Comput Methods Programs Biomed - Clustering of distinct PACS archives using a cooperative peer-to-peer network.

Tópicos

{ data(1714) softwar(1251) tool(1186) }
{ model(2220) cell(1177) simul(1124) }
{ imag(2830) propos(1344) filter(1198) }
{ intervent(3218) particip(2042) group(1664) }
{ implement(1333) system(1263) develop(1122) }
{ data(3963) clinic(1234) research(1004) }
{ system(1050) medic(1026) inform(1018) }
{ activ(1452) weight(1219) physic(1104) }
{ featur(3375) classif(2383) classifi(1994) }
{ take(945) account(800) differ(722) }
{ motion(1329) object(1292) video(1091) }
{ assess(1506) score(1403) qualiti(1306) }
{ method(1557) propos(1049) approach(1037) }
{ design(1359) user(1324) use(1319) }
{ featur(1941) imag(1645) propos(1176) }
{ visual(1396) interact(850) tool(830) }
{ perform(1367) use(1326) method(1137) }
{ health(3367) inform(1360) care(1135) }
{ medic(1828) order(1363) alert(1069) }
{ group(2977) signific(1463) compar(1072) }
{ data(3008) multipl(1320) sourc(1022) }
{ can(981) present(881) function(850) }
{ high(1669) rate(1365) level(1280) }
{ method(1969) cluster(1462) data(1082) }
{ data(1737) use(1416) pattern(1282) }
{ concept(1167) ontolog(924) domain(897) }
{ control(1307) perform(991) simul(935) }
{ care(1570) inform(1187) nurs(1089) }
{ method(984) reconstruct(947) comput(926) }
{ import(1318) role(1303) understand(862) }
{ compound(1573) activ(1297) structur(1058) }
{ ehr(2073) health(1662) electron(1139) }
{ first(2504) two(1366) second(1323) }
{ health(1844) social(1437) communiti(874) }
{ process(1125) use(805) approach(778) }
{ detect(2391) sensit(1101) algorithm(908) }
{ model(3404) distribut(989) bayesian(671) }
{ can(774) often(719) complex(702) }
{ imag(1947) propos(1133) code(1026) }
{ inform(2794) health(2639) internet(1427) }
{ system(1976) rule(880) can(841) }
{ measur(2081) correl(1212) valu(896) }
{ imag(1057) registr(996) error(939) }
{ bind(1733) structur(1185) ligand(1036) }
{ sequenc(1873) structur(1644) protein(1328) }
{ method(1219) similar(1157) match(930) }
{ network(2748) neural(1063) input(814) }
{ imag(2675) segment(2577) method(1081) }
{ patient(2315) diseas(1263) diabet(1191) }
{ studi(2440) review(1878) systemat(933) }
{ treatment(1704) effect(941) patient(846) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ framework(1458) process(801) describ(734) }
{ problem(2511) optim(1539) algorithm(950) }
{ error(1145) method(1030) estim(1020) }
{ chang(1828) time(1643) increas(1301) }
{ learn(2355) train(1041) set(1003) }
{ clinic(1479) use(1117) guidelin(835) }
{ algorithm(1844) comput(1787) effici(935) }
{ extract(1171) text(1153) clinic(932) }
{ general(901) number(790) one(736) }
{ search(2224) databas(1162) retriev(909) }
{ case(1353) use(1143) diagnosi(1136) }
{ howev(809) still(633) remain(590) }
{ studi(1410) differ(1259) use(1210) }
{ risk(3053) factor(974) diseas(938) }
{ perform(999) metric(946) measur(919) }
{ research(1085) discuss(1038) issu(1018) }
{ model(2341) predict(2261) use(1141) }
{ studi(1119) effect(1106) posit(819) }
{ blood(1257) pressur(1144) flow(957) }
{ spatial(1525) area(1432) region(1030) }
{ record(1888) medic(1808) patient(1693) }
{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ analysi(2126) use(1163) compon(1037) }
{ structur(1116) can(940) graph(676) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ method(2212) result(1239) propos(1039) }

Resumo

To face the demanding requirements of the clinical environment, PACS archives need to be resilient and reliable, supporting high availability and fault tolerance. Often, to ensure no data loss, PACS archives retain two copies of images on separate physical machines, using distributed data storage facilities. However, PACS do not take advantage of the various replicas to improve the transfer rates of medical images. This happens mostly because the DICOM standard does not comply with distributed fetching of image fragments while performing a store. Inspired by this unexplored opportunity, we designed and implemented a new solution that takes advantage of the distributed image replicas and, at the same time, respects the DICOM standard. Our strategy brought significant improvements in the exchange rates, load balancing and availability of installed PACS archives. Moreover, the adopted strategy forms a cluster of PACS archives that transparently enables horizontal scaling, facilitates the creation of backups, and gives to healthcare professionals a unified view of the distributed repositories.

Resumo Limpo

face demand requir clinic environ pac archiv need resili reliabl support high avail fault toler often ensur data loss pac archiv retain two copi imag separ physic machin use distribut data storag facil howev pac take advantag various replica improv transfer rate medic imag happen most dicom standard compli distribut fetch imag fragment perform store inspir unexplor opportun design implement new solut take advantag distribut imag replica time respect dicom standard strategi brought signific improv exchang rate load balanc avail instal pac archiv moreov adopt strategi form cluster pac archiv transpar enabl horizont scale facilit creation backup give healthcar profession unifi view distribut repositori

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