dual-phase-cone-beam-computed-tomography-to-see-reach-treat

Dual-phase Cone-beam Computed Tomography to See, Reach, and Treat Hepatocellular Carcinoma during Drug-eluting Beads Transarterial Chemo-embolization

The advent of cone-beam computed tomography (CBCT) in the angiography suite has been revolutionary in interventional radiology. CBCT offers 3 ...

Philips Healthcare

Minimally invasive interventions are often performed under fluoroscopic guidance. Drawbacks of fluoroscopic guidance are the fact that the presented images are 2D projections and that both the patient and the clinician are exposed to radiation. Image-guided navigation using pre-interventionally acquired 3D MR or CT data is an alternative. However, this often requires invasive anatomical landmark-based, marker-based or surface-based image-to-patient registration. In this paper, a coupling between an image-guided navigation system and an intraoperative C-arm X-ray device with 3D imaging capabilities (3D rotational X-ray (3DRX) system) that enables direct navigation without invasive image-to-patient registration on 3DRX volumes, is described and evaluated. The coupling is established in a one-time preoperative calibration procedure. The individual steps in the registration procedure are explained and evaluated. The acquired navigation accuracy using this coupling is approximately one millimeter.

Accuracy evaluation of direct navigation with an isocentric 3D rotational X-ray system.

Recently, three-dimensional (3D) rotational x-ray imaging has been combined with navigation technology, enabling direct 3D navigation for minimally invasive image guided interventions. In this study, phantom experiments are used to determine the accuracy of such a navigation set-up for a mobile C-arm with propeller motion. After calibration of the C-arm system, the accuracy is evaluated by pinpointing divots on a special-purpose phantom with known geometry. This evaluation is performed both with and without C-arm motion in between calibration and registration for navigation. The variation caused by each of the individual transformations in the calibration and registration process is also studied. The feasibility of direct navigation on 3D rotational x-ray images for functional endoscopic sinus surgery has been evaluated in a cadaver navigation experiment. Navigation accuracy was approximately 1.0 mm, which is sufficient for functional endoscopic sinus surgery. C-arm motion in between calibration and registration slightly degraded the registration accuracy by approximately 0.3 mm. Standard deviations of each of the transformations were in the range 0.15-0.31 mm. In the cadaver experiment, the navigation images were considered in good correspondence with the endoscopic images by an experienced ENT surgeon. Availability of 3D localization information provided by the navigation system was considered valuable by the ENT surgeon.

Direct navigation on 3D rotational x-ray data acquired with a mobile propeller C-arm: accuracy and application in functional endoscopic sinus surgery.

&#x4E09;&#x7EF4;&#x65CB;&#x8F6C; x &#x5C04;&#x7EBF;&#x6210;&#x50CF;&#x4EE5;&#x6765;&#xFF0C;&#x7528;&#x4E8E;&#x4E09;&#x7EF4;&#x65CB;&#x8F6C;&#x51A0;&#x72B6;&#x52A8;&#x8109;&#x6210;&#x50CF;&#x7684;&#x534F;&#x8BAE;&#x5DF2;&#x6210;&#x4E3A;&#x5E7F;&#x6CDB;&#x63D0;&#x4F9B;&#x4F8B;&#x884C;&#x7684;&#x4E34;&#x5E8A;&#x5B9E;&#x8DF5;&#x4E2D;&#x3002;Intra-procedural &#x5FC3;&#x810F;&#x6210;&#x50CF;&#x7684;&#x8BA1;&#x7B97;&#x673A;&#x65AD;&#x5C42;&#x626B;&#x63CF; (CT)-&#x50CF;&#x65F6;&#x88C5;&#x4E00;&#x76F4;&#x7279;&#x522B;&#x6709;&#x8BF4;&#x670D;&#x529B;&#x7684;&#x4E34;&#x5E8A;&#x5F00;&#x9500;&#x548C;&#x80FD;&#x529B;&#x662F;&#x89E3;&#x5256;&#x7279;&#x70B9;&#x65F6;&#x7684;&#x5E72;&#x9884;&#x51CF;&#x5C11;&#x3002;&#x6211;&#x4EEC;&#x4EE5;&#x524D;&#x4E0E;&#x77ED;&#x5BF9;&#x6BD4;&#x4E38;&#x6CE8;&#x5C04;&#x548C;&#x626B;&#x63CF;&#x65F6;&#x95F4;&#x7EA6; 4-10 s &#x7684;&#x63A8;&#x51FA;&#x4E86;&#x6210;&#x50CF;&#x7684;&#x5DE6;&#x7684;&#x5FC3;&#x623F;&#x548C;&#x80BA;&#x9759;&#x8109; (LAPVs) &#x4E34;&#x5E8A;&#x4E0A;&#x53EF;&#x884C;&#x7684;&#x529E;&#x6CD5;&#x3002;&#x6240;&#x4EA7;&#x751F;&#x7684;&#x6570;&#x636E;&#x671F;&#x95F4;&#x7535;&#x89E3;&#x5256;&#x6620;&#x5C04; (EAM) &#x548C;&#x4ECB;&#x5165;&#x6307;&#x5BFC;&#x5FC3;&#x623F;&#x98A4;&#x52A8; (&#x623F;&#x98A4;) &#x6D88;&#x878D;&#x8FC7;&#x7A0B;&#x4E2D;&#x7684;&#x6709;&#x8DB3;&#x591F;&#x7684;&#x56FE;&#x50CF;&#x8D28;&#x91CF;&#xFF0C;intra-procedural &#x4F7F;&#x7528;&#x3002;&#x5728;&#x6B64;&#x6587;&#x4EF6;&#x4E2D;&#xFF0C;&#x6211;&#x4EEC;&#x63D0;&#x51FA;&#x4E00;&#x79CD;&#x65B0;&#x6280;&#x672F;&#x4E8E;&#x4E00;&#x4F53;&#x7684;&#x5168;&#x81EA;&#x52A8;&#x5206;&#x5272;&#x7684;&#x623F;&#x98A4;&#x6D88;&#x878D;&#x5E72;&#x9884;&#x6307;&#x5BFC; LAPVs intra-procedural &#x8868;&#x9762;&#x4EE3;&#x3002;&#x5BF9;&#x6BD4;&#x589E;&#x5F3A;&#x65CB;&#x8F6C; x &#x5C04;&#x7EBF;&#x8840;&#x7BA1;&#x9020;&#x5F71; (&#x4E09;&#x7EF4; RA) &#x6536;&#x8D2D;&#x57FA;&#x4E8E;&#x7B5B;&#x9009;&#x80CC;&#x6295;&#x5F71;&#x91CD;&#x5EFA;&#x7ED3;&#x5408;&#x5141;&#x8BB8;&#x5BB9;&#x79EF; LAPV &#x89E3;&#x5256;&#x8FD1;&#x5B9E;&#x65F6;&#x7684;&#x5BA1;&#x8BAF;&#x3002;&#x4E00;&#x79CD;&#x57FA;&#x4E8E;&#x6A21;&#x578B;&#x7684;&#x81EA;&#x52A8;&#x5206;&#x5272;&#x7B97;&#x6CD5;&#x5141;&#x8BB8;&#x5FEB;&#x901F;&#x548C;&#x51C6;&#x786E; LAPV &#x7F51;&#x4EE3;&#x5C3D;&#x7BA1;&#x56FE;&#x50CF;&#x8D28;&#x91CF; &#xFF1B; &#x6240;&#x9762;&#x4E34;&#x7684;&#x6311;&#x6218;&#x76F8;&#x5BF9;&#x4E8E;&#x672F;&#x524D;&#x5FC3;&#x810F; CT/&#x5148;&#x751F;&#xFF0C;RA &#x4E09;&#x7EF4;&#x56FE;&#x50CF;&#x906D;&#x53D7;&#x66F4;&#x591A;&#x7684;&#x6587;&#x7269;&#x548C;&#x51CF;&#x5C11;&#x7684;&#x566A;&#x97F3;&#x4FE1;&#x53F7;&#x3002;&#x6211;&#x4EEC;&#x901A;&#x8FC7;&#x6BD4;&#x8F83; 1&#xFF09; &#x81EA;&#x52A8;&#x548C;&#x624B;&#x52A8;&#x5E02;&#x573A;&#x7EC6;&#x5206;&#x7684; intra-procedural &#x4E09;&#x7EF4; RA &#x6570;&#x636E;&#x9A8C;&#x8BC1;&#x6211;&#x4EEC;&#x7684;&#x7EFC;&#x5408;&#x7684;&#x65B9;&#x6CD5;&#xFF0C;2&#xFF09; &#x81EA;&#x52A8;&#x5E02;&#x573A;&#x7EC6;&#x5206;&#x7684; intra-procedural &#x4E09;&#x7EF4; RA &#x548C;&#x672F;&#x524D; CT/&#x5148;&#x751F;&#x6570;&#x636E;&#x4EE5;&#x53CA; 3&#xFF09; intra-procedural EAM &#x70B9;&#x4E91;&#x6570;&#x636E;&#x4E0E;&#x81EA;&#x52A8;&#x5E02;&#x573A;&#x7EC6;&#x5206;&#x7684;&#x4E09;&#x7EF4; RA &#x548C; CT/&#x5148;&#x751F;&#x6570;&#x636E;&#x3002;&#x6211;&#x4EEC;&#x9A8C;&#x8BC1;&#x7ED3;&#x679C;&#x81EA;&#x52A8;&#x5206;&#x6BB5;&#x5E73;&#x5747;&#x5206;&#x5272;&#x9519;&#x8BEF; 1&#xFF09; &#x7EA6; 1.3 m m &#x76F8;&#x6BD4;&#x624B;&#x52A8;&#x4E0E;&#x81EA;&#x52A8;&#x5206;&#x5272;&#x7684;&#x672F;&#x524D; CT/&#x5148;&#x751F;&#x6570;&#x636E;&#x548C; 3) &#x7EA6; 2.1 m m 2) &#x5927;&#x7EA6; 2.3 &#x6BEB;&#x7C73;&#x76F8;&#x6BD4;&#x7684;&#x4E09;&#x7EF4; RA &#x5E02;&#x573A;&#x7EC6;&#x5206;&#x76F8;&#x6BD4;&#x6CE8;&#x518C; intra-procedural EAM &#x70B9;&#x4E91; intra-procedural &#x4E09;&#x7EF4; RA &#x6570;&#x636E;&#x663E;&#x793A;&#x3002;&#x6574;&#x4F53;&#x7684;&#x5B9E;&#x9A8C;&#x8868;&#x660E; LAPV &#x66F2;&#x9762;&#x53EF;&#x4EE5;&#x5C06;&#x81EA;&#x52A8;&#x5206;&#x6BB5; intra-procedurally &#x4ECE;&#x4E09;&#x7EF4; RA &#x6570;&#x636E;&#x7684;&#x53EF;&#x6BD4;&#x8D28;&#x91CF;&#x4E0E;&#x672F;&#x524D; CT/&#x5148;&#x751F;&#x4ECE;&#x6D3E;&#x751F;&#x7684;&#x7F51;&#x683C;&#x3002;

