{"id":7242,"date":"2020-08-06T10:02:03","date_gmt":"2020-08-06T15:02:03","guid":{"rendered":"https:\/\/cornflowerblue-rail-980953.hostingersite.com\/?p=7242"},"modified":"2020-08-06T10:05:22","modified_gmt":"2020-08-06T15:05:22","slug":"7242","status":"publish","type":"post","link":"https:\/\/staging-hub.acoredu.com\/tr\/7242\/","title":{"rendered":"What’s new in Cardiothoracic Imaging (Turkish) – August 2020"},"content":{"rendered":"

Toraks<\/strong><\/span><\/h3>\n

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Kantitatif Bilgisayarl\u0131 Tomografik G\u00f6r\u00fcnt\u00fclemenin Kronik Obstr\u00fcktif Akci\u011fer Hastal\u0131\u011f\u0131n\u0131 ve Fibrotik \u0130nterstisyel Akci\u011fer Hastal\u0131\u011f\u0131n\u0131 Anlamada Kullan\u0131m\u0131: Son Teknoloji ile Gelece\u011fe Do\u011fru<\/span><\/strong><\/h5>\n

Castillo-Saldana, D., et al. (2020). “Using Quantitative Computed Tomographic Imaging to Understand Chronic Obstructive Pulmonary Disease and Fibrotic Interstitial Lung Disease: State of the Art and Future Directions.” J Thorac Imaging<\/u> 35<\/strong>(4): 246-254.<\/p>\n

Key Words: COPD, ILD, Quantitative CT<\/p>\n

https:\/\/journals.lww.com\/thoracicimaging\/Fulltext\/2020\/07000\/Using_Quantitative_Computed_Tomographic_Imaging_to.6.aspx<\/a><\/p>\n

St. Paul\u2019s Hastanesi ve British Columbia \u00dcniversitesinden ara\u015ft\u0131rmac\u0131lar, Kronik Obstr\u00fcktif Akci\u011fer Hastal\u0131\u011f\u0131 (KOAH) ve \u0130nsterstisyel Akci\u011fer Hastal\u0131klar\u0131 (\u0130AH) alanlar\u0131nda Kantitatif BT\u2019nin (KBT) ilgi \u00e7ekici uygulamalar\u0131ndan bahsediyor. Ryerson ve ark. KBT uygulamalar\u0131n\u0131n, tipik KOAH ve \u0130AH de\u011ferlendirmelerine ek olarak bu iki kompleks hastal\u0131k modelinin te\u015fhis ve takibinde kullan\u0131labilece\u011fini \u00f6ne s\u00fcr\u00fcyor. KBT; KOAH hastalar\u0131nda amfizem b\u00f6lgelerinin do\u011fru haritalanmas\u0131, hava yolu kal\u0131nla\u015fmalar\u0131n\u0131n saptanmas\u0131 ve terminal bron\u015fiollerde erken evre hastal\u0131klar\u0131n saptanmas\u0131 i\u00e7in bron\u015f a\u011fac\u0131n\u0131n 3 boyutlu rekonstr\u00fcksiyonunda kullan\u0131labilir. KOAH\u2019ta klinik KBT uygulamalar\u0131n\u0131n \u00f6n\u00fcndeki major s\u0131n\u0131rl\u0131l\u0131klar; iyonize radyasyon maruziyeti, BT cihaz\u0131 ve hasta de\u011fi\u015fkenli\u011fi, manuel post-prosessing sebebiyle kayda de\u011fer kalite kontrol\u00fcd\u00fcr(and significant quality control due to manual post-processing.). KBT, \u0130AH ilerleyi\u015finin takibinde ay\u0131rt edici parankimal dansitenin incelenmesi ve bu bulgular\u0131n dansite histogram\u0131 \u015feklinde \u00e7izimi ile hastal\u0131\u011f\u0131n ilerleyi\u015finin takibinde \u00f6nemli bir rol oynayabilir. Bu objektif veri, kalitatif de\u011ferlendirmelerle birlikte; y\u00fcksek morbiditeye sahip bir hasta pop\u00fclasyonunda hastal\u0131k ilerleyi\u015finin ve tedaviye yan\u0131t\u0131n takibinde klinisyenlere daha hassas bir y\u00f6ntemin sa\u011flanmas\u0131 i\u00e7in kullan\u0131labilir. Bu kantitatif algoritmalar\u0131n sa\u011flam klinik uygulamalarda yer edinebilmesi i\u00e7in hala kayda de\u011fer geli\u015fmelere ihtiya\u00e7 olsa da bu geli\u015fmelerin heyecan verici oldu\u011fu ink\u00e2r edilemez.<\/p>\n

