΠΈΠ΅ΠΉ Π»Π΅Π³ΠΊΠΈΡ
, ΠΏΠ»Π΅Π²ΡΡ, ΡΡΠ΅Π΄ΠΎΡΡΠ΅Π½ΠΈΡ. ΠΡΠ±ΠΎΡ Π΄ΠΎΡΡΡΠΏΠ° ΠΎΡΡΡΠ΅ΡΡΠ²Π»ΡΠ»ΡΡ ΠΏΠΎΡΠ»Π΅ Π°Π½Π°Π»ΠΈΠ·Π° ΡΠ΅Π½ΡΠ³Π΅Π½ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ, ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΠΎ-ΡΠΎΠΌΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ . Π£ΠΠ β Π²ΡΡΠΎΠΊΠΎΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΡΠΉ ΠΌΠ΅ΡΠΎΠ΄ ΡΡΠΎΡΠ½Π΅Π½ΠΈΡ ΠΊΠΈΡΡΠΎΠ·Π½ΠΎΠΉ ΠΏΡΠΈΡΠΎΠ΄Ρ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΠΉ, ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½Π½ΠΎΡΡΠΈ ΡΠ°ΠΊΠ° Π»Π΅Π³ΠΊΠΎΠ³ΠΎ, Π΄ΠΈΡΡΠ΅ΡΠ΅Π½ΡΠΈΠ°ΡΠΈΠΈ Π³Π΅Π½Π΅Π·Π° ΠΏΠ»Π΅Π²ΡΠ°Π»ΡΠ½ΡΡ
ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΠΉ, Π΄ΠΈΡΡΠ΅ΡΠ΅Π½ΡΠΈΠ°Π»ΡΠ½ΠΎΠΉ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ Π·Π°Π³ΡΡΠ΄ΠΈΠ½Π½ΠΎΠ³ΠΎ Π·ΠΎΠ±Π°, ΡΠΈΠΌΠΎΠΌ. Π»ΠΈΠΌΡΠΎΠΌ. ΠΠ΅ΡΠΎΠ΄ β Π²Π°ΠΆΠ½Π°Ρ ΡΠΎΡΡΠ°Π²Π»ΡΡΡΠ°Ρ Π»ΡΡΠ΅Π²ΠΎΠΉ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠΉ ΠΎΡΠ³Π°Π½ΠΎΠ² Π΄ΡΡ
Π°Π½ΠΈΡ, Π΄Π°Π½Π½ΡΠ΅ ΠΊΠΎΡΠΎΡΠΎΠ³ΠΎ ΡΡΠΎΡΠ½ΡΡΡ ΠΏΡΠΈΡΠΎΠ΄Ρ, Π½ΠΎΠ·ΠΎΠ»ΠΎΠ³ΠΈΡ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΠΉ
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