&#x623F;&#x98A4;&#x6D88;&#x878D;&#x8FC7;&#x7A0B;&#x4E2D;&#x89E3;&#x5256; intra-procedural &#x8868;&#x9762;&#x751F;&#x6210;&#x7684;&#x65CB;&#x8F6C; x &#x5C04;&#x7EBF;&#x56FE;&#x50CF;&#x7684;&#x81EA;&#x52A8;&#x5206;&#x5272;&#x3002;

Since the introduction of 3-D rotational X-ray imaging, protocols for 3-D rotational coronary artery imaging have become widely available in routine clinical practice. Intra-procedural cardiac imaging in a computed tomography (CT)-like fashion has been particularly compelling due to the reduction of clinical overhead and ability to characterize anatomy at the time of intervention. We previously introduced a clinically feasible approach for imaging the left atrium and pulmonary veins (LAPVs) with short contrast bolus injections and scan times of approximately 4 -10 s. The resulting data have sufficient image quality for intra-procedural use during electro-anatomic mapping (EAM) and interventional guidance in atrial fibrillation (AF) ablation procedures. In this paper, we present a novel technique to intra-procedural surface generation which integrates fully-automated segmentation of the LAPVs for guidance in AF ablation interventions. Contrast-enhanced rotational X-ray angiography (3-D RA) acquisitions in combination with filtered-back-projection-based reconstruction allows for volumetric interrogation of LAPV anatomy in near-real-time. An automatic model-based segmentation algorithm allows for fast and accurate LAPV mesh generation despite the challenges posed by image quality; relative to pre-procedural cardiac CT/MR, 3-D RA images suffer from more artifacts and reduced signal-to-noise. We validate our integrated method by comparing 1) automatic and manual segmentations of intra-procedural 3-D RA data, 2) automatic segmentations of intra-procedural 3-D RA and pre-procedural CT/MR data, and 3) intra-procedural EAM point cloud data with automatic segmentations of 3-D RA and CT/MR data. Our validation results for automatically segmented intra-procedural 3-D RA data show average segmentation errors of 1) approximately 1.3 mm compared with manual 3-D RA segmentations 2) approximately 2.3 mm compared with automatic segmentation of pre-procedural CT/MR data and 3) approximately 2.1 mm compared with registered intra-procedural EAM point clouds. The overall experiments indicate that LAPV surfaces can be automatically segmented intra-procedurally from 3-D RA data with comparable quality relative to meshes derived from pre-procedural CT/MR.

Automatic segmentation of rotational x-ray images for anatomic intra-procedural surface generation in atrial fibrillation ablation procedures.