\u00a0<\/strong><\/p>\n

COVID<\/strong><\/span><\/h3>\n

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COVID-19\u2019da Pulmoner Tromboemboli: Ven\u00f6z Tromboemboli mi Arteriyel Tromboz mu?<\/span><\/strong><\/h5>\n

Ferrari, F., et al. (2020). \u201cPulmonary Thromboembolism in COVID-19: Venous Thromboembolism or Arterial Thrombosis?\u201d RSNA<\/p>\n

Cavagna, E., et al. (2020). “Pulmonary Thromboembolism in COVID-19: Venous Thromboembolism or Arterial Thrombosis?” Radiology: Cardiothoracic Imaging<\/u> 2<\/strong>(4): e200289.<\/p>\n

Key Words: COVID-19, Pulmonary Embolus, Thromboembolism<\/p>\n

https:\/\/pubs.rsna.org\/doi\/10.1148\/ryct.2020200289<\/a><\/p>\n

\u0130talyan radyologlardan Cavagna ve ark. a\u011f\u0131r hastalarda yeni koronavir\u00fcs ve tromboemboli ili\u015fkisine yeni bir bak\u0131\u015f a\u00e7\u0131s\u0131 sunuyor. COVID-19 hastalar\u0131n\u0131n daha y\u00fcksek pulmoner arteriyal emboli (PE) oran\u0131na sahip oldu\u011fu biliniyor ve bu durum kritik hastalar\u0131n y\u00fcksek s\u0131kl\u0131\u011f\u0131 g\u00f6z \u00f6n\u00fcnde bulunduruldu\u011funda endi\u015fe verici oluyor. Bu retrospektif \u00e7al\u0131\u015fmaya, PE\u2019yi d\u00fc\u015f\u00fcnd\u00fcren semptomlar\u0131 olan ve PE ekartasyonu i\u00e7in Pulmoner BT anjiyografi (BTPA) \u00e7ekilmi\u015f 109 COVID-19 hastas\u0131 al\u0131nd\u0131. Akci\u011ferin 5 lobunun tahmini tutulumunu hesaplamak i\u00e7in hastalara semi-kantitatif bir skor (0-5) atand\u0131. Y\u00fcksek BT lezyon skoruna ve anormal laboratuvar de\u011ferlerine (LDH, CRP, d-dimer vs) sahip hastalarda daha y\u00fcksek oranda pulmoner tromboemboli oldu\u011fu g\u00f6zlemlendi. Bu hiperkoagulabilite fenomeni tercihen COVID\u2019den en \u00e7ok etkilenen akci\u011fer parankim alanlar\u0131n\u0131n \u00a0segmenter (%90.2) ve sub-segmenter (%61) arterlerinde oluyordu. Ara\u015ft\u0131rmac\u0131lar bunlar\u0131n emboli de\u011fil, lokal akci\u011fer inflamasyonuna ve hiperkoagulabilite durumuna sekonder pulmoner arter trombozlar\u0131 oldu\u011funu tahmin ediyor. \u00c7al\u0131\u015fma bu \u00e7\u0131kar\u0131m\u0131 kesin bir \u015fekilde onaylayacak g\u00fc\u00e7te olmasa da ara\u015ft\u0131rmac\u0131lar bu radikal iddiay\u0131 destekleyen di\u011fer baz\u0131 \u00e7al\u0131\u015fmalar\u0131 kan\u0131t g\u00f6steriyor. Gelecek \u00e7al\u0131\u015fmalar\u0131n daha fazla veriyle bu hipotezi do\u011frulayaca\u011f\u0131 \u00fcmit ediliyor.<\/p>\n

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Kuzey Amerika Radyoloji Toplulu\u011fu(RSNA) COVID-19 Pn\u00f6monisi Raporlamas\u0131 i\u00e7in Toraks BT S\u0131n\u0131fland\u0131rma Sistemi: G\u00f6zlemciler Aras\u0131 De\u011fi\u015fkenlik ve RT-PCR ile Korelasyon<\/span><\/strong><\/h5>\n

Jaegere, T. M. H. d., et al. (2020). “Radiological Society of North America Chest CT Classification System for Reporting COVID-19 Pneumonia: Interobserver Variability and Correlation with RT-PCR.” Radiology: Cardiothoracic Imaging<\/u> 2<\/strong>(3): e200213.<\/p>\n

Anahtar Kelimeler: COVID-19, RSNA S\u0131n\u0131fland\u0131rmas\u0131, CORADS<\/p>\n

https:\/\/pubs.rsna.org\/doi\/10.1148\/ryct.2020200213<\/a><\/p>\n

Hollanda\u2019daki Zuyderland T\u0131p Merkezinden radyologlar, RSNA COVID-19 Toraks BT S\u0131n\u0131fland\u0131rma Sistemi ve COVID-19 Raporlama ve Veri Sistemi (CO-RADS) kullan\u0131m\u0131 s\u0131ras\u0131nda radyologlar\u0131n g\u00f6zlemciler aras\u0131 uyumunu incelemek amac\u0131yla bu sistemlerin sonu\u00e7lar\u0131 ile hastalar\u0131n PCR sonu\u00e7lar\u0131n\u0131 kar\u015f\u0131la\u015ft\u0131rd\u0131. 96 hastan\u0131n toraks BT\u2019si ile yap\u0131lan retrospektif incelemede 2 radyoloji uzman\u0131 ve bir be\u015finci y\u0131l uzmanl\u0131km \u00f6\u011frencisi, g\u00f6zlemciler aras\u0131 de\u011fi\u015fkenli\u011fi test etmek i\u00e7in RSNA toraks BT s\u0131n\u0131fland\u0131rma sistemini ve CORADS s\u0131n\u0131flandrma sistemini kulland\u0131lar. Radyologlardan BT bulgular\u0131n\u0131 COVID varl\u0131\u011f\u0131 i\u00e7in \u201ctipik\u201d, \u201catipik\u201d, \u201cbelirsiz\u201d veya \u201cnegatif\u201d olarak s\u0131n\u0131fland\u0131rmalar\u0131 istendi. Uzmanlar aras\u0131nda \u00f6nemli d\u00fczeyde g\u00f6zlemciler aras\u0131 uyum, uzmanlar ile uzmanl\u0131k \u00f6\u011frencisi aras\u0131nda ise ortalama d\u00fczeyde g\u00f6zlemciler aras\u0131 uyum oldu\u011funu buldular. Bu veriler daha sonra hastalar\u0131n RT-PCR COVID test sonu\u00e7lar\u0131 ile k\u0131yasland\u0131. Testi pozitif olan 45 hasta, BT bulgular\u0131na g\u00f6re s\u0131n\u0131flara ayr\u0131ld\u0131 ve pozitiflik oran\u0131n\u0131n \u201cTipik\u201d grupta %76.9-96.6, \u201cBelirsiz\u201d grupta %51.2-64.1, \u201cAtipik\u201d grupta %2.8-5.3 ve \u201cNegatif\u201d grupta %20-25 oldu\u011fu belirtildi. \u201cAtipik\u201d grupta pozitiflik oran\u0131n\u0131n d\u00fc\u015f\u00fck olmas\u0131, di\u011fer akci\u011fer hastal\u0131klar\u0131yla ili\u015fkilendiren bulgular\u0131n varl\u0131\u011f\u0131 ile a\u00e7\u0131klan\u0131yor. \u201cNegatif\u201d grupta ilgin\u00e7 bir \u015fekilde bu kadar y\u00fcksek bir oran \u00e7\u0131km\u0131\u015f olmas\u0131 ise endi\u015felendirici. Ara\u015ft\u0131rmac\u0131lar bu \u00a0grupta COVID-19 ile ili\u015fkili belirtileri hen\u00fcz g\u00f6stermeyen hastalar\u0131n var olabilece\u011fini belirtiyor. Ara\u015ft\u0131rmac\u0131lar CO-RADs s\u0131n\u0131fland\u0131rma sistemini de kulland\u0131lar ve g\u00f6zlemciler aras\u0131 uyumun benzer oldu\u011funu g\u00f6rd\u00fcler. Semptomatik hastalar\u0131n hastal\u0131\u011f\u0131n farkl\u0131 evrelerinde olabilece\u011fi g\u00f6z \u00f6n\u00fcne al\u0131nd\u0131\u011f\u0131nda, BT\u2019nin koronavir\u00fcs taramas\u0131ndaki rol\u00fcn\u00fcn s\u0131n\u0131rl\u0131 oldu\u011fu anla\u015f\u0131lmaktad\u0131r. A\u011f\u0131r COVID-19 enfeksiyonlar\u0131 kayda de\u011fer t\u0131bbi mortalite ve morbidite ile birlikte oldu\u011fu i\u00e7in BT hala hasta y\u00f6netiminde de\u011ferli bir rol oynuyor.<\/p>\n

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Kardiyak<\/span><\/strong><\/h3>\n

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Sa\u011fl\u0131kl\u0131 Kat\u0131l\u0131mc\u0131larda Egzersiz S\u0131ras\u0131nda Kardiyovask\u00fcler D\u00f6rt Boyutlu Ak\u0131m MRG\u2019nin Uygulanabilirli\u011fi<\/span><\/strong><\/h5>\n

MacDonald, J.A., et al. (2020). \u201cFeasibility of Cardiovascular Four-dimensional Flow MRI during Exercise in Healthy Participants.\u201d Radiology: Cardiothoracic Imaging 2(3):e190033<\/p>\n

Keywords: Cardiac MRI, 4D-Flow<\/p>\n

https:\/\/pubs.rsna.org\/doi\/10.1148\/ryct.2020190033<\/a><\/p>\n

Macdonald ve ark. serbest nefes 4-D ak\u0131m Manyetik Rezonans G\u00f6r\u00fcnt\u00fcleme (MRG)’nin egzersiz s\u0131ras\u0131nda etkinli\u011fini incelemi\u015ftir. Sa\u011fl\u0131kl\u0131 10 ki\u015filik grupta, MRG uyumlu merdiven basamak cihaz\u0131 \u00fczerinde, gantri i\u00e7erisinde s\u0131rt \u00fcst\u00fc (supin) pozisyonda kalacak \u015fekilde standart egzersiz protokol\u00fc uygulan\u0131p dinlenme ve egzersiz s\u0131ras\u0131nda kardiyak MRG ger\u00e7ekle\u015ftirildi. \u00c7al\u0131\u015fmada nefes artefakt\u0131n\u0131 ve kalp hareket artefakt\u0131n\u0131\u00a0 engellemek i\u00e7in solunum tetikleme ve EKG tetikleme kullan\u0131ld\u0131. Ara\u015ft\u0131rmac\u0131lar dinlenme ve egzersiz s\u0131ras\u0131nda sistol ve diyastolde at\u0131m hacmini ve ventrik\u00fcler kinetik enerjiyi(VKE) de\u011ferlendirmek i\u00e7in 4-D ak\u0131m protokol\u00fc kulland\u0131. Kardiyak debinin tahmin edildi\u011fi \u00fczere egzersiz s\u0131ras\u0131nda dinlenmeye g\u00f6re artt\u0131\u011f\u0131 saptand\u0131 fakat ilgin\u00e7 bir \u015fekilde, sa\u011f ventrik\u00fcler kinetik enerji dinlenme durumuna g\u00f6re \u00f6nemli \u00f6l\u00e7\u00fcde artarken, sol ventrik\u00fcler kinetik enerji ve at\u0131m hacminde \u00f6nemli bir art\u0131\u015f saptanmad\u0131. At\u0131m hacminin s\u0131rt\u00fcst\u00fc egzersiz an\u0131nda belirgin art\u0131\u015f g\u00f6stermedi\u011fi daha \u00f6nce kan\u0131tlanm\u0131\u015ft\u0131. Sa\u011f VKE\u2019nin sistol s\u0131ras\u0131nda sol VKE\u2019ye g\u00f6re istatistiksel olarak anlaml\u0131 bir \u015fekilde daha fazla oldu\u011fu, diyastol an\u0131nda ise \u00a0aralar\u0131nda fark olmad\u0131\u011f\u0131 g\u00f6sterildi. Yap\u0131lan incelemede sol VKE\u2019de belirgin de\u011fi\u015fim g\u00f6zlenmedi\u011finden, 4-D stres MRG uygulamas\u0131n\u0131n kullan\u0131m\u0131 s\u0131n\u0131rl\u0131 olabilir. Di\u011fer taraftan, 4-D stres MRG sa\u011f VKE de\u011fi\u015fimini belirgin bir \u015fekilde g\u00f6sterdi\u011fi i\u00e7in sa\u011f kalp hastal\u0131klar\u0131n\u0131n de\u011ferlendirilmesinde iyi bir ara\u00e7 olabilir.<\/p>\n

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Makine \u00d6\u011frenimi<\/span><\/strong><\/h3>\n

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Akut G\u00f6\u011f\u00fcs A\u011fr\u0131s\u0131nda Triple Rule-Out Bilgisayarl\u0131 Tomografi (BT) Anjiyografiye Uygulanan Makine \u00d6\u011frenimine Dayal\u0131 Koroner BT Fraksiyonel Ak\u0131m Rezervinin \u00d6nemi<\/strong><\/span><\/h5>\n

Schoepf, U.J., et al. (2020) \u201cValue of Machine Learning-based Coronary CT Fractional Flow Reserve Applied to Triple-Rule-Out CT Angiography in Acute Chest Pain.\u201d Radiology: Cardiothoracic Imaging 2(3):e190137<\/p>\n

Keywords: Machine Learning, Triple Rule Out (TRO), Fractional Flow Reserve (FFR), CT Coronary Angiography<\/p>\n

https:\/\/pubs.rsna.org\/doi\/10.1148\/ryct.2020190137<\/a><\/p>\n

South Carolina T\u0131p \u00dcniversitesinden radyologlar, Triple Rule-Out (TRO) \u00a0BT anjiyografi s\u0131ras\u0131nda fraksiyonel ak\u0131m rezervi (FFR) tahmininde makine \u00f6\u011frenimi i\u00e7in bir uygulama tasarlad\u0131lar. Schoepf ve ark. 159 hastay\u0131 kapsayan bu retrospektif incelemede d\u00fc\u015f\u00fck ve orta derece kardiyak hastal\u0131k riski olan hastalarda 159 adet Triple Rule-Out BT Anjiyografiyi inceledi. Fraksiyonel ak\u0131m rezervi (FFR) derin \u00f6\u011frenme algoritmas\u0131 ile hesapland\u0131. \u00a0FFR\u2019nin, ciddi obstr\u00fcktif koroner hastal\u0131k de\u011ferlendirmesinde y\u00fcksek tan\u0131sal do\u011frulu\u011fu mevcuttur ve koroner arter hastal\u0131klar\u0131n\u0131 (KAH) de\u011ferlendirmek i\u00e7in kullan\u0131lmaktad\u0131r. \u00c7al\u0131\u015fma grubu, hemodinamik a\u00e7\u0131dan anlaml\u0131 stenozu bulunan hastalar\u0131 tan\u0131mlamak i\u00e7in FFR hesaplamas\u0131n\u0131 kullanmaya \u00e7al\u0131\u015fm\u0131\u015f ve koroner m\u00fcdahale veya maj\u00f6r advers kardiyovask\u00fcler olaylar\u0131 (MAKO) son nokta olarak kullanm\u0131\u015ft\u0131r. FFR de\u011ferinin 0.8\u2019den k\u00fc\u00e7\u00fck olmas\u0131 hemodinamik a\u00e7\u0131dan belirgin stenoz olarak de\u011ferlendirildi. Triple Rule-Out BT anjiyografi yap\u0131lan hastalar\u0131n %55’inde belirgin stenoz (> %50), %5’inde \u015fiddetli stenoz (> %70) vard\u0131. \u00c7al\u0131\u015fmaya kat\u0131lanlar\u0131n % 52’sinde FFR ve Triple Rule-Out BT anjiyografi birbiriyle uyumlu bulgular sergilemi\u015ftir. Uyumlu bulgular g\u00f6sterenlerin % 55’inde belirgin koroner stenoz ve FFR <0.8 olarak saptanm\u0131\u015f, % 45’inde FFR> 0.8 iken Triple Rule-Out BT anjiyografide anlaml\u0131 stenoz saptanmam\u0131\u015ft\u0131r. Grafikler incelendi\u011finde, koroner revask\u00fclarizasyon veya anlaml\u0131 major advers kardiyovask\u00fcler olay\u0131n g\u00f6r\u00fcnt\u00fcleme yap\u0131lan hastalar\u0131n %27.5 ‘inde meydana geldi\u011fi ve FFR de\u011feri <0.8 olanlar\u0131n koroner revask\u00fclarizasyon veya major advers kardiyovask\u00fcler olay ya\u015fama ihtimallerinin akranlar\u0131na g\u00f6re anlaml\u0131 derecede daha y\u00fcksek oldu\u011fu kaydedildi. Yazarlar, 0.8\u2019den k\u00fc\u00e7\u00fck FFR de\u011ferinin y\u00fcksek risk alt\u0131nda olan ve ileri test yap\u0131lmas\u0131 gereken hastalar\u0131 saptamak i\u00e7in yararl\u0131 olabilece\u011fini belirtmektedir. 4 hastada FFR 0.8\u2019den b\u00fcy\u00fck olmas\u0131na ra\u011fmen koroner revask\u00fclarizasyon yap\u0131ld\u0131\u011f\u0131 kaydedildi, bu nedenle bu 4 hastaya FFR de\u011feri ileri tetkik i\u00e7in bir cut-off olarak kullan\u0131lm\u0131\u015f olsayd\u0131 terap\u00f6tik m\u00fcdahale atlanm\u0131\u015f olabilirdi. Ayr\u0131ca hem TRO BT anjiyografi hem SPECT uygulanan hastalar\u0131n % 54’\u00fcnde ve hem TRO BT anjiyografi hem stres ekokardiyografi yap\u0131lan hastalar\u0131n % 48’inde uyumsuz bulgular oldu\u011fu; bu hastalar\u0131n 3’\u00fcnde FFR> 0.8 ve stres SPECT \/ BT’de pozitif sonu\u00e7 oldu\u011fu belirlendi. Bu \u00e7al\u0131\u015fma retrospektif oldu\u011fundan ve 0.8\u2019den k\u00fc\u00e7\u00fck FFR de\u011feri referans al\u0131narak herhangi bir tedavi karar\u0131 verilmedi\u011finden, ka\u00e7 tane hastan\u0131n olumsuz sonu\u00e7 alaca\u011f\u0131 belirsizdir. Sonu\u00e7 olarak d\u00fc\u015f\u00fck ve orta riskli koroner arter hastalar\u0131nda TRO BT anjiyografiye FFR eklenmesi, akut g\u00f6\u011f\u00fcs a\u011fr\u0131s\u0131n\u0131n de\u011ferlendirilmesine katk\u0131 sa\u011flayabilir.<\/p>\n

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Makine \u00d6\u011frenimi Kullanarak Tip B Aort Diseksiyonunda BT Tabanl\u0131 Ger\u00e7ek ve Yalanc\u0131 L\u00fcmen Segmentasyonu<\/strong><\/span><\/h5>\n

Hahn, L.D., et al. \u201cCT-based True- and False-Lumen Segmentation in Type B Aortic Dissection Using Machine Learning.\u201d Radiology: Cardiothoracic Imaging 2020; 2(3): e190179<\/p>\n

Keywords: Aortic Dissection, Machine Learning<\/p>\n

https:\/\/pubs.rsna.org\/doi\/10.1148\/ryct.2020190179<\/a><\/p>\n

Stanford’daki ara\u015ft\u0131rmac\u0131lar, komplike olmayan Tip B aort diseksiyonunun multiplanar rekonstr\u00fcksiyonlar\u0131n\u0131 (MPR) otomatik olarak olu\u015fturmak ve daha sonra ger\u00e7ek (GL) ve yalanc\u0131 l\u00fcmenleri (YL) segmentlere ay\u0131rmak i\u00e7in a\u015famal\u0131 olarak evri\u015fimli sinir a\u011flar\u0131 (ESS) kulland\u0131lar.<\/p>\n

Hahn, L.D. ve arkada\u015flar\u0131, k\u00fcm\u00fclatif 153 aort BT anjiyogram\u0131 olan 45 hastay\u0131 retrospektif olarak inceledi. Hastalar rastgele bir \u00e7al\u0131\u015fma setine, validasyon setine ve bir test setine atand\u0131; bunlar daha sonra ESS\u2019in kademeli segmentasyonuna beslendi. Segmentasyon i\u00e7in ad\u0131mlar; aortu aksiyal d\u00fczlemde arka plandan tan\u0131mlamay\u0131, intraaortik ortalama dansiteyi kullanarak aort merkezini belirlemeyi, merkez \u00e7izgisine ortagonal bir d\u00fczlemde MPR olu\u015fturmay\u0131, ard\u0131ndan aort diseksiyonunda GL ve YL segmentasyonunu ve son olarak da MPR\u2019yi \u00a0tekrar aksiyel d\u00fczleme d\u00f6n\u00fc\u015ft\u00fcrmeyi i\u00e7eriyordu. Otomatik segmentasyon, kardiyotorasik radyoloji uzmanlar\u0131 taraf\u0131ndan i\u00e7 kontrollerle (test seti) ve manuel aort segmentasyonu ile kar\u015f\u0131la\u015ft\u0131r\u0131ld\u0131. Bu \u00e7al\u0131\u015fman\u0131n son noktalar\u0131 aras\u0131nda zar benzerlik katsay\u0131s\u0131 (S\u00f8rensen\u2013Dice coefficient – DSC) ve mm cinsinden kenarlar aras\u0131ndaki ortalama \u00d6klid mesafesi vard\u0131.<\/p>\n

Ara\u015ft\u0131rmac\u0131lar, validasyon seti ile test seti aras\u0131nda anlaml\u0131 bir fark olmad\u0131\u011f\u0131n\u0131, ortalama DSC’nin 0.873-0.9 aras\u0131nda de\u011fi\u015fti\u011fini ve bunlar\u0131n segmentasyonu y\u00fcksek do\u011frulukta g\u00f6sterdi\u011fini buldular. Ortalama \u00d6klid mesafesi 3 mm\u2019den k\u00fc\u00e7\u00fckt\u00fc. \u00a0Otomatik segmentasyon ayn\u0131 zamanda GL, YL ve aortun t\u00fcm uzunluklar\u0131 boyunca \u00e7ap grafikleri olu\u015fturarak radyologlara vol\u00fcmleri kantitatif olarak de\u011ferlendirme imkan\u0131 verdi. Aort \u00e7ap\u0131, m\u00fcdahale gerektiren operasyonlarda karar k\u0131lmak i\u00e7in kullan\u0131ld\u0131\u011f\u0131ndan rutin kullan\u0131m i\u00e7in uygulanabilirli\u011fi olduk\u00e7a fazlad\u0131r. Ger\u00e7ek ve yalanc\u0131 l\u00fcmen ayr\u0131m\u0131n\u0131n hataya a\u00e7\u0131k olmas\u0131 (yak\u0131n d\u00f6nem \u00e7al\u0131\u015fmalarda 3.8% olarak raporlanm\u0131\u015ft\u0131r) ve deneyim gerektirmesi nedeniyle mevcut klinik uygulamalar halen olmas\u0131 gereken noktadan uzaktad\u0131r. Yazarlar, \u00e7al\u0131\u015fmadaki pop\u00fclasyonun test ve validasyon i\u00e7in de kullan\u0131ld\u0131\u011f\u0131ndan ESS’nin yanl\u0131l\u0131k getirmi\u015f olabilece\u011fini \u00f6ne s\u00fcrmektedir. Veri setleri ve hasta pop\u00fclasyonu b\u00fcy\u00fcd\u00fck\u00e7e ESS, daha geni\u015f diseksiyon \u00e7e\u015fitlili\u011fini ay\u0131rt edebilecektir. \u00c7o\u011fu yapay zeka algoritmas\u0131 BT cihazlar\u0131ndaki farkl\u0131l\u0131k, hastan\u0131n v\u00fccut habitusu\/anatomisi ve g\u00fcr\u00fclt\u00fc nedeniyle ger\u00e7ek hayat senaryolar\u0131nda zorlanma e\u011filimindedir. Yapay zeka geli\u015fmeye devam etti\u011finden, bu y\u00f6ntem klinisyenler i\u00e7in ileride kompleks olmayan tip B aort diseksiyonu g\u00f6r\u00fcnt\u00fclemesinde g\u00fcvenilir bir kaynak olacakt\u0131r.<\/p>\n","protected":false},"excerpt":{"rendered":"Toraks   Kantitatif Bilgisayarl\u0131 Tomografik G\u00f6r\u00fcnt\u00fclemenin Kronik Obstr\u00fcktif Akci\u011fer Hastal\u0131\u011f\u0131n\u0131 ve Fibrotik \u0130nterstisyel Akci\u011fer Hastal\u0131\u011f\u0131n\u0131 […]","protected":false},"author":3,"featured_media":7182,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0,"footnotes":""},"categories":[209],"tags":[],"class_list":["post-7242","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized-tr"],"yoast_head":"\nWhat's new in Cardiothoracic Imaging (Turkish) - 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