Depuis l&#x26;#39;introduction de la 3-D de rotation imagerie par rayons X, les protocoles pour le 3-D imagerie des art&#xE8;res coronaires de rotation sont devenues largement disponibles dans la pratique clinique de routine. Intra-proc&#xE9;durale imagerie cardiaque dans une tomodensitom&#xE9;trie (CT)-comme la mode a &#xE9;t&#xE9; particuli&#xE8;rement convaincante en raison de la r&#xE9;duction des frais g&#xE9;n&#xE9;raux clinique et la capacit&#xE9; &#xE0; caract&#xE9;riser l&#x26;#39;anatomie au moment de l&#x26;#39;intervention. Nous avons d&#xE9;j&#xE0; introduit une approche cliniquement faisable pour l&#x26;#39;imagerie de l&#x26;#39;oreillette gauche et les veines pulmonaires (LAPVs) avec de courtes injections bolus de contraste et de num&#xE9;riser fois d&#x26;#39;environ 4 -10 s. Les donn&#xE9;es qui en r&#xE9;sultent ont une qualit&#xE9; d&#x26;#39;image suffisante pour une utilisation intra-proc&#xE9;durale lors de l&#x26;#39;&#xE9;lectro-anatomique de cartographie (EAM) et l&#x26;#39;orientation interventionnelle dans la fibrillation auriculaire (FA) les proc&#xE9;dures d&#x26;#39;ablation. Dans cet article, nous pr&#xE9;sentons une nouvelle technique pour la g&#xE9;n&#xE9;ration de surface intra-proc&#xE9;durale qui int&#xE8;gre enti&#xE8;rement automatis&#xE9; de segmentation des LAPVs d&#x26;#39;orientation dans les interventions d&#x26;#39;ablation de FA. Rehaussement du contraste de rotation X-ray angiographie (3-D RA) les acquisitions, en combinaison avec filtr&#xE9; r&#xE9;tro-projection bas&#xE9;e sur la reconstruction permet d&#x26;#39;interrogation volum&#xE9;trique de l&#x26;#39;anatomie LAPV en quasi-temps r&#xE9;el. Un algorithme de segmentation automatique bas&#xE9;e sur un mod&#xE8;le permet de g&#xE9;n&#xE9;ration de maillage rapide et pr&#xE9;cise LAPV malgr&#xE9; les d&#xE9;fis pos&#xE9;s par la qualit&#xE9; d&#x26;#39;image; par rapport &#xE0; la proc&#xE9;dure de pr&#xE9;-cardiaque CT / MR, images 3-D RA souffrent de plus d&#x26;#39;artefacts et de la r&#xE9;duction du signal-bruit. Nous validons notre m&#xE9;thode int&#xE9;gr&#xE9;e en comparant 1) segmentations manuelles et automatiques du commerce intra-proc&#xE9;durale donn&#xE9;es 3-D RA, 2) la segmentation automatique de la proc&#xE9;dure intra-3-D RA et pr&#xE9;-proc&#xE9;dure CT / MR de donn&#xE9;es, et 3) intra-proc&#xE9;durale des donn&#xE9;es de nuages &#x200B;&#x200B;de points avec EAM segmentations automatiques de 3-D RA et CT / MR de donn&#xE9;es. Nos r&#xE9;sultats de la validation pour automatiquement segment&#xE9;es intra-proc&#xE9;durales donn&#xE9;es 3-D RA montrent les erreurs de segmentation moyenne de 1) d&#x26;#39;environ 1,3 mm par rapport &#xE0; manuelle 3-D segmentations RA 2) d&#x26;#39;environ 2,3 mm par rapport &#xE0; la segmentation automatique de pr&#xE9;-proc&#xE9;dure CT / MR de donn&#xE9;es et 3) d&#x26;#39;environ 2,1 mm par rapport &#xE0; la proc&#xE9;dure enregistr&#xE9;s intra-nuages &#x200B;&#x200B;de points EAM. Les exp&#xE9;riences indiquent que l&#x26;#39;ensemble des surfaces LAPV peuvent &#xEA;tre automatiquement segment&#xE9; intra-proc&#xE9;dure &#xE0; partir de 3-D des donn&#xE9;es RA avec une qualit&#xE9; comparable par rapport &#xE0; mailles d&#xE9;riv&#xE9;es de la pr&#xE9;-proc&#xE9;dure CT / MR.

Segmentation automatique de rotation X-ray Images pour la g&#xE9;n&#xE9;ration de surface anatomique intra-auriculaire de proc&#xE9;dure dans les proc&#xE9;dures ablation de la fibrillation

Seit der Einf&uuml;hrung der 3D Rotations R&ouml;ntgen-Bildgebung sind Protokolle f&uuml;r 3D Rotations koronare Bildgebung weithin verf&uuml;gbar in der klinischen Praxis Routine geworden. Intra-Procedural kardiale Bildgebung in einer Computertomographie (CT)-wie Mode besonders &uuml;berzeugende aufgrund der Verringerung der klinischen Overhead und F&auml;higkeit wurde, Anatomie zum Zeitpunkt der Intervention zu charakterisieren. Wir zuvor ein klinisch machbar Konzept f&uuml;r imaging, den linken Vorhof und die Lungenvenen (LAPVs) mit kurzen Kontrast Bolus Einspritzungen und Scan-Zeiten von ca. 4-10 s eingef&uuml;hrt. Die resultierenden Daten haben ausreichende Bildqualit&auml;t f&uuml;r den intra-procedural Einsatz bei Elektro-anatomische Zuordnung (EAM) und Interventionelle Anleitung bei Vorhofflimmern (AF) Ablation Verfahren. In diesem Beitrag pr&auml;sentieren wir eine neue Technik zu intra-procedural Oberfl&auml;che-Generation, die vollautomatische Segmentierung des LAPVs f&uuml;r die Ausrichtung der AF Ablation Interventionen integriert. Kontrast-verst&auml;rkte rotatorische R&ouml;ntgen Angiographie (3-d RA) &Uuml;bernahmen in Kombination mit gefiltert-R&uuml;cken-Projektion-basierte Wiederaufbau erm&ouml;glicht Volumetrische Verh&ouml;r LAPV Anatomie in nahe-Echtzeit. Eine automatische modellbasierte Segmentierungsalgorithmus erm&ouml;glicht schnelle und pr&auml;zise LAPV Gittergenerierung trotz der Herausforderungen durch die Bildqualit&auml;t; relativ pre-procedural kardiale CT/MR leiden RA 3D-Bilder mehr Artefakte und geringeren Signal-Rausch. Wir &uuml;berpr&uuml;fen unsere integrierten Methode 1) automatische und manuelle Segmentierungen intra-procedural 3-d-RA-Daten vergleichen, 2) automatische Segmentierungen 3) intra-procedural EAM, intra-procedural 3-d-RA und pre-procedural CT/MR-Daten zeigen, Wolke mit automatische Segmentierungen der 3-d RA und CT/MR-Daten. Unsere Validierungsergebnisse f&uuml;r segmentiert automatisch intra-procedural 3-d RA zeigen durchschnittliche Segmentierung St&ouml;rungen 1) ca. 1,3 mm im Vergleich zu manuellen 3-d RA Segmentierungen 2) ca. 2,3 mm mit automatische Segmentierung von pre-procedural CT/MR-Daten und 3) ca. 2,1 mm verglichen mit registrierten intra-procedural EAM-Punktwolken verglichen. Die allgemeine Experimente zeigen, dass LAPV Fl&auml;chen automatisch intra-procedurally von RA 3D-Daten mit vergleichbarer Qualit&auml;t relativ Maschen abgeleitet pre-procedural CT/MR segmentiert werden k&ouml;nnen.

Automatische Segmentierung rotatorischen R&ouml;ntgenbilder f&uuml;r anatomische intra-procedural Oberfl&auml;che-Generation in Vorhofflimmern Ablation Verfahren.

Poich&eacute; l&#39;introduzione di imaging a raggi x rotazione 3D, protocolli per l&#39;imaging 3D rotazionale dell&#39;arteria coronaria sono diventati largamente disponibili nella pratica clinica di routine. Intra-Procedural imaging cardiaco in una tomografia computerizzata (CT)-come la moda sono stata particolarmente interessante a causa della riduzione del sovraccarico clinica e la capacit&agrave; di caratterizzare anatomia al momento dell&#39;intervento. In precedenza abbiamo introdotto un approccio clinicamente fattibile per l&#39;imaging dell&#39;atrio sinistro e vene polmonari (LAPVs) con iniezioni di bolo di contrasto breve e tempi di scansione di circa 4 -10 s. I dati risultanti sono sufficienti qualit&agrave; dell&#39;immagine per uso intra-procedural durante la mappatura elettro-anatomico (EAM) e interventistica orientamento nelle procedure di ablazione di fibrillazione atriale (AF). In questo articolo, vi presentiamo una tecnica romanzo di intra-procedural generazione superficiale che integra completamente automatizzato di segmentazione di LAPVs per l&#39;orientamento in interventi di ablazione AF. Contrasto-enhanced rotazionale a raggi x angiografia (3-d RA) acquisizioni combinazione con ricostruzione filtrata-retro-proiezione-based permette di interrogatorio volumetrica dell&#39;anatomia LAPV in near-real-time. Un algoritmo di segmentazione automatica basata su modello permette per la generazione di mesh veloce e precisa con LAPV nonostante le sfide posato dalla qualit&agrave; dell&#39;immagine; rispetto al pre-procedural cardiac CT/MR, immagini 3D RA soffrono pi&ugrave; artefatti e ridotto segnale-rumore. Noi convalidare il nostro metodo integrato confrontando segmentazioni 1) automatiche e manuale di intra-procedural dati RA 3-d, 2) automatici segmentazioni di intra-procedural RA 3D e dati CT/Signor pre-procedural e 3) intra-procedural EAM punto dati cloud con segmentazioni automatici di RA 3D e CT/MR. Nostri risultati della convalida automaticamente segmentato intra-procedural 3D Visualizza dati RA segmentazione media errori 1) circa 1,3 mm rispetto al manuale 3D segmentazioni RA 2) circa 2,3 mm rispetto a segmentazione automatica di dati CT/MR pre-procedural e 3) circa 2,1 mm rispetto ai registrati nuvole di punti EAM intra-procedural. I generali esperimenti indicano che le superfici LAPV possono essere automaticamente segmentate intra-procedurally dai dati RA 3D con una qualit&agrave; paragonabile rispetto a maglie derivato da pre-procedural CT/MR.

Segmentazione automatica di immagini a raggi x rotazionale per la generazione di superficie intra-procedural anatomica nelle procedure di ablazione di fibrillazione atriale.

3-D&#x56DE;&#x8EE2;X&#x7DDA;&#x30A4;&#x30E1;&#x30FC;&#x30B8;&#x30F3;&#x30B0;&#x306E;&#x5C0E;&#x5165;&#x4EE5;&#x6765;&#x3001;3-D&#x56DE;&#x8EE2;&#x51A0;&#x52D5;&#x8108;&#x30A4;&#x30E1;&#x30FC;&#x30B8;&#x30F3;&#x30B0;&#x306E;&#x305F;&#x3081;&#x306E;&#x30D7;&#x30ED;&#x30C8;&#x30B3;&#x30EB;&#x306F;&#x3001;&#x65E5;&#x5E38;&#x8A3A;&#x7642;&#x3067;&#x5E83;&#x304F;&#x5229;&#x7528;&#x3067;&#x304D;&#x308B;&#x3088;&#x3046;&#x306B;&#x306A;&#x308A;&#x307E;&#x3057;&#x305F;&#x3002;&#x30B3;&#x30F3;&#x30D4;&#x30E5;&#x30FC;&#x30BF;&#x65AD;&#x5C64;&#x64AE;&#x5F71;&#xFF08;CT&#xFF09;&#x306E;&#x3088;&#x3046;&#x306A;&#x65B9;&#x6CD5;&#x3067;&#x30A4;&#x30F3;&#x30C8;&#x30E9;&#x624B;&#x7D9A;&#x304D;&#x5FC3;&#x81D3;&#x30A4;&#x30E1;&#x30FC;&#x30B8;&#x30F3;&#x30B0;&#x306F;&#x200B;&#x200B;&#x3001;&#x81E8;&#x5E8A;&#x306E;&#x30AA;&#x30FC;&#x30D0;&#x30FC;&#x30D8;&#x30C3;&#x30C9;&#x3068;&#x4ECB;&#x5165;&#x306E;&#x6642;&#x306B;&#x89E3;&#x5256;&#x5B66;&#x3092;&#x7279;&#x5FB4;&#x3065;&#x3051;&#x308B;&#x80FD;&#x529B;&#x306E;&#x4F4E;&#x4E0B;&#x306B;&#x8D77;&#x56E0;&#x3059;&#x308B;&#x3001;&#x7279;&#x306B;&#x9B45;&#x529B;&#x7684;&#x3067;&#x3042;&#x3063;&#x305F;&#x3002;&#x6211;&#x3005;&#x306F;&#x4EE5;&#x524D;&#x30A4;&#x30E1;&#x30FC;&#x30B8;&#x30F3;&#x30B0;&#x5DE6;&#x5FC3;&#x623F;&#x3068;&#x77ED;&#x3044;&#x30B3;&#x30F3;&#x30C8;&#x30E9;&#x30B9;&#x30C8;&#x30DC;&#x30FC;&#x30E9;&#x30B9;&#x6CE8;&#x5C04;&#x306B;&#x3088;&#x308B;&#x80BA;&#x9759;&#x8108;&#xFF08;LAPVs&#xFF09;&#x306E;&#x81E8;&#x5E8A;&#x7684;&#x306B;&#x5B9F;&#x73FE;&#x53EF;&#x80FD;&#x306A;&#x30A2;&#x30D7;&#x30ED;&#x30FC;&#x30C1;&#x3092;&#x5C0E;&#x5165;&#x3057;&#x3001;&#x7D04;4&#x301C;10&#x79D2;&#x306E;&#x6642;&#x9593;&#x3092;&#x30B9;&#x30AD;&#x30E3;&#x30F3;&#x3057;&#x307E;&#x3059;&#x3002;&#x5F97;&#x3089;&#x308C;&#x305F;&#x30C7;&#x30FC;&#x30BF;&#x306F;&#x3001;&#x30A4;&#x30F3;&#x30C8;&#x30E9;&#x624B;&#x7D9A;&#x304D;&#x96FB;&#x6C17;&#x89E3;&#x5256;&#x5B66;&#x7684;&#x30DE;&#x30C3;&#x30D4;&#x30F3;&#x30B0;&#x6642;&#x306B;&#x4F7F;&#x7528;&#xFF08;EAM&#xFF09;&#x3068;&#x5FC3;&#x623F;&#x7D30;&#x52D5;&#xFF08;AF&#xFF09;&#x30A2;&#x30D6;&#x30EC;&#x30FC;&#x30B7;&#x30E7;&#x30F3;&#x624B;&#x7D9A;&#x304D;&#x306E;&#x4ECB;&#x5165;&#x6307;&#x5C0E;&#x306E;&#x305F;&#x3081;&#x306B;&#x5341;&#x5206;&#x306A;&#x753B;&#x8CEA;&#x3092;&#x6301;&#x3063;&#x3066;&#x3044;&#x307E;&#x3059;&#x3002;&#x672C;&#x7A3F;&#x3067;&#x306F;&#x3001;AF&#x30A2;&#x30D6;&#x30EC;&#x30FC;&#x30B7;&#x30E7;&#x30F3;&#x306E;&#x4ECB;&#x5165;&#x306E;&#x6307;&#x91DD;&#x306E;&#x305F;&#x3081;&#x306B;LAPVs&#x306E;&#x5B8C;&#x5168;&#x81EA;&#x52D5;&#x62BD;&#x51FA;&#x3092;&#x7D71;&#x5408;&#x3057;&#x305F;&#x30A4;&#x30F3;&#x30C8;&#x30E9;&#x624B;&#x7D9A;&#x304D;&#x9762;&#x306E;&#x4E16;&#x4EE3;&#x306B;&#x65B0;&#x305F;&#x306A;&#x624B;&#x6CD5;&#x3092;&#x63D0;&#x793A;&#x3057;&#x307E;&#x3059;&#x3002;&#x30D5;&#x30A3;&#x30EB;&#x30BF;&#x30EA;&#x30F3;&#x30B0;&#x3055;&#x308C;&#x3001;&#x30D0;&#x30C3;&#x30AF;&#x30D7;&#x30ED;&#x30B8;&#x30A7;&#x30AF;&#x30B7;&#x30E7;&#x30F3;&#x3092;&#x30D9;&#x30FC;&#x30B9;&#x518D;&#x5EFA;&#x3068;&#x306E;&#x4F75;&#x7528;&#x3067;&#x9020;&#x5F71;&#x56DE;&#x8EE2;X&#x7DDA;&#x8840;&#x7BA1;&#x9020;&#x5F71;&#xFF08;3-D RA&#xFF09;&#x306E;&#x8CB7;&#x53CE;&#x306F;&#x3001;&#x307B;&#x307C;&#x30EA;&#x30A2;&#x30EB;&#x30BF;&#x30A4;&#x30E0;&#x3067;LAPV&#x89E3;&#x5256;&#x5B66;&#x306E;&#x4F53;&#x7A4D;&#x5C0B;&#x554F;&#x304C;&#x53EF;&#x80FD;&#x306B;&#x306A;&#x308A;&#x307E;&#x3059;&#x3002;&#x81EA;&#x52D5;&#x30E2;&#x30C7;&#x30EB;&#x30D9;&#x30FC;&#x30B9;&#x306E;&#x200B;&#x200B;&#x30BB;&#x30B0;&#x30E1;&#x30F3;&#x30C6;&#x30FC;&#x30B7;&#x30E7;&#x30F3;&#x30A2;&#x30EB;&#x30B4;&#x30EA;&#x30BA;&#x30E0;&#x306F;&#x3001;&#x753B;&#x8CEA;&#x306B;&#x3088;&#x3063;&#x3066;&#x3082;&#x305F;&#x3089;&#x3055;&#x308C;&#x308B;&#x8AB2;&#x984C;&#x306B;&#x3082;&#x304B;&#x304B;&#x308F;&#x3089;&#x305A;&#x3001;&#x8FC5;&#x901F;&#x304B;&#x3064;&#x6B63;&#x78BA;&#x306A;LAPV&#x30E1;&#x30C3;&#x30B7;&#x30E5;&#x751F;&#x6210;&#x304C;&#x53EF;&#x80FD;&#x306B;&#x306A;&#x308A;&#x3001;&#x4E8B;&#x524D;&#x306B;&#x624B;&#x7D9A;&#x304D;&#x578B;&#x5FC3;&#x81D3;CT / MR&#x306B;&#x6BD4;&#x3079;&#x3066;&#x3001;3-D RA&#x753B;&#x50CF;&#x306F;&#x591A;&#x304F;&#x306E;&#x5DE5;&#x82B8;&#x54C1;&#x3084;&#x4FE1;&#x53F7;&#x5BFE;&#x96D1;&#x97F3;&#x6BD4;&#x306E;&#x6E1B;&#x5C11;&#x306B;&#x82E6;&#x3057;&#x3093;&#x3067;&#x3044;&#x307E;&#x3059;&#x3002;&#x6211;&#x3005;&#x306F;&#x3001;1&#xFF09;&#x3092;&#x6BD4;&#x8F03;&#x3059;&#x308B;&#x3053;&#x3068;&#x306B;&#x3088;&#x3063;&#x3066;&#x3001;&#x30A4;&#x30F3;&#x30C8;&#x30E9;&#x624B;&#x7D9A;&#x304D;3-D RA&#x30C7;&#x30FC;&#x30BF;&#x3001;2&#xFF09;&#x5185;&#x306E;&#x624B;&#x7D9A;&#x304D;3-D RA&#x3068;&#x4E8B;&#x524D;&#x624B;&#x7D9A;&#x304D;&#x306E;CT / MR&#x30C7;&#x30FC;&#x30BF;&#x306E;&#x81EA;&#x52D5;&#x30BB;&#x30B0;&#x30E1;&#x30F3;&#x30C6;&#x30FC;&#x30B7;&#x30E7;&#x30F3;&#x306E;&#x81EA;&#x52D5;&#x3068;&#x624B;&#x52D5;&#x306E;&#x30BB;&#x30B0;&#x30E1;&#x30F3;&#x30C6;&#x30FC;&#x30B7;&#x30E7;&#x30F3;&#x3092;&#x5F53;&#x793E;&#x306E;&#x7D71;&#x5408;&#x65B9;&#x6CD5;&#x3092;&#x691C;&#x8A3C;&#x3001;3&#xFF09;&#x5185;&#x306E;&#x624B;&#x7D9A;&#x304D;3-D RA&#x3068;CT / MR&#x30C7;&#x30FC;&#x30BF;&#x306E;&#x81EA;&#x52D5;&#x30BB;&#x30B0;&#x30E1;&#x30F3;&#x30C6;&#x30FC;&#x30B7;&#x30E7;&#x30F3;&#x306B;&#x3088;&#x308B;EAM&#x306E;&#x70B9;&#x7FA4;&#x30C7;&#x30FC;&#x30BF;&#x3002;&#x81EA;&#x52D5;&#x7684;&#x306B;&#x30BB;&#x30B0;&#x30E1;&#x30F3;&#x30C8;&#x5316;&#x3055;&#x308C;&#x305F;&#x30A4;&#x30F3;&#x30C8;&#x30E9;&#x624B;&#x7D9A;&#x304D;3-D RA&#x30C7;&#x30FC;&#x30BF;&#x306E;&#x30D0;&#x30EA;&#x30C7;&#x30FC;&#x30B7;&#x30E7;&#x30F3;&#x306E;&#x7D50;&#x679C;&#x306B;&#x306F;&#x3001;&#x4E8B;&#x524D;&#x306B;&#x624B;&#x7D9A;&#x304D;CT / MR&#x30C7;&#x30FC;&#x30BF;&#x306E;&#x81EA;&#x52D5;&#x30BB;&#x30B0;&#x30E1;&#x30F3;&#x30C6;&#x30FC;&#x30B7;&#x30E7;&#x30F3;&#x3068;&#x6BD4;&#x3079;&#x3066;RA&#x306E;&#x30BB;&#x30B0;&#x30E1;&#x30F3;&#x30C6;&#x30FC;&#x30B7;&#x30E7;&#x30F3;2&#xFF09;&#x7D04;2.3&#x30DF;&#x30EA;&#x30E1;&#x30FC;&#x30C8;&#x30EB;&#x30DE;&#x30CB;&#x30E5;&#x30A2;&#x30EB;3-D&#x3068;&#x6BD4;&#x8F03;&#x3057;&#x3066;&#x7D04;1.3&#x30DF;&#x30EA;&#x30E1;&#x30FC;&#x30C8;&#x30EB;1&#xFF09;&#x306E;&#x5E73;&#x5747;&#x7684;&#x306A;&#x30BB;&#x30B0;&#x30E1;&#x30F3;&#x30C6;&#x30FC;&#x30B7;&#x30E7;&#x30F3;&#x306E;&#x30A8;&#x30E9;&#x30FC;&#x3092;&#x8868;&#x793A;&#x3057;&#x3001;&#x767B;&#x9332;&#x3055;&#x308C;&#x305F;&#x30A4;&#x30F3;&#x30C8;&#x30E9;&#x624B;&#x7D9A;&#x304D;EAM&#x30DD;&#x30A4;&#x30F3;&#x30C8;&#x96F2;&#x3068;&#x6BD4;&#x8F03;&#x3057;&#x3066;3&#xFF09;&#x7D04;2.1&#x30DF;&#x30EA;&#x30E1;&#x30FC;&#x30C8;&#x30EB;&#x3002;&#x5168;&#x4F53;&#x7684;&#x306A;&#x5B9F;&#x9A13;&#x306F;LAPV&#x8868;&#x9762;&#x306F;&#x3001;&#x4E8B;&#x524D;&#x624B;&#x7D9A;&#x304D;&#x578B;CT / MR&#x304B;&#x3089;&#x6D3E;&#x751F;&#x3057;&#x305F;&#x30E1;&#x30C3;&#x30B7;&#x30E5;&#x306E;&#x76F8;&#x5BFE;&#x5339;&#x6575;&#x3059;&#x308B;&#x54C1;&#x8CEA;&#x3067;3-D RA&#x30C7;&#x30FC;&#x30BF;&#x304B;&#x3089;&#x3001;&#x30A4;&#x30F3;&#x30C8;&#x30E9;&#x624B;&#x7D9A;&#x304D;&#x3092;&#x81EA;&#x52D5;&#x7684;&#x306B;&#x5206;&#x5272;&#x3059;&#x308B;&#x3053;&#x3068;&#x3092;&#x793A;&#x3057;&#x3066;&#x3044;&#x308B;&#x3002;

&#x5FC3;&#x623F;&#x7D30;&#x52D5;&#x30A2;&#x30D6;&#x30EC;&#x30FC;&#x30B7;&#x30E7;&#x30F3;&#x306E;&#x624B;&#x9806;&#x3067;&#x89E3;&#x5256;&#x30A4;&#x30F3;&#x30C8;&#x30E9;&#x624B;&#x7D9A;&#x304D;&#x9762;&#x751F;&#x6210;&#x306E;&#x305F;&#x3081;&#x306E;&#x56DE;&#x8EE2;X&#x7DDA;&#x753B;&#x50CF;&#x306E;&#x81EA;&#x52D5;&#x30BB;&#x30B0;&#x30E1;&#x30F3;&#x30C6;&#x30FC;&#x30B7;&#x30E7;&#x30F3;

&#xD68C;&#xC804; 3 &#xCC28;&#xC6D0; x &#xC120; &#xC601;&#xC0C1;&#xC758; &#xB3C4;&#xC785; &#xC774;&#xD6C4; 3 &#xCC28;&#xC6D0; &#xD68C;&#xC804; &#xAD00;&#xC0C1; &#xB3D9;&#xB9E5; &#xC774;&#xBBF8;&#xC9D5; &#xD504;&#xB85C;&#xD1A0;&#xCF5C; &#xC77C;&#xC0C1;&#xC801;&#xC778; &#xC784;&#xC0C1; &#xC5F0;&#xC2B5;&#xC5D0;&#xC11C; &#xB110;&#xB9AC; &#xC0AC;&#xC6A9; &#xB418;&#xACE0;&#xC788;&#xB2E4;. &#xACC4;&#xC0B0; &#xB41C; &#xB2E8;&#xCE35; &#xCD2C;&#xC601; (CT)&#xC5D0;&#xC11C; intra-procedural &#xC2EC;&#xC7A5; &#xC774;&#xBBF8;&#xC9D5;-&#xD328;&#xC158; &#xC784;&#xC0C1; &#xC624;&#xBC84; &#xD5E4;&#xB4DC;&#xC640; &#xAC1C;&#xC785; &#xB2F9;&#xC2DC; &#xD574;&#xBD80;&#xD559;&#xC758; &#xD2B9;&#xC131;&#xC744; &#xB2A5;&#xB825;&#xC758; &#xAC10;&#xC18C;&#xB85C; &#xC778;&#xD574; &#xD2B9;&#xD788; &#xB3CB;&#xBCF4;&#xC774;&#xB294; &#xB418;&#xC5C8;&#xC2B5;&#xB2C8;&#xB2E4; &#xAC83; &#xCC98;&#xB7FC;. &#xC6B0;&#xB9AC; &#xC774;&#xC804;&#xC5D0; &#xC9E7;&#xC740; &#xB300;&#xBE44; bolus &#xC8FC;&#xC0AC; &#xBC0F; &#xC57D; 4-10 s&#xC758; &#xC2A4;&#xCE94; &#xC2DC;&#xAC04; &#xC774;&#xBBF8;&#xC9D5; &#xC67C;&#xCABD;&#xB41C; &#xC544; &#xD2B8;&#xB9AC; &#xC6C0;&#xACFC; &#xD3D0; &#xC815; &#xB9E5; (LAPVs)&#xC5D0; &#xB300; &#xD55C; &#xC784;&#xC0C1;&#xC801;&#xC73C;&#xB85C; &#xC2E4;&#xD604; &#xAC00;&#xB2A5;&#xD55C; &#xC811;&#xADFC;&#xBC95;&#xC744; &#xC18C;&#xAC1C; &#xD588;&#xB2E4;. &#xACB0;&#xACFC; &#xB370;&#xC774;&#xD130;&#xB294; &#xC804;&#xAE30; &#xD574;&#xBD80;&#xD559; &#xB9E4;&#xD551; (EAM) &#xBC0F; &#xC2EC; &#xBC29; &#xC138; &#xB3D9; (AF) &#xC81C;&#xAC70; &#xC808;&#xCC28;&#xC5D0;&#xC11C; &#xC911;&#xC7AC; &#xC9C0;&#xCE68; &#xC911; intra-procedural &#xC0AC;&#xC6A9;&#xC5D0; &#xB300; &#xD55C; &#xCDA9;&#xBD84; &#xD55C; &#xC774;&#xBBF8;&#xC9C0; &#xD488;&#xC9C8;&#xC744;&#xAC00;&#xC9C0;&#xACE0;. &#xC774; &#xC885;&#xC774;&#xC5D0;&#xC11C;&#xB294; AF &#xC808;&#xC81C; &#xAC1C;&#xC785;&#xC5D0; &#xB300; &#xD55C; &#xC9C0;&#xCE68;&#xC5D0; &#xB300; &#xD55C; Lapvs&#xC758; &#xC644;&#xC804; &#xC790;&#xB3D9;&#xD654; &#xB41C; &#xC138;&#xBD84;&#xD654; &#xD1B5;&#xD569; intra-procedural &#xD45C;&#xBA74; &#xC138;&#xB300; &#xC18C;&#xC124; &#xAE30;&#xBC95; &#xC18C;&#xAC1C;. &#xB9AC;&#xC5BC; &#xD0C0;&#xC784; &#xADFC;&#xCC98;&#xC5D0; LAPV &#xD574;&#xBD80;&#xD559;&#xC758; &#xCCB4;&#xC801; &#xC2EC;&#xBB38;&#xC5D0; &#xB300; &#xD55C; &#xD544;&#xD130;&#xB9C1;-&#xBC31;-&#xD22C;&#xC601; &#xAE30;&#xBC18; &#xC7AC;&#xAC74;&#xACFC; &#xD568;&#xAED8;&#xC5D0;&#xC11C; &#xD68C;&#xC804; X-ray angiography (3 &#xCC28;&#xC6D0; RA) &#xC778;&#xC218; &#xB300;&#xBE44; &#xAC15;&#xD654; &#xB41C; &#xC218; &#xC788;&#xC2B5;&#xB2C8;&#xB2E4;. &#xC790;&#xB3D9; &#xBAA8;&#xB378; &#xAE30;&#xBC18; &#xC138;&#xADF8;&#xBA3C;&#xD2B8;&#xD654; &#xC54C;&#xACE0;&#xB9AC;&#xC998;&#xC744; &#xC774;&#xBBF8;&#xC9C0; &#xD488;&#xC9C8;;&#xC73C;&#xB85C; &#xC778; &#xD55C;&#xB3C4; &#xC804;&#xC5D0; &#xBD88;&#xAD6C; &#xD558; &#xACE0; &#xC2E0;&#xC18D; &#xD558; &#xACE0; &#xC815;&#xD655;&#xD55C; LAPV &#xBA54;&#xC26C; &#xC0DD;&#xC131; &#xD5C8;&#xC6A9; pre-procedural &#xC2EC;&#xC7A5; CT/MR, &#xC0C1;&#xB300; 3 &#xCC28;&#xC6D0; RA &#xC774;&#xBBF8;&#xC9C0; &#xC720;&#xBB3C; &#xC810;&#xC810; &#xAC10;&#xC18C; &#xC7A1;&#xC74C; &#xC2E0;&#xD638;&#xC5D0;&#xC11C; &#xACE0;&#xD1B5;. &#xC6B0;&#xB9AC;&#xAC00; &#xC6B0;&#xB9AC;&#xC758; &#xD1B5;&#xD569;&#xB41C; &#xBC29;&#xBC95; 1) &#xC790;&#xB3D9; &#xBC0F; &#xC218;&#xB3D9; intra-procedural 3 &#xCC28;&#xC6D0; RA &#xB370;&#xC774;&#xD130;&#xC758; &#xC138;&#xADF8;&#xBA3C;&#xD2B8;&#xB97C; &#xBE44;&#xAD50; &#xD558; &#xC5EC; &#xC720;&#xD6A8;&#xC131;&#xC744; &#xAC80;&#xC0AC;, intra-procedural 3 &#xCC28;&#xC6D0; RA&#xC640; &#xB370;&#xC774;&#xD130;, CT/MR pre-procedural 3) intra-procedural EAM&#xC758; 2) &#xC790;&#xB3D9; &#xC138;&#xADF8;&#xBA3C;&#xD2B8; 3 &#xCC28;&#xC6D0; RA&#xC758; &#xC790;&#xB3D9; &#xC138;&#xADF8;&#xBA3C;&#xD2B8;&#xC640; &#xD074;&#xB77C;&#xC6B0;&#xB4DC; &#xB370;&#xC774;&#xD130; &#xBC0F; CT/MR &#xB370;&#xC774;&#xD130; &#xD569;&#xB2C8;&#xB2E4;. &#xC5D0; &#xB300; &#xD55C; &#xC720;&#xD6A8;&#xC131; &#xAC80;&#xC0AC; &#xACB0;&#xACFC; &#xC790;&#xB3D9;&#xC73C;&#xB85C; intra-procedural 3 &#xCC28;&#xC6D0; RA &#xB370;&#xC774;&#xD130; &#xD45C;&#xC2DC; 1) &#xC57D; 1.3 m m 2) &#xC57D; 2.3 m m&#xC5D0; &#xBE44;&#xD574; &#xC790;&#xB3D9; &#xBD84;&#xD560; pre-procedural CT/MR &#xB370;&#xC774;&#xD130;&#xC640; 3) &#xC57D; 2.1 m m&#xC758; &#xC218;&#xB3D9; 3 &#xCC28;&#xC6D0; RA &#xC138;&#xADF8;&#xBA3C;&#xD2B8;&#xC5D0; &#xBE44;&#xD574; &#xD3C9;&#xADE0; &#xC138;&#xADF8;&#xBA3C;&#xD2B8;&#xD654; &#xC624;&#xB958; &#xB4F1;&#xB85D; &#xB41C; intra-procedural EAM &#xC810; &#xAD6C;&#xB984;&#xC5D0; &#xBE44;&#xD574; &#xC138;&#xADF8;&#xBA3C;&#xD2B8;. &#xC804;&#xBC18;&#xC801;&#xC778; &#xC2E4;&#xD5D8;&#xC744; LAPV &#xD45C;&#xBA74; &#xBD84;&#xB2E8; &#xB420; &#xC218; &#xC788;&#xB2E4; &#xC790;&#xB3D9;&#xC73C;&#xB85C; intra-procedurally pre-procedural CT/MR&#xC5D0;&#xC11C; &#xD30C;&#xC0DD; &#xB41C; &#xBA54;&#xC26C;&#xB97C; &#xAE30;&#xC900;&#xC73C;&#xB85C; &#xC720;&#xC0AC;&#xD55C; &#xD488;&#xC9C8;&#xB85C; 3 &#xCC28;&#xC6D0; RA &#xB370;&#xC774;&#xD130;&#xC5D0;&#xC11C; &#xB098;&#xD0C0;&#xB0C5;&#xB2C8;&#xB2E4;.

&#xC2EC; &#xBC29; &#xC138; &#xB3D9; &#xC81C;&#xAC70; &#xC808;&#xCC28;&#xC5D0; &#xD574;&#xBD80;&#xD559; intra-procedural &#xD45C;&#xBA74; &#xC0DD;&#xC131;&#xC744; &#xC704;&#xD55C; &#xD68C;&#xC804; x-&#xC120; &#xC774;&#xBBF8;&#xC9C0;&#xC758; &#xC790;&#xB3D9; &#xBD84;&#xD560;.

Desde la introducci&#xF3;n de 3-D de rotaci&#xF3;n im&#xE1;genes de rayos X, los protocolos para 3-D de rotaci&#xF3;n las arterias coronarias se han convertido en ampliamente disponible en la pr&#xE1;ctica cl&#xED;nica de rutina. Intra-procedimiento de im&#xE1;genes card&#xED;acas en una tomograf&#xED;a computarizada (TC)-al igual que la moda ha sido particularmente atractiva debido a la reducci&#xF3;n de los gastos generales y capacidad cl&#xED;nica para caracterizar la anatom&#xED;a en el momento de la intervenci&#xF3;n. Anteriormente hemos introducido un enfoque cl&#xED;nicamente factible para obtener im&#xE1;genes de la aur&#xED;cula izquierda y las venas pulmonares (LAPVs) con cortas inyecciones de contraste en bolo y tiempos de exploraci&#xF3;n de aproximadamente 4 -10 s. Los datos resultantes tienen una calidad de imagen suficiente para que dentro de su uso durante el procedimiento de electro-anat&#xF3;mica de mapeo (EAM) y una gu&#xED;a de intervenci&#xF3;n en la fibrilaci&#xF3;n auricular (FA) los procedimientos de ablaci&#xF3;n. En este art&#xED;culo se presenta una novedosa t&#xE9;cnica para la generaci&#xF3;n de superficies dentro de los procedimientos que se integra totalmente automatizado segmentaci&#xF3;n de los LAPVs de orientaci&#xF3;n en las intervenciones de ablaci&#xF3;n de FA. Realzada con contraste de giro angiograf&#xED;a de rayos X (3-D RA) las adquisiciones, en combinaci&#xF3;n con filtrados de retroproyecci&#xF3;n basado en la reconstrucci&#xF3;n volum&#xE9;trica permite el interrogatorio de la anatom&#xED;a LAPV casi en tiempo real. Un sistema autom&#xE1;tico basado en el modelo algoritmo de segmentaci&#xF3;n permite la generaci&#xF3;n de mallas r&#xE1;pido y preciso LAPV a pesar de los desaf&#xED;os planteados por la calidad de la imagen, en relaci&#xF3;n con pre-procesal cardiaca CT / MR, im&#xE1;genes 3-D con AR sufren de m&#xE1;s artefactos y la reducci&#xF3;n de se&#xF1;al a ruido. Nos validar nuestro m&#xE9;todo integrado mediante la comparaci&#xF3;n de 1) segmentaciones autom&#xE1;ticos y manuales de procedimiento dentro de la 3-D los datos de la AR, 2) segmentaciones autom&#xE1;ticos de procedimiento dentro de la 3-D de la AR y de pre-procesales datos CT / MR, y 3) dentro del procedimiento- EAM de datos de nube de puntos con segmentaciones autom&#xE1;ticos de 3-D de la AR y de los datos CT / MR. Nuestros resultados de la validaci&#xF3;n autom&#xE1;tica de segmentaci&#xF3;n intra-procedimiento en 3-D de datos con AR muestran errores medios de segmentaci&#xF3;n de 1) de aproximadamente 1,3 mm en comparaci&#xF3;n con el manual de 3-D segmentaciones RA 2) de aproximadamente 2,3 mm en comparaci&#xF3;n con la segmentaci&#xF3;n autom&#xE1;tica del procedimiento de pre-CT / MR de datos y 3) de aproximadamente 2,1 mm en comparaci&#xF3;n con los registrados dentro de nubes de puntos de procedimiento EAM. Los experimentos globales indican que las superficies pueden ser autom&#xE1;ticamente LAPV segmentada dentro de la de procedimiento del 3-D de datos con AR con una calidad comparable con respecto a las mallas derivados de la pre-procesal CT / MR.

Segmentaci&#xF3;n autom&#xE1;tica de rotaci&#xF3;n im&#xE1;genes de rayos X para la generaci&#xF3;n de la superficie anat&#xF3;mica intra-procesal en los procedimientos de fibrilaci&#xF3;n auricular de ablaci&#xF3;n

C-arm cone-beam computed tomography (CBCT) can be used to visualize tumor-feeding vessels and parenchymal staining during transcatheter arterial chemoembolization (TACE). To capture these two phases, all current commercially available CBCT systems necessitate two separate contrast-enhanced scans. In this feasibility study, we report initial results of novel software that enhanced our current CBCT system to capture these two phases using only one contrast injection. Novelty of this work is the addition of software that enabled the acquisition of two sequential, back-to-back CBCT scans (dual-phase CBCT, DPCBCT) so both tumor feeding vessels and parenchyma are captured using only one contrast injection. To illustrate our initial experience, DPCBCT was used for TACE treatments involving lipiodol, drug-eluting beads, and Yttrium-90 radioembolizing microspheres. For each case, the DPCBCT images were compared to pre-intervention contrast-enhanced MR/CT. DPCBCT is feasible for TACE treatments and the preliminary results show positive correlation with pre-intervention conventional CT and MR. In addition, the degree of embolization can be monitored. DPCBCT is a promising technology that provides comprehensive visualization of tumor-feeding vessels and parenchymal staining using a single injection of contrast. DPCBCT could potentially be used during TACE to verify catheter position and monitor the embolization effect.

Evaluating tumors in transcatheter arterial chemoembolization (TACE) using dual-phase cone-beam CT.

To evaluate the sensitivity of dual-phase cone-beam computed tomography during hepatic arteriography (CBCTHA) for the detection of hepatocellular carcinoma (HCC) by comparing it with the diagnostic imaging "gold standard": contrast-enhanced magnetic resonance imaging (CE-MRI) of the liver.

Comparing the detectability of hepatocellular carcinoma by C-arm dual-phase cone-beam computed tomography during hepatic arteriography with conventional contrast-enhanced magnetic resonance imaging.

Imaging modalities utilized in the interventional cardiology suite have seen an impressive evolution and expansion recently, particularly with regard to the recent interest in three-dimensional (3D) imaging. Despite this, the backbone of visualization in the catheterization laboratory remains two-dimensional (2D) X-ray fluoroscopy and cine-angiography. New imaging techniques under development, referred to as three-dimensional rotational angiography (RA) and C-arm CT, hold great promise for improving current device implantation and understanding of cardiovascular anatomy. This paper reviews the evolution of rotational angiography and advanced 3D X-ray imaging applications to interventional cardiology.

Potential role of three-dimensional rotational angiography and C-arm CT for valvular repair and implantation.

The importance of assessing trabecular architecture together with bone mineral density to determine bone stiffness and fracture risk in osteoporosis has been well established. However, no imaging modalities are available to assess trabecular architecture at clinically relevant sites in the axial skeleton. Recently developed flat-panel CT devices, however, offer resolutions that are potentially good enough to resolve bone architecture at these sites. The goal of the present study was to investigate how accurate trabecular architecture and stiffness can be determined based on images from such a device (XperCT, Philips Healthcare). Ten cadaver human C3 vertebrae, twelve T12 vertebrae and 12 proximal femora were scanned with XperCT while mimicking in-vivo scanning conditions and compared to scans of the same bones with microCT. Standard segmentation and morphology quantification algorithms were applied as well as finite element (FE) simulation based on segmented and gray value images. Results showed that mean trabecular separation (Tb.Sp) and number (Tb.N) can be accurately determined at all sites. The accuracy of other parameters, however, depended on the site. For T12 no other structural parameters could be accurately quantified and no FE-results could be obtained from segmented images. When using gray-level images, however, accurate determination of cancellous bone stiffness was possible. For the C3 vertebrae and proximal femora, mean bone volume fraction (BV/TV), Tb.Sp, Tb.N, and anisotropy (C3 only) could be determined accurately. For Tb.Th, structure model index (SMI, femur only), and anisotropy good correlations were obtained but the values were not determined accurately. FE simulations based on segmented images were accurate for the C3 vertebrae, but severely underestimated bone stiffness for the femur. Here also, this was improved by using the gray value models. In conclusion, XperCT does provide a resolution that is good enough to determine trabecular architecture, but the signal to noise ratio is key to the accuracy of the morphology measurement. When the trabeculae are thick e.g. in the femur or the noise is low, e.g. cervical spine, architecture and stiffness could be determined accurately, but when the trabeculae are thin and the noise is high, e.g. thoracic spine, architecture could not be determined accurately and the connectivity was lost and hence no mechanical properties could be calculated directly.

Determination of vertebral and femoral trabecular morphology and stiffness using a flat-panel C-arm-based CT approach.

To describe a novel technique for multimodality positron emission tomography (PET) fusion-guided interventions that combines cone-beam computed tomography (CT) with PET/CT before the procedure.

Cone-beam computed tomography fusion and navigation for real-time positron emission tomography-guided biopsies and ablations: a feasibility study.

To investigate whether C-arm dual-phase cone-beam computed tomography (CT) performed during transcatheter arterial chemoembolization (TACE) with doxorubicin-eluting beads can help predict tumor response at 1-month follow-up in patients with hepatocellular carcinoma (HCC).

Intraprocedural C-arm dual-phase cone-beam CT: can it be used to predict short-term response to TACE with drug-eluting beads in patients with hepatocellular carcinoma?

C-Arm CT (CACT) is a new imaging modality in liver oncology therapy that allows for the acquisition of 3D images intra-procedurally. CACT has been used to enhance intra-arterial therapies for the liver by improving lesion detection, avoiding non-target embolization, and allowing for more selective delivery of agents. However, one of the limitations of this technology is image artifacts created by respiratory motion. Purpose: To determine in this preliminary study improvements in image acquisition, motion compensation, and high resolution 3D reconstruction that can improve CACT image quality (IQ).

Article	Year
Accuracy evaluation of direct navigation with an isocentric 3D rotational X-ray system. Medical image analysis\| PubMed ID: 16099196	2006
Direct navigation on 3D rotational x-ray data acquired with a mobile propeller C-arm: accuracy and application in functional endoscopic sinus surgery. Physics in medicine and biology\| PubMed ID: 16333154	2005
Automatic segmentation of rotational x-ray images for anatomic intra-procedural surface generation in atrial fibrillation ablation procedures. IEEE transactions on medical imaging\| PubMed ID: 20129843	2010
Evaluating tumors in transcatheter arterial chemoembolization (TACE) using dual-phase cone-beam CT. Minimally invasive therapy & allied technologies : MITAT : official journal of the Society for Minimally Invasive Therapy\| PubMed ID: 21082901	2011
Comparing the detectability of hepatocellular carcinoma by C-arm dual-phase cone-beam computed tomography during hepatic arteriography with conventional contrast-enhanced magnetic resonance ... Cardiovascular and interventional radiology\| PubMed ID: 21328023	2012
Potential role of three-dimensional rotational angiography and C-arm CT for valvular repair and implantation. The international journal of cardiovascular imaging\| PubMed ID: 21394614	2011
Determination of vertebral and femoral trabecular morphology and stiffness using a flat-panel C-arm-based CT approach. Bone\| PubMed ID: 22057082	2012
Cone-beam computed tomography fusion and navigation for real-time positron emission tomography-guided biopsies and ablations: a feasibility study. Journal of vascular and interventional radiology : JVIR\| PubMed ID: 22494658	2012
Intraprocedural C-arm dual-phase cone-beam CT: can it be used to predict short-term response to TACE with drug-eluting beads in patients with hepatocellular carcinoma? Radiology\| PubMed ID: 23143027	2013
Image quality improvements in C-Arm CT (CACT) for liver oncology applications: preliminary study in rabbits. Minimally invasive therapy & allied technologies : MITAT : official journal of the Society for Minimally Invasive Therapy\| PubMed ID: 23837536	2013

Niels Noordhoek